Cloud Blog

What’s new with Google Cloud

Fri, 28 Aug 2026 16:00:00 +0000

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more.

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

aside_block: <ListValue: []>

Aug 24 - Aug 28

Grok 4.6 is now available in Preview on Gemini Enterprise Agent Platform. xAI's most capable model, built for coding, agentic tasks, and knowledge work, Grok 4.6 joins Grok 4.3 and Grok 4.20 in Model Garden and becomes the flagship of the Grok family. It supports reasoning, function calling, and structured output for multi-step agentic workflows, and accepts text and image input.

Get started today
Empowering autonomous agents with advanced security governance
AI agents offer incredible productivity gains, but granting them access to read emails, query databases, and trigger APIs introduces critical new security risks. In fact, 79% of tech leaders cite security and governance as their biggest challenge to scaling AI. Traditional tools are no longer enough to handle automated threats like prompt injection and dynamic permissions. Discover how forward-thinking enterprises are using secure-by-default design, agent identity governance, and human-in-the-loop controls to deploy agents with confidence.

Read more
Stateful processing is available in BigQuery continuous queries in Preview
Stateful operations significantly expand what’s possible with BigQuery continuous queries. This feature allows users to leverage functions like JOINs, aggregations, and windowing functions directly in their streaming queries. Now you can calculate metrics over time (for example, a 30-minute average) to power your downstream applications and AI agents with much richer, real-time signals.
Try out our feature here and share your feedback with bq-continuous-queries-feedback@google.com!
Synthetic data generator tool is available for Managed Service for Kafka
You’ve launched your first Kafka cluster. Now what? The next thing to do is to produce some data to the cluster, but that involves modifying a client application somewhere or spinning up a virtual machine. The synthetic data generator tool, now generally available, can start sending mock data to your cluster in 3 clicks, and will get data streaming into your cluster in less than two minutes. The perfect utility for those moments you just want to test your cluster and new features. Try our quickstart today!
Dataflow pipeline updates are faster & more flexible
Dataflow pipeline updates can now stop-and-replace pipelines, a major addition to the existing in-place-update feature. The new parallel pipeline option accelerates the migration between the old & new pipeline, resulting in reduced disruption to your business. You can also set a timeout on drains that prevents runaway costs for your pipeliness in the event of stuck processing. This feature is generally available. Try it here!

Aug 17 - Aug 21

Webinar: Agent Identity as the backbone for secure AI innovation
An AI agent with a stolen API key looks identical to a legitimate one. As autonomous agents scale across enterprise systems, static credentials and legacy IAM policies can no longer keep up with machine-speed execution. Join Shaun Liu, Product Manager at Google Cloud, on August 27 at 1 PM ET to explore Google Cloud’s vision for unifying agent, human, and nonhuman identity into a workload-centric platform using verifiable cryptographic identities (SPIFFE, ID-JAG, OAuth).

Register for the webinar now

Aug 10 - Aug 14

Diagnosing Apigee Hybrid Cassandra Read Latency for Peak Performance
Diagnose real-time Cassandra read latency and resolve API key verification bottlenecks in Apigee Hybrid with this step-by-step troubleshooting guide. Learn how to deploy a debugging client and query performance tables to maintain sub-millisecond response times.

Read the Apigee Hybrid Cassandra Troubleshooting Guide
Keep moving with agents! The All Things Agentic Hackathon is officially live.
We're challenging builders to build next-generation agents that take on the busy work and handle the heavy lifting in the background using Gemini 3.5 and Google Cloud. Compete for your share of $190,000 in prizes, cash, and Google Cloud credits! Submissions are open from August 3, 2026, to August 31, 2026.

Learn more and register. Sign up for GEAR to get exclusive updates and your badge. #AllThingsAgenticHackathon
Accelerate PostgreSQL migrations using Gemini in Database Migration Service
Enterprise database migrations often stall during the "last mile" of translating legacy stored procedures, triggers, and custom functions from Oracle or SQL Server. Database Migration Service (DMS) now provides AI-assisted code conversion powered by Gemini in Databases. By combining deterministic compiler rules for 1:1 syntax with Gemini contextual synthesis for complex procedural blocks, DMS converts legacy code into native PostgreSQL and AlloyDB with full schema awareness and side-by-side validation.

Read the full blog post to learn how to streamline your database code conversion.
Compute Flex CUDs now available for G2 and G4 GPU VMs
Compute Flexible Committed Use Discounts (Flex CUDs) are now available for G2 (NVIDIA L4) and G4 (NVIDIA RTX Pro 6000) VMs. You can now lock in predictable savings while retaining the flexibility to adapt across VM families, migrate between regions, and combine general-purpose compute, GKE, Cloud Run, and G2 & G4 GPU VMs under a single spend commitment. Flex CUDs for G-series VMs let you lock in savings today while preserving the agility to upgrade to latest hardware without disruption!

Explore VM instance pricing or learn more about Flex CUDs.
Rapid Bucket accelerates the training and checkpoint performance in PyTorch Ecosystem via GCSFS
With the release of GCSFS 2026.8.0, organisations can now unlock maximum ROI from their AI/ML infrastructure by eliminating data starvation on GPUs in PyTorch ecosystem when they are using Frameworks like Dask, Pandas, PyTorch , PyTorch Lightning, Hugging Face Datasets, Ray dataetc. By making adaptive concurrent prefetching the default, GCSFS dynamically predicts and background-fetches sequential read patterns—boosting single-file throughput by 5x, and scaling up to 21 GiB/s , saturating the NIC when paired with Rapid Bucket. Saturating the NIC translates to significantly improved accelerator goodput and reduced training wait times with zero integration friction. Training and checkpoint restore workflows benefit from intelligent memory management that automatically drains the buffer during random reads to completely avoid bandwidth or memory penalties.

Aug 3 - Aug 7

Navigate data sovereignty and AI innovation with hybrid cloud
For enterprises facing strict compliance rules, keeping sensitive data on-premises often means missing out on cutting-edge AI. Data from the 2026 State of AI Infrastructure report reveals that 52% of IT leaders are adopting hybrid cloud strategies to bridge this gap. Our latest blog post explores how Google Distributed Cloud (GDC) helps organizations deploy connected or air-gapped models to run advanced AI entirely within secure environments—mitigating geopolitical risks without sacrificing innovation. Read more.
SAP and Google Cloud Launch BDC Connect for BigQuery
For years, enterprises have struggled with the cost, risk, and complexity of moving mission-critical SAP data into advanced analytics platforms. The general availability of SAP Business Data Cloud (BDC) Connect for BigQuery marks a turning point. By introducing revolutionary zero-copy, bi-directional data sharing, this new capability seamlessly bridges SAP systems with Google Cloud's powerful data and AI ecosystem. Instead of wrestling with manual data duplication and lost business context, organizations can now eliminate silos, dramatically lower their analytics costs, and rapidly deploy trustworthy, agentic AI solutions grounded in real-time operational reality. Read the full announcement to learn how to transform your data strategy.
Google Cloud Cortex Framework version 7 is now generally available!
This release helps you modernize your data architecture for AI agent readiness, enabling you to quickly deploy, customize, and extend robust data products while simplifying orchestration and reducing infrastructure overhead. It provides data product accelerators for SAP-sourced data to build trusted, high-quality data products ready for advanced analytics and agentic use cases. The Framework integrates with Google Cloud products including BigQuery, Dataform, Knowledge Catalog, and Gemini Enterprise Agent Platform. Learn more in our announcement blog, technical documentation, or try a demo deployment today.
From API Management to AI Gateway with Apigee
Massive LLM adoption unlocked automation but exposed critical vulnerabilities, from unpredictable token costs to security risks like prompt injection. Without central management, organizations face accelerated technical debt. Learn how to transform Apigee into an enterprise AI Gateway to centralize governance. This architectural roadmap details how to utilize semantic cache to optimize token costs, implement prompt protection policies for security, and productize tools using the emerging MCP standard.

Read the full architectural roadmap on the Apigee Community Hub
Centrally govern enterprise AI traffic with Apigee AI Gateway
Manage, track, and secure model communication across your entire infrastructure from a single pane of glass. In a new video walkthrough, Principal Architect Tyler Ayers demonstrates how Apigee AI Gateway simplifies agentic governance. Learn how to transparently proxy model traffic, log real-time token counts, and apply runtime security quotas without impacting your developer workflow.

Watch the Apigee AI Gateway demo
Maximize Provisioned Throughput Utilization
Sudden traffic micro-spikes can exceed per-second quotas, triggering 429 errors or forcing overflow into shared resource pools. A new architectural guide demonstrates how to build a serverless "shock absorber" using Cloud Run and Google Cloud Tasks. By decoupling request ingestion from execution, this queue-based pattern flattens volatile traffic bursts and smoothly drips requests to Gemini at your exact quota rate, maximizing Provisioned Throughput utilization while eliminating job failures during peak usage. Read the step-by-step setup guide.
Eliminate security blindspots in agentic tool interactions
Unmonitored agentic tool calls via the Model Context Protocol (MCP) can introduce critical security risks to your enterprise architecture. Join our technical deep dive on Thursday, August 13, to discover how to position Apigee as a centralized security gateway. Featuring the new ParsePayload policy and payload operations groups in API Products, this session demonstrates how to enforce granular tool filtering, manage execution quotas, and scale secure agent ecosystems without impeding developer velocity.

Register for the August 13 Community TechTalk

Jul 27 - Jul 31

Data Cloud and Apigee CDMX: The AI Agent Evolution | August 12, 2026
Enterprise AI demands evolution beyond basic conversational assistants. To generate real value, AI models must connect with the organization's core systems and live data sources. Join us this August 12 at Google CDMX for the exclusive event AI Evolution: Powering Tomorrow's Enterprise. Learn how to design an agile and secure ecosystem by unifying the power of Gemini, Apigee, and data agent technologies through practical demonstrations led by Google Cloud engineers.

Secure your spot for the in-person session in Mexico City Register now!
Vast Edge, built on GCP, launches the first live recovery interface for cloud backups, enabling IT teams to inspect backup contents in real time. This transforms backups from a blind, log-based process into an interactive platform where teams can instantly search, preview, and validate the exact data available for restore.

This platform protects Google Workspace, NetSuite, Salesforce, Workday and many SaaS environments, providing complete visibility and enterprise-grade oversight.

Visit Vast Edge Backup & Disaster Recovery and get a free trial of their backup solutions on the GCP Marketplace for Google Workspace Backup, NetSuite Backup, Salesforce Backup, and Workday Backup.

Jul 20 - Jul 24

Claude Opus 5, Anthropic’s latest model, is now available on Agent Platform. It brings performance improvements over Opus 4.8 across coding, long-running agents, and knowledge work.The model is Zero Data Retention (ZDR) compatible. For safety, high-risk workflows — such as penetration testing or exploit generation — it will notify you and fall back to Opus 4.8.We’re excited to continue to offer enterprise customers options across frontier models to build, deploy, and scale AI securely. Try it here.
Apigee Northam Roadshow 2026 | The AI Agent Evolution: Powering Tomorrow's Enterprise
AI is evolving. As your organization deploys autonomous agents, the integration between APIs and models becomes critical. Join Google Cloud specialists for an exclusive day of deep-dive sessions and live demos. Discover how the unified power of Apigee and the Google Cloud Agent Platform allows you to build, govern, and scale high-performance AI agents with complete control. Call to Action: Register for Sunnyvale | Register for NYC | Register for Chicago
Deploy an Apigee Proxy for MCP Registry Discovery
Learn how to deploy an Apigee X proxy to format Apigee API Hub data into the Model Context Protocol (MCP) Registry format. This tutorial by Tyler Ayers guides developers through cloning the sample repository, deploying using the Apigee Feature Templater (aft), and testing the endpoint to make API data easily discoverable by coding agents.

Read the full community tutorial to get started.
Simplify AI Infrastructure: Getting Started with Apigee AI Gateway
Managing a complex AI landscape with multiple backend environments can present significant operational and governance challenges. A new tutorial walks you through how to build a unified API proxy using Apigee AI Gateway. By establishing a single, secure entry point for all model traffic, teams gain access to real-time analytics, comprehensive tracing, and financial operations auditing—completely seamlessly, and with absolutely no modifications required to client environments or user configurations.

Read the step-by-step setup guide
Your AI agents are ready. Is your data?
The biggest bottleneck to scaling AI isn't the models—it's giving them access to business context. As enterprises move to proactive systems of action, legacy infrastructure often buckles under the nonlinear speed of AI agents. Google Cloud’s new Agentic Data Cloud, built on AI-native infrastructure, solves this by unifying data, AI models, and operational databases. Discover how a borderless Lakehouse and active Knowledge Catalog can empower your AI agents with trusted, real-time context without unnecessary engineering overhead. Read more.
Secure and govern your AI at Apigee AI Horizon in London
Moving AI from basic prompts to complex agentic workflows requires trust and control. Join us on Tuesday, 1st September 2026 at Google London for our 5th edition of Apigee AI Horizon. Discover how Google Cloud product leaders and architects are using Apigee and Model Armor to secure LLM APIs, implement policy controls, and manage token consumption. Do not miss this one—register soon!

Secure your spot for AI Horizon London

Jul 13 - Jul 17

Resource-Based CUD Sharing is Now Enabled by Default
Starting June 16, 2026, the default setting for Google Cloud Resource-based Committed Use Discount (CUD) sharing will change from disabled to enabled for new billing accounts and eligible existing accounts without active CUDs. This update automatically maximizes your savings by pooling underutilized discounts across your resources.

You retain full control and can adjust your CUD sharing preferences at any time by changing your CUD scope configuration. For instructions, see Enable CUD sharing or Disable CUD sharing.
Webinar for India: Google Cloud for EdTech: Optimizing Traffic and Token Governance at Scale
API traffic surges and AI model integration are reshaping the EdTech landscape. Join Satyam Maloo for the webinar Google Cloud for EdTech: Optimizing Traffic and Token Governance at Scale on July 23, 2026. Learn to implement advanced rate limiting, gain granular token visibility, and leverage real-time analytics to govern your platform effectively. Whether you’re scaling for peak academic seasons or integrating complex AI workflows, this session provides the infrastructure blueprint you need.

Register Now
Scaling AI Agents: Treat prompts like software artifacts
As AI agents move into production, monolithic system prompts often result in configuration drift, merge conflicts, and silent runtime failures. The solution is adopting a Prompts-as-Code architecture. By breaking prompts into modular skill files and using a build-time transpiler, engineering teams can introduce dependency resolution, static validation, and CI/CD rigor to their agent's control plane. Stop manually editing massive text files and start building deterministic, reliable agent infrastructure.

Read more here.

Jul 6 - Jul 10

Webinar: Introducing Google Cloud NGFW Enterprise advanced malware protection - powered by Palo Alto Networks
Discover the new Cloud NGFW advanced malware sandbox, arriving in preview later this year. Powered by Palo Alto Networks Advanced Wildfire, it leverages data from 70,000+ customers to help defeat advanced malware. Join us on July 16 at 11 AM EDT to learn how to build a resilient, zero-trust cloud infrastructure that protects your apps and data, wherever they reside.

Register for the webinar now
Safely run AI-generated code in Cloud Run sandboxes
Cloud Run sandboxes, now in public preview, are lightweight, isolated execution boundaries that you can spawn near-instantly within your existing Cloud Run service instances.

Whether you need to let an LLM run a dynamically generated Python script to calculate business margins or spin up a headless browser to perform web research, Cloud Run sandboxes give you a secure, isolated sandbox to run these tasks without leaving your serverless environment.

Read the blog to learn more and get started today.
Australia API Horizon: Scaling Enterprise Governed AI Agents
The transition from AI chatbots to autonomous agents is the most critical integration point for your business. Join Google Cloud at our upcoming events to explore exclusive deep-dive sessions on architecting for the agentic era.

Discover how to use Apigee as an intelligent AI Gateway to govern, secure, and scale high-performance architectures. You will learn to seamlessly build AI tools from your existing APIs and maintain control over your entire ecosystem.

Join us in your preferred city:
Build highly available, multi-region services on Cloud Run
Maintaining uptime for business-critical applications just got a lot easier on Cloud Run. Service health, now Generally Available, automates cross-region failover by leveraging readiness probes for instance-level health checks with a simple, two-click setup. You can configure service health with global external Application Load Balancers for public-facing applications or cross-region internal Application Load Balancers for private networking traffic.

Learn how to configure service health for Cloud Run.
Report: 83% of organizations need infrastructure upgrades for agentic AI
The shift from conversational bots to autonomous agents is breaking legacy systems. Our new State of AI Infrastructure report details how engineering leaders are adapting to these massive new workloads. To eliminate inference bottlenecks, control hidden scaling costs, and manage agent sprawl, the industry is rapidly moving toward fluid compute, centralized governance, and unified, co-designed architectures.

Explore our key infrastructure insights
Stop tinkering, start scaling: the industrialized AI Playbook
Did you know that only 5% of custom AI investments actually return measurable business value? The problem isn’t the technology—it’s how organizations are wired to run it.

In this compelling read, Google Cloud Consulting breaks down the operational blueprint that bridges the stark gap between "cool tech experiments" and real, P&L-impacting enterprise ROI.

Read the full article on Medium
AI Agent Clinic: Slashing App Latency by 80%
Prototyping an AI agent is easy, but scaling for live traffic presents unique challenges. In the latest AI Agent Clinic, our technical experts partner with a developer to optimize PlaybackIQ, a live football analysis agent. This session demonstrates how to use OpenTelemetry to trace bottlenecks in the Gemini Enterprise Agent Platform and deploy to Cloud Run for high-concurrency scaling, achieving an 80% reduction in response time. Learn production-grade debugging strategies to optimize your own LLM applications.

Watch the 60-minute teardown

Jun 29 - Jul 3

Claude Sonnet 5, Anthropic’s latest model, is now available on Agent Platform.
This addition serves as a drop-in replacement for Sonnet 4.6, giving organizations expanded choice for task completion across enterprise workflows. It features enhanced reasoning, cleaner code generation, and computer use capabilities for desktop and browser workflows.

By continuing to rapidly bring frontier models to our platform, Google Cloud offers an uncompromised choice of the industry's best technology to build, test, and scale enterprise-grade AI.

Get started today.
Automate your AI governance with Apigee and YAML
Manual API gateway configurations can quickly slow down your AI engineering velocity. Join the Apigee community on Thursday, July 16, to discover an automated, declarative blueprint for model garden management. Learn how a simple, repeatable YAML pattern lets your AI practitioners instantly spin up secure, policy-backed enterprise configurations without friction. Bring your questions and connect during our live Q&A session.

Register for the July 16 Community TechTalk
Build next-generation AI portals for autonomous agents
Standard developer portals were designed for human developers to subscribe to static APIs. Today, autonomous agents, LLM toolkits, and dynamic runtimes demand a central nervous system for governance. Join our technical deep dive on Thursday, July 23, to explore Apigee's new AI Portals solution. You will see exactly how to deploy full-service, MCP powered hubs to safely manage enterprise self-service for models, tools, and agents.

Register for the July 23 Community TechTalk
Protect your infrastructure from advanced cyberattacks at the API layer (Presented in Portuguese)
In an era of increasingly sophisticated threats, relying solely on traditional firewalls leaves critical data gaps. Join our technical community TechTalk on Thursday, July 30—conducted in Portuguese—to learn how to proactively mitigate risks directly at the gateway layer. This session demonstrates how to configure and govern essential Apigee security policies to build a robust line of defense, ensuring maximum availability and complete integrity for your enterprise microservices.

Register for the July 30 Portuguese Community TechTalk

Jun 22 - Jun 26

Accelerate TPU model loading while saving RAM on GKE.
Large model cold starts often stall scaling and leave high-value TPUs idle. The open-source Run:ai Model Streamer now natively supports TPUs with Google Cloud Storage in TPU vLLM 0.18.0. This integration accelerates inference pipelines on GKE by streaming tensors directly into CPU memory, bypassing local disk bottlenecks and the "double-buffering" trap. In benchmarks, loading a 480B parameter model was over 2x faster while cutting peak host memory usage by half. Read the full guide and get started today.
Stop Training Blind: Scaling AI with the New OpenTelemetry-Based TPU AI Telemetry Collector Agent
Google Cloud’s new AI Telemetry Collector agent standardizes TPU monitoring using OpenTelemetry. It optimizes enterprise ML workloads by identifying silent failures and providing zero-cost operational metrics without draining host CPU cycles. The agent seamlessly routes telemetry to Google Cloud Monitoring or Prometheus and custom Grafana setups. Pre-installed on Google-optimized Ubuntu images or available via Docker, it tracks memory, network latency, and core utilization to maximize multi-node training efficiency.

You can read more of this capability by clicking this link.

Jun 15 - Jun 19

Join us for a deep dive into agentic AI control with AppyThings
Your integrations aren’t failing—they are evolving. When users interact with AI agents, they no longer arrive directly at your site, resulting in experiences stripped of your context, expertise, and intended experience. Join us on Thursday, June 25, for a community tech talk in partnership with AppyThings to learn how to solve this new gateway challenge. We will explore how MTN laid an integration foundation with the Model Context Protocol (MCP) to deliver accurate, consistent experiences. Our technical experts will demonstrate how to leverage Apigee as a centralized tools management solution to govern agent access.

Register for the session
Optimize Spot VM Deployments with Capacity Advisor for Spot, Now in Public Preview
Google Compute Engine has launched Capacity Advisor for Spot to Public Preview, now open to all customers. This tool turns Spot capacity discovery into a data-driven process by providing real-time deployment recommendations to maximize obtainability and minimize preemption risks. Query the Capacity Advisor API for obtainability and minimum estimated uptimes, or use the new Console UI featuring a global availability map, spot price lookups, and historical preemption rate trends to visually find the most cost-efficient compute capacity.

Get started today to start optimizing your Spot VM deployments!
Build a multi-tenant agentic AI system
When scaling generative AI across different business units, your teams need specialized AI agents with unique operational rules and tools. Our new reference architecture helps you build a centralized multi-tenant platform to prevent fragmented silos, eliminate data exposure risks, and maintain unified compliance. Read the guide to design and deploy a multi-tenant agentic AI system in Google Cloud.
How to Configure Gemini Enterprise to Connect to a Custom MCP Server
The Gemini Enterprise MCP Connector was a big announcement at Google Cloud Next because it introduces the ability to connect Gemini Enterprise to MCP servers. This blog post provides a step-by-step guide on how to configure your first Custom MCP Server connector using the Google Maps Ground Lite MCP server as an example. Once you understand this flow, you can configure multiple MCP servers with Gemini Enterprise to bring all the context you need.

Jun 8 - Jun 12

Simplify Multi-Cloud Planning with Cloud Location Finder, now Generally Available
Cloud Location Finder provides up-to-date data on public regions, zones, and Google Distributed Cloud Connected locations across Google Cloud, AWS, Azure, and OCI. You can now programmatically discover locations based on provider, proximity, territory, and carbon footprint to optimize your global infrastructure strategy for performance, compliance, and sustainability.

Get started for free today

Jun 1 - Jun 5

Modeling the physical world with BigQuery Graph
Managing complex supply chains requires more than just spreadsheets; it requires a digital replica of the physical world. In this post, Guru Rangavittal and Candice Chen explore how BigQuery Graph enables organizations to build a digital twin by turning physical assets into an interconnected map of nodes and edges. By moving beyond traditional relational databases, businesses gain real-time clarity into operations—from executing surgical ingredient recalls to analyzing weather-driven logistics risks. Discover how BigQuery Graph transforms reactive firefighting into proactive, precision modeling, allowing you to see critical connections in seconds and future-proof your supply chain.
Apigee for AI: Govern LLMs and MCP Servers (Presented in Spanish)
Learn how to securely transition your AI initiatives from experimental prototypes to enterprise-ready deployments. Join Luis Cuellar on June 18 for a technical deep dive (presented in Spanish) exploring Apigee’s latest AI gateway capabilities. Discover how to centralize governance over Model Context Protocol (MCP) servers, protect Large Language Models (LLMs) with robust API gateway security policies, and manage token-based quotas.

Register for the June 18 Spanish Community TechTalk

May 25 - May 29

Anthropic’s Claude Opus 4.8 is now available on Gemini Enterprise Agent Platform. As we continue to expand our platform's model offerings, this addition gives organizations more options for handling complex, multi-stage enterprise workflows. Claude Opus 4.8 brings strong capabilities in agentic coding, allowing developers to manage extensive refactors and tracking dependencies over extended sessions.
API Horizon Munich July 6, 2026: Orchestrating the Next Era of AI and APIs
Master the orchestration of next-gen AI and digital ecosystems. Join Google Cloud experts and DACH tech leaders on July 6 for an exclusive look at the Apigee roadmap, Agent Management, and Model Context Protocol (MCP). Gain real-world insights and connect with the regional integration community.

Register now
Securing AI Agents: The Extended Agent Gateway Pattern
Learn how to prevent autonomous AI agents from invoking unauthorized APIs. Join Apigee Specialist Joel Gauci on June 4 for a technical deep dive into the Extended Agent Gateway pattern. This session covers enforcing Fine-Grained Authorization (FGA), implementing secure token exchange, and establishing Model Context Protocol (MCP) governance at the API gateway layer to protect enterprise backend services.

Register for the June 4 Community TechTalk
API-to-Agent Security: Exposing REST APIs to Gemini Enterprise via MCP
Connect Gemini Enterprise agents to core data without creating security hazards. Join Google Cloud Specialist Nigel Walters on June 11 to learn how to instantly transform legacy REST APIs into secure Model Context Protocol (MCP) servers. We’ll cover how to safely register tools with Gemini while enforcing gateway-level guardrails like rate limiting and access control policies.

Register for the June 11 Community TechTalk

May 18 - May 22

Chinese Webinar | June 4: AI Command and Control
As AI agents move from experimental pilots to core enterprise functions, governance has become a critical next step. Join Google Cloud on June 4th at 10:00 AM (Beijing Time) to learn how to build a secure AI management layer architecture. We'll explore how to develop governed MCP (Model Context Protocol) endpoints, manage tool access to enterprise data, and leverage robust audit logs to operationalize AI. This session also includes a practical demonstration of these governance frameworks on Google Cloud.

Register here
GCP Announces New Features to Benchmark and Optimize LLMs for On-Device Use Cases
Deploying fine-tuned LLMs from GCP to edge devices like smartphones is complex due to fragmented hardware. Google AI Edge Portal bridges this gap, giving GCP developers the ability to test AI performance on 120+ Android devices, representing the full diversity of high, medium, and low tier smartphones on the market today. This week at I/O, we announced brand new capabilities to benchmark and debug LLM performance across these devices. Sign-up to utilize these new features in private preview today.

May 11 - May 15

Build Your AI & MCP Control Tower for Universal Governance
Master the future of agentic security with Apigee. Join our Community TechTalk on May 21 to discover how Apigee serves as a central "Control Tower" for the Model Context Protocol (MCP). We will explore how new JSON-RPC tool authorization enables fine-grained access policies across your organization, ensuring secure and scalable AI deployments. Whether managing internal tools or external users, learn to govern your agentic ecosystem with absolute precision. This session is designed for global coverage across EMEA and AMER regions.

Register for the May 21 Community TechTalk

Apr 27 - May 1

Master Your Launch: The Apigee Production Go-Live Checklist
Ensure a secure launch with the Apigee production guide. Join Nicola Cardace on May 28 to explore security guardrails, including IAM roles, mTLS configurations, and encrypted KVM migrations. Scheduled at 11 AM EDT / 5 PM CEST to support EMEA and AMER teams, this TechTalk provides the technical roadmap you need to flip the switch with absolute confidence.

Register for the May 28 Community TechTalk
Transforming APIs into Governed Agentic Tools on the Google Cloud Agentic Platform
Turn your APIs into secure, governed agentic tools on the Google Cloud Agentic Platform. Join Specialist Christophe Lalevée on May 7 for a technical deep dive into AI productization. Scheduled at 5 PM CEST / 11 AM EDT to maximize coverage for developers across EMEA and AMER, this session explores the integration and governance frameworks required to scale enterprise-ready AI with confidence.

Register for the May 7 Community TechTalk
Fractional G4 VMs are Generaly Available, providing a highly efficient and cost-effective entry point for AI and graphics workloads. These new configurations, using NVIDIA virtual GPU (vGPU) technology, allow you to leverage the power of the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs in flexible, smaller increments, so you can right-size your infrastructure to match the specific demands of your applications. By providing more granular access to advanced hardware, fractional G4 VMs let you optimize resource allocation and reduce overhead without sacrificing performance. You can now select from additional GPU slice sizes for your specific needs:
- 1/2 GPU: Ideal for more intensive tasks such as LLM inference, robotics sensor simulation, and high-fidelity 3D rendering.
- 1/4 GPU: Optimized for mainstream workloads, including mid-range creative design, video transcoding, and real-time data visualization.
- 1/8 GPU: Great for lightweight applications such as remote desktops, productivity tools, and entry-level streaming services.
Transitioning AI from a sandbox prototype to an enterprise-grade system is a major hurdle. A monolithic script won't suffice for widespread deployment. To achieve true scale and reliability with Gemini, organizations must adopt service-oriented micro-agent architectures, establish Zero-Trust security, and implement rigorous EvalOps. Master the "Agentic Maturity Ladder" to ensure your AI & Agentic solutions are robust, secure, and ready for the real world.

Watch the deep dive and read the developer blog to learn more.
ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available
Data scientists and developers can now combine the local productivity of VS Code with the scalable infrastructure of Google Cloud. The new Google Cloud Workbench Notebooks extension allows you to connect to and run notebooks on managed cloud environments directly within your local IDE. This integration streamlines the ML lifecycle by eliminating context switching and providing high-performance compute for complex workloads in a familiar interface. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation.
- Install from Marketplace: GoogleCloudTools.workbench-notebooks
- Contribute on GitHub: colab-enterprise-vscode

Apr 20 - Apr 24

Announcing the 2026 Google Cloud Partners of the Year
Google Cloud is honored to celebrate the winners of the 2026 Partner of the Year awards! These awards recognize an exceptional group of partners across AI, Security, Infrastructure, and more, who have demonstrated a commitment to customer success. From global system integrators to specialized startups, these winners are leveraging the power of Google Cloud to solve complex challenges and drive digital transformation worldwide. Join us in congratulating these organizations for their innovation, collaboration, and impactful results over the past year.

See the 2026 Partner Award winners

Apr 13 - Apr 17

We're excited to announce the Public Preview of Datastream’s metadata integration with Knowledge Catalog. This is the first step in our vision to provide a centralized, "single pane of glass" for all Datastream assets. The enhancement automatically synchronizes Streams, Connection Profiles, and Private Connections, eliminating data silos. It enhances discoverability, allowing you to search for Datastream assets using the same interface as BigQuery tables. Centralized governance is also provided, making your real-time data estate more transparent and easier to manage.
Upgrading Apigee OPDK to 4.53 with OS Modernization
Modernize your infrastructure using Google’s official, sequential upgrade path. Our Technical expert, Rakesh Talanki outlines how to upgrade Apigee OPDK to v4.53 while migrating to a supported OS (RHEL 8.x/9.x). This guide covers the "build-out" methodology, including multi-data center syncing, to ensure a stable, zero-downtime transition

Read the guide
Cloud Run Worker Pools and CREMA: Powering Serverless AI at Scale
Google Cloud has announced the General Availability of Cloud Run worker pools, a new resource type designed specifically for pull-based, non-HTTP workloads. Unlike traditional Cloud Run services that scale based on request traffic, worker pools provide an "always-on" environment for background tasks like processing message queues or running large-scale AI inference. To support this, Google Cloud also open-sourced the Cloud Run External Metrics Autoscaler (CREMA). Built on KEDA, CREMA enables queue-aware autoscaling for worker pools, allowing them to dynamically scale based on external signals like Pub/Sub backlog or Kafka lag.
Apigee Model Context Protocol (MCP) now Generally Available
Expose enterprise APIs as MCP tools for agentic AI applications with the General Availability of MCP in Apigee. This update allows developers to transform APIs into AI-ready tools using OpenAPI Specifications, removing the need for local MCP servers or additional infrastructure. With managed endpoints and semantic search in API hub, you can now provide AI agents with secure, governed access to enterprise data at scale.

Explore the MCP overview

Apr 6 - Apr 10

Community TechTalk: Powering Retail Agents with ADK, UCP & Apigee X
Move beyond basic chatbots to secure, transactional AI experiences. Join our Community TechTalk on April 16 to learn how Apigee X and Gemini build a "Trust Layer" for AI shopping assistants using UCP standards. We’ll demonstrate how to block prompt injections with Model Armor and implement cost governance via token limits to secure the path from discovery to purchase.

Register for the TechTalk
Implement multimodal capabilities in your AI agents
Explore three new reference architectures for building sophisticated multi-agent AI systems that can process and analyze multimodal data. To analyze disparate multimodal data and produce a high-confidence classification, see Classify multimodal data. To create a fluid conversational AI that processes audio and video streams in real time, see Enable live bidirectional multimodal streaming. To consolidate fragmented multimodal data into a searchable knowledge graph, see Multimodal GraphRAG resource orchestration.
Automate SecOps workflows with an agentic AI system
To accelerate incident response and reduce manual toil for your security team, you need a system that can automate remediation playbooks. Our new reference architecture helps you build an AI agent that orchestrates complex triage and investigation workflows across disparate security tools, such as SIEM, CSPM, and EDR, from a single interface. See the full guide to orchestrate security operations workflows.

Mar 30 - Apr 3

ASEAN Webinar | April 30: Mastering Agentic Governance at Scale with GCP
As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud experts Shilpi Puri & Wely Lau for a webinar on April 30th at 11:00 AM SGT to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.

RSVP here.

Mar 23 - Mar 27

Turn your API sprawl into an agent-ready catalog
As organizations scale, APIs often become scattered across multiple gateways, creating "blind spots" that hinder AI adoption. To solve this, we’ve introduced two new capabilities for Apigee API hub: a new integration with API Gateway to automatically centralize API metadata into a single control plane, and a specification boost add-on (now in public preview). This add-on uses AI to enhance your API documentation with the precise examples and error codes that AI agents need to function reliably.

Read the full blog post to get started.
Webinar | April 16: AI Command & Control
As AI agents move from experimental pilots to core enterprise functions, governance is the critical next step. Join Google Cloud expert Satyam Maloo for a webinar on April 16th at 11:00 AM IST to learn how to architect a secure AI Management layer. We’ll explore developing governed MCP endpoints, managing tool access to enterprise data, and operationalizing AI with robust audit logs. The session includes a live demo of these frameworks in action on Google Cloud.

RSVP here.
Modernizing and Decoupling Event Ingestion with Apigee
In modern cloud-native architectures, decoupling producers from consumers is critical for building resilient systems. While Google Cloud Pub/Sub provides a scalable backbone, exposing it directly to external clients can introduce security and management overhead. This new guide explores how to leverage Apigee as an intelligent HTTP ingestion point. Learn how to handle security, mediation, and traffic control before messages reach your internal bus using the PublishMessage policy or Pub/Sub API.

Read the full guide.

Mar 16 - Mar 20

Gemini-powered Assistant in BigQuery Studio Gets Context-Aware Upgrades
The Gemini-powered assistant in BigQuery Studio has been transformed into a fully context-aware analytics partner, supporting your entire data lifecycle. The new capabilities include intelligent resource discovery, which uses Dataplex Universal Catalog search to find resources across projects and deep dive into metadata using natural language. You can now automate tasks, such as scheduling production-grade queries directly through the chat interface, and instantly troubleshoot long-running or failed jobs with root cause analysis and cost control auditing.

Explore the full range of what the assistant can do.

Mar 9 - Mar 13

Want to use Gemini to develop code and don't know where to start?
This article includes a couple of examples of developing code with Gemini prompts; it identified changes that were needed to be made to get the code working. The article also refers to other examples that are available on github.

Mar 2 - Mar 6

Introducing Gemini 3.1 Flash-Lite, our fastest and most cost-efficient Gemini 3 series model. Built for high-volume developer workloads at scale, 3.1 Flash-Lite delivers high quality for its price and model tier. Gemini 3.1 Flash-Lite can tackle tasks at scale, like high-volume translation and content moderation, where cost is a priority. And it can also handle more complex workloads where more in-depth reasoning is needed, like generating user interfaces and dashboards, creating simulations or following instructions.

Starting today, 3.1 Flash-Lite is rolling out in preview to enterprises via Vertex AI and developers via the Gemini API in Google AI Studio.
TechTalk: Implementing Device Authorization Grant (RFC 8628) for Apigee
Learn how to authorize "headless" devices like Smart TVs or AI agents that lack keyboards and browsers. Join our Community TechTalk on March 19 (5PM CET / 12PM EDT) to go under the hood of Apigee X/Hybrid. We’ll cover the real-world mechanics of state management, polling, and human-in-the-loop security patterns for devices and autonomous agents.

Register for the TechTalk

Feb 23 - Feb 27

Pro-level image generation gets faster and more accessible with Nano Banana 2
Nano Banana 2 is our state-of-the-art image generation and editing model. It delivers Pro-level image generation and editing at the speed you expect from Flash — making the quality, reasoning, and world knowledge you loved about Nano Banana Pro more accessible. Learn more about the model here.

The Intelligent Path to Compliance: Transforming Regulatory QC with Google Cloud
Reducing "Refuse to File" (RTF) risks and submission cycle times is critical for life sciences leaders. Google Cloud’s Regulatory Submission Semantic QC Auditor leverages Gemini and RAG architecture to transform Quality Control from a manual burden into an active, intelligent workflow.

By automating semantic cross-referencing, narrative coherence checks, and dynamic guidance-based auditing, this solution ensures rigorous accuracy and auditability. Operating within a secure GxP-ready environment, it empowers teams to detect subtle inconsistencies and generate remediation plans without sacrificing data privacy.

Learn more.
Stop typing, start interacting! The Gemini Live Agent Challenge is here. Build immersive agents that can help you see, hear, and speak using Gemini and Google Cloud. Compete for your share of $80,000+ in prizes and a trip to Google Cloud Next '26!

Submissions are open from February 16, 2026 to March 16, 2026. Learn more and register at geminiliveagentchallenge.devpost.com

Feb 9 - Feb 13

Introducing Gemini 3.1 Pro on Google Cloud.
3.1 Pro is a noticeably smarter, more capable baseline for complex problem-solving. We’re shipping 3.1 Pro at scale, building upon our goal to help you transform your business for the agentic future. Learn more about the model’s capabilities here. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI.
Automate Storage Compatibility with GKE Dynamic Default Storage Classes
Managing storage across mixed-generation VM clusters in GKE just got easier. With the new Dynamic Default Storage Class, Google Kubernetes Engine automatically selects between Persistent Disk (PD) and Hyperdisk based on a node's specific hardware compatibility. This abstraction eliminates the need for complex scheduling rules and manual pairing, ensuring your volumes "just work" regardless of the underlying infrastructure. By defining both variants in a single class, you reduce operational overhead while maintaining peak performance and cost-efficiency across your entire cluster.

Explore automated disk type selection
Community TechTalk: AI-Powered Apigee Development with strofa.io
Join the Apigee community on February 26 for a deep dive into strofa.io. Guest speaker Denis Kalitviansky will demonstrate how this new AI-powered tool automates and orchestrates Apigee development, from local emulators to large-scale hybrid environments. Discover how to scale your API management and streamline team collaboration using the latest in AI-driven automation.

Register now to reserve your spot.

Jan 26 - Jan 30

Simplify API Governance with Native OpenAPI v3 Support
Eliminate integration debt and accelerate deployment velocity with the General Availability of OpenAPI v3 (OASv3) support for API Gateway and Cloud Endpoints. You no longer need to downgrade modern specifications to OASv2. Instead, you can now define API contracts and enforce critical policies—including telemetry, quotas, and security—using native Google-specific extensions directly within your OASv3 files. This update ensures your APIs are secure by design while remaining fully compatible with the modern developer ecosystem and Google Cloud’s AI services.

Get started with OpenAPI v3 on API Gateway and Cloud Endpoints.

Accelerate API Testing with the New Open Source API Tester
Start validating your APIs with API Tester, a simple, YAML-based Test Driven Development (TDD) framework. Designed for the Apigee community, this tool allows you to write human-readable tests, run them instantly via a web client or CLI, and perform deep unit testing on Apigee proxies. With native support for JSONPath assertions and Apigee shared flows, you can verify everything from payload data to internal variables like proxy.basepath without leaving your terminal.

Explore the API Tester guide and start testing your proxies today.
Secure Sensitive Data with Kubernetes Secrets in Apigee hybrid
Enhance security in Apigee hybrid by accessing Kubernetes Secrets directly within your API proxies. This hybrid-exclusive feature keeps sensitive credentials within your cluster boundary and prevents replication to the management plane. It supports strict separation of duties: operators manage secrets via kubectl, while developers reference them as secure flow variables—ideal for high-compliance and GitOps workflows.

Implement Kubernetes Secrets in your hybrid proxies.
See the Console in a Whole New Light: Dark Mode is Now Generally Available in Google Cloud
Elevate your cloud management workflow with Dark Mode, now generally available in the Google Cloud console. We have delivered a modern, cohesive, and accessible experience reimagined for maximum comfort and productivity—especially during extended working hours and low-light environments. Dark Mode can be enabled automatically based on your operating system's preference, or manually through the Settings -> Appearance menu.

Switch to Dark Mode today to enjoy a modern, comfortable, and productive environment!
Apigee X Networking: PSC or VPC Peering?
Deciding how to connect Apigee X? Watch this video to compare Private Service Connect and VPC Peering. We break down northbound and southbound routing, IP consumption, and how to reach targets on-prem or in the cloud. Learn to simplify your architecture and avoid common networking "gotchas" for a smoother deployment.

Watch the video.

Jan 19 - Jan 23

Bridge the Gap: Excel-to-API Conversion in Apigee Portals
Give your customers more ways to connect! This new article by Tyler Ayers explores how to extend the Apigee Integrated Portal to support direct Excel file uploads. By leveraging SheetJS and custom portal scripts, you can enable users to upload spreadsheets, preview data, and submit it directly to your APIs, all without writing a single line of integration code themselves. It’s a powerful way to simplify onboarding for those who aren't yet API-ready.

Learn how to build it.
Elevate your applications with Firestore’s new advanced query engine
We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.

Learn more about Firestore pipeline operations.

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

Thu, 27 Aug 2026 16:00:00 +0000

When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actually delivers ROI.

What we found changed our strategy entirely.

Since the rollout of Gemini Enterprise and our previous enterprise AI deployments, Pythian observed firsthand why so many enterprise AI initiatives stall out or fail.

Most organizations trap themselves in a tool-centric mindset — buying licenses, making tools broadly available, and assuming value will naturally follow. They get stuck chasing "nickel and dime" micro-efficiencies (like saving 5 minutes per user) while missing structural, high-ROI workflow transformations. Compounding the problem, even when custom agents are built, they frequently stall in pilot mode or break down in production because teams lack the operational capability to manage AI model drift, agent lifecycles, and ongoing observability.

To solve this, we engineered the Pythian AI Operating Model — a multifaceted, end-to-end framework designed to take enterprise AI from high-level strategy all the way into sustained production. While our dual center of excellence (COE) serves as the core execution muscle, it is the application of the entire framework, from Field CTO strategy and tooling deployment to the dual COE and XOps, that consistently unlocks million-dollar outcomes.

By proving this complete model internally first, Pythian drove a 3x surge in active user engagement and cut our database incident resolution times by 80%.

The four pillars of the Pythian AI operating model

To move past the common failure points of enterprise AI, our framework consolidates strategy, execution, and operations into a single continuous loop:

Field CTO strategy ──> tooling deployment ──> dual COE execution ──> production XOps

Field CTO strategy and governance: Generative AI is arguably the most academically challenging architectural shift in IT history. Led by former C-suite tech leaders, our Field CTO practice provides executive advisory to establish steering committees and clear value metrics. The team audits operations using 16 horizontal agentic patterns (like automated document processing and runbook creation) to build a prioritized backlog of high-ROI use cases before development starts.
Tooling and platform deployment: The team establishes a secure, production-grade foundation on platforms like Gemini Enterprise and connects AI directly into CRMs, ERPs, and database estates to ground models in real corporate context.
The dualCOE: This execution muscle is split into two specialized engines:

People productivity COE: This group handles adoption and change management. Instead of expecting non-technical teams (like HR or Procurement) to build its own agents, this COE builds no-code agents for them, focusing entirely on enablement.
Process productivity COE: This team engineers deep, custom-coded AI agents and complex agentic workflows that integrate into core data platforms for autonomous operations.

XOps (AI production management): While deploying an agent is 20% of the journey, maintaining accuracy in production is 80%. Because AI models and prompt structures naturally drift over time, this XOps practice provides the continuous monitoring, prompt tuning, and model observability needed to keep agents performing without breaking core workflows.

The difference between chasing minor, scattered efficiencies and driving structural enterprise ROI comes down to how you align your operating strategy:

Alignment element	Tool-centric approach	Pythian AI operating model
Primary metric	Individual minutes saved per user	High-impact workflow reimagination and ROI
Operational focus	Broad, unguided tool availability	Prioritized backlog via 16 agentic patterns
Execution muscle	Ad-hoc user experimentation	Dual COE (people and process productivity)
Production lifecycle	Unmonitored static deployments	Active XOps (Continuous accuracy and drift management)

Real-world impact: from database ops to global supply chains

Whether managing 70 manufacturing plants or 30,000 enterprise databases, AI succeeds when tied to structural, high-value workflows:

Pythian “as a customer:” Across 15,000 monthly database tickets, our Process COE deployed an agentic workflow that reads tickets, searches knowledge bases, and auto-generates mini runbooks before an engineer touches them. The result was slashed mean time to resolution by 80% and tripled active user engagement.
Knowledge management customer: We deployed autonomous IT support agents across 10,000 consultants. As a result, we were able to automate 10% of 20,000 annual IT tickets into "no-touch" resolutions, saving 1,000,000+ operational hours.
Supply chain customer: By building custom agentic supply chain tools on Gemini Enterprise, we compressed forecast-matching cycles from weeks down to 2–3 days across 70 global manufacturing sites.
Retail customer: We combined Gemini Agentic AI and computer vision to automate store product onboarding. As a result, we transformed a 20-minute manual task into a multi-second flow.

Ready to build your AI operating model?

Scaling AI demands more than tool-level experimentation. It also requires an end-to-end AI operating model. Learn how Pythian pairs with Google Cloud to operationalize strategy, streamline XOps, and fast-track your Gemini Enterprise journey.

Deploy personal AI agents with Cloud Run instances

Thu, 27 Aug 2026 16:00:00 +0000

Need a low-cost, high-performance way to run long-lived, stateful workloads such as AI agents? Today, we introduced Cloud Run instances, which let you do just that.

Consider AI agents such as OpenClaw or Hermes, which are intended for individual developers or personal use. Because these agents often work continuously and tend to serve only one user at a time, their infrastructure requirements look quite different from stateless, high-throughput web services that typically run on Cloud Run services.

Cloud Run services scale to zero when requests stop, so they aren’t ideal for a long-lived agent that expects exactly one copy to be running continuously. On the other hand, the alternative — running a dedicated VM — means paying for full compute 24/7, managing operating system updates, opening firewall ports, and provisioning your own HTTPS endpoints.

Cloud Run instances provide dedicated, singleton compute runtimes on Cloud Run. They have the following attributes:

Runs just one instance with no autoscaling
Up to 7-day continuous runtime, with automatic restart policy configured by default
Every instance gets a HTTPS URL that remains unchanged across updates and restarts.
You can stop each instance when you aren't using it and resume it whenever you need it

The cost to run a Cloud Run instance with 1 vCPU and 1 GiB of memory continuously for 30 days is $5.70. Cloud Run instances use shared vCPU with vCPU burst budgets to run continuously for a low, predictable price. This model is also ideal for long-lived agents that aren’t doing compute-intensive work all the time, and only spike in usage when asked to perform a task.

Example: Deploy OpenClaw on a Cloud Run instance

OpenClaw is an open-source personal AI agent that can perform various tasks on your behalf, and become a better assistant over time. Many OpenClaw users start out running it on their own laptops, until they realize they need somewhere to run it where it won’t shut down every time their laptop goes to sleep.

Deploying OpenClaw to a Cloud Run instance is easy. Once you’ve uploaded OpenClaw’s configuration files to a Cloud Storage bucket, you can deploy your OpenClaw agent to a Cloud Run instance with just one command:

code_block: <ListValue: [StructValue([('code', 'gcloud beta run instances create openclaw-instance \\\r\n --image ghcr.io/openclaw/openclaw:latest \\\r\n --port 18789 \\\r\n --public \\\r\n --add-volume mount-path=/home/node/.openclaw,type=cloud-storage,mount-options="uid=1000;gid=1000;file-mode=0700;dir-mode=0700",bucket=${BUCKET} \\\r\n --set-env-vars "OPENCLAW_GATEWAY_PASSWORD=${PASSWORD},GEMINI_API_KEY=${GEMINI_API_KEY}"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5de1c8650>)])]>

Once deployed, you can keep this OpenClaw instance running for as long as you want. You can interact with it over Telegram, WhatsApp, or the social media platform of your choice, and connect it to any tools you want it to use, as well.

For the full instructions on how to deploy OpenClaw, refer to this codelab.

Coming soon, we’re also launching SSH access for both Cloud Run instances and Cloud Run services. Sign up for private access here.

What users are saying

Cloud Run instances are helping Google Cloud users achieve their goals for running AI agents and other long-lived workloads at low cost and high performance.

OffDeal, an AI-powered investment bank for small businesses, is running long-lived agents on Cloud Run instances:

“We're currently using Cloud Run instances as our primary infrastructure for our long-running agent. It reduced cold starts by 88%. Everything was very straightforward to implement, and it has been very reliable.” - Luis Ruiz Morel, Member of Technical Staff @ OffDeal

Learn more

Currently in preview, Cloud Run instances are a cost-effective way to run a new kind of workload, without sacrificing performance. For more information about Cloud Run instances, check out the following resources:

How Uber improves network reliability while unblocking cloud migration

Wed, 26 Aug 2026 16:00:00 +0000

Uber has a lot in common with the cities it serves. Both are always changing and growing, both must carefully manage the resulting traffic to prevent congestion and sprawl.

Uber has continuously evolved its technical strategies to manage its expanding network, and this careful planning and constant evolution helps ensure that application traffic across its entire platform runs smoothly. Ultimately, maintaining a reliable, high-scale platform that operates seamlessly at any given time is key to preserving user trust.

One important solution in this effort has been application awareness on Cloud Interconnect. An industry-first tool for application prioritization across hybrid networks, application awareness on Cloud Interconnect has helped Uber prioritize critical traffic to ensure business continuity during potential network congestion events.

Uber acted as an early design partner for application awareness on Cloud Interconnect, helping ensure that this capability met the demands of Uber’s global-scale operations. It not only improved Uber’s daily operations, it also gave Uber the confidence to move forward with a Google Cloud migration, with confidence that there would be less risk of service interruptions during switchovers.

In this post, we’ll explain the features Uber most sought and why, the inner workings of application awareness on Cloud Interconnect, and how it can help other organizations as well.

Prioritizing critical traffic

When migrating distributed, hybrid, or multicloud applications at a global scale, network reliability becomes a primary concern. Even the most worthwhile migrations may not seem worth it if such migrations interrupt ongoing service. For organizations like Uber, moving vast amounts of data to support large data analytics workload — including emerging AI use cases — can saturate network links, resulting in increased reliability risk for their critical application traffic.

With standard cloud interconnect approaches, enterprises typically apply simple bandwidth overprovisioning to meet extreme infrastructure needs. But with today's hybrid cloud demands, and given the size of an organization like Uber, overprovisioning network capacity for peak usage is often too costly and unreliable.

The shortcomings of overprovisioning only become magnified with the integration of cutting-edge AI innovations. Uber needs systems in place that can take on massive data transfers without congesting its network and protecting the performance of business-critical applications.

With the benefit of application awareness on Cloud Interconnect, including the four major features of application awareness — traffic handling, congestion response, latency management, and cost efficiency — Uber was able to achieve the networking optimization its modern tech stack requires.

Starting with a private preview, Uber deployed this feature across its infrastructure, beginning with Google Cloud Interconnect deployments in Phoenix, Arizona, and Ashburn, Virginia. Application awareness on Cloud Interconnect allows Uber to classify and prioritize end-user application traffic over less time-sensitive data using DSCP marking and configured queuing profiles.

In the following chart, we look at the four key features of application awareness on Cloud Interconnect, how they differ from legacy approaches, and how they help provide better operational continuity for organizations like Uber.

Feature	Standard interconnect solutions	Application awareness on Cloud Interconnect
Traffic handling	All traffic treated equally (first-in, first-out)	Traffic classified into six distinct traffic classes
Congestion response	High-priority application traffic may be dropped during bursts	Business-critical traffic is protected via strict priority or bandwidth sharing policies
Latency management	Unpredictable latency for high priority applications	Predictable and consistent low-latency for time-sensitive workloads
Cost efficiency	Requires expensive overprovisioning to absorb peaks	Efficient bandwidth utilization and lower TCO

Uber's key takeaways

For Uber, the business value of being able to prioritize business-critical traffic on its networks by deploying application awareness on Cloud Interconnect was immediate. And in doing so, Uber has also created a blueprint that other enterprises with similar hybrid cloud challenges can replicate. The core elements of that blueprint include:

Ensuring business continuity: Uber can decide in real time which application traffic to prioritize during major, high-traffic events. This means that mission critical applications stay up and running during even extreme events (both planned and unplanned). Uber leadership has called application awareness on Cloud Interconnect important for its global operations.
Efficient bandwidth utilization: Instead of blindly overprovisioning bandwidth to prevent congestion, application awareness allows Uber to better utilize their existing Cloud Interconnect capacity aligned with their expected network bandwidth needs. The result is lower total cost of ownership for network infrastructure.
Unblocked workload migration: By protecting critical applications from network congestion, Uber was able to migrate significant workloads to Google Cloud and, in the process, dramatically reduce operational overhead.

"Application awareness on Cloud Interconnect was the key that unlocked our ability to migrate more strategic workloads to Google Cloud and is critical for maintaining service reliability during peak global demand. By allowing us to intelligently prioritize traffic, it helps us ensure that we can protect our higher priority services and make our infrastructure more efficient, lowering our total cost of ownership. This wasn't just a feature deployment; it was a deep engineering partnership that delivered a solution critical to our business." – Harry Liu, Director of Engineering, Uber

Securing network reliability for AI and beyond

As more enterprises integrate cloud-based AI models, distributed applications, and data analytics, it's becoming a business imperative to be ready to handle the massive data transfers that follow. But in doing so, they also have to ensure they never compromise the reliability of their critical applications.

With application awareness on Cloud Interconnect, Uber demonstrated that moving beyond simple bandwidth overprovisioning to protect business-critical traffic was an essential step to building the stability required to embrace modern hybrid and multicloud strategies.

You can read our blog about the potential of Cloud Interconnect across industries to learn more about what the service can bring to your organization, and if you’re ready to explore more, our team of networking and industry experts are ready to help.

Simplify your resilience testing strategy with Fault Injection Testing

Wed, 26 Aug 2026 16:00:00 +0000

When databases fail and network paths falter, you still need your mission-critical cloud services to stay online. Yet guaranteeing high availability has become increasingly difficult because of the complexity of modern distributed systems.

To help you maintain availability and reliability during adverse events, we’re announcing Fault Injection Testing in preview. Fault Injection Testing is designed to help developers and architects automate failure testing to ensure predictable behavior during disruptions.

By deliberately introducing faults into your environment, you can verify your safety mechanisms before an actual outage impacts your customers.

Why native resilience testing matters

Unlike in self-hosted data centers, cloud applications offer less direct access to underlying infrastructure to facilitate failover testing.

Without native tools to prove your application can survive a failure, you risk a critical gap in your reliability strategy that exposes you to several risks:

Damaged trust and reputation: Frequent failures or poor performance lead to customer dissatisfaction and long-term damage to your brand's image.
Compliance and regulatory penalties: For many industries, particularly financial institutions, failing to prove disaster recovery capabilities can lead to non-compliance, audits, and fines.
Migration delays: Large-scale migrations often stop when teams cannot verify that critical applications will remain stable during a zone failure.

How Fault Injection Testing works

Fault Injection Testing allows you to run experiments by creating experiment templates. These templates act as blueprints, defining the specific fault to be injected and the resources that will be targeted for the experiment.

In this public preview, you can test two primary failure scenarios:

Failover Cloud SQL: This fault triggers a failover of a high availability Cloud SQL instance from the primary zone to a standby zone.
Degrade application traffic: This allows you to selectively add latency and HTTP error codes through a Layer 7 load balancer.

Before any fault is injected, Fault Injection Testing performs an automated dry run. This read-only simulation checks your permissions and provides an up-to-date list of every resource that will be affected.

Once you verify the scope, you can manually start the injection. The duration you defined in the template will run its course, and the faults will be reverted at the expiration of the timer.

During the experiment, you can verify that your application is behaving as you planned. If things do not go as planned, you can use the stop and revert capability to immediately halt the experiment and begin restoring resources to their normal state.

During preview, we recommend as a best practice to use FIT in a non-production environment. Preview is an opportunity to get early access to learn how the service fits and complements your existing testing practices, and to provide us with your feedback to improve the product as well!

Built for the enterprise

Partners like KeyBank and Servier are already using Fault Injection Testing to validate their deployments. By using native fault injection, these organizations can approximate demanding failure scenarios — such as zonal outages — to help ensure their services remain stable.

Get started with Fault Injection Testing

Fault Injection Testing is available through the Google Cloud console, the gcloud CLI, and REST APIs.

Request preview access: Talk to your Google Cloud Account Team to add your project to the preview.
Enable the API: Search for "Fault Testing API" in your Google Cloud console and select enable.
Assign roles: Ensure your team has the roles/faulttesting.operator role to configure and run experiments.
Run your first dry run: Create a template for a Cloud SQL or load balancer resource in a non-production environment and execute a dry run to see the potential impact.

For more details on implementation, talk to your account team, or view the User Guide for Fault Injection Testing.

Using OKF with Knowledge Catalog to serve context for agents

Wed, 26 Aug 2026 16:00:00 +0000

We continue to iterate on the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. But a big question remains: How can you share and govern access to an OKF bundle across an organization?

OKF v0.1 established a portable format for the context agents need: markdown files with YAML frontmatter, one required field, and five conventions. Then, OKF v0.2 added the trust signals (provenance, verification, freshness, attestation) that a machine-authored bundle requires to be relied on, allowing a team to publish a trustworthy bundle for its own agents.

However, what OKF does not answer is how teams share their bundles across an organization. A git repo per bundle is portable, but it is not searchable alongside the data it describes, it cannot be secured and governed using the same organizational identity and compliance policies, and it does not sit next to the technical metadata (schemas, lineage, ownership) that data teams already work in. Every downstream agent must know where each bundle resides, and that does not scale beyond a small number of bundles.

To scale an OKF bundle across an organization, you can use Knowledge Catalog, Google Cloud's context engine for agents. By mapping the bundle onto Knowledge Catalog's existing types, every concept becomes discoverable, governed, and reachable by any agent already reading from the catalog.

Knowledge Catalog is the context engine for agents

Every agent that queries Knowledge Catalog reads from one governed index over what the organization already has in BigQuery, Cloud Storage, operational databases, and applications. Each entry carries schema, lineage, ownership, and tags, and can be extended with typed aspects that add domain-specific fields. The same catalog exposes search and cross-project lookup to retrieve optimized context for each agentic query. The context retrieval is secure and governed by IAM controls, so agents can only see the entries they have access to based on IAM identity.

Publishing an OKF bundle into Knowledge Catalog takes a one-time setup and a single push. Both use the OKF sample code in the Knowledge Catalog repository, whose wrappers call gcloud dataplex for setup and delegate push to kcmd (the Metadata-as-Code CLI in the same repository).

The setup registers three Knowledge Catalog resources: an EntryGroup to hold the bundle, an EntryType named okf-bundle for its concepts, and an AspectType named okf that carries the OKF signal fields (from the okf-aspect.json schema in the sample code). The push then creates one okf-bundle Entry per concept, each with two Aspects: an overview Aspect for the markdown body, and an okf Aspect for the structured signals. Display name, description, and tags live on the Entry itself. The bundle's index.md navigation files and its root log.md are also published as Entries: index files carry only the overview Aspect (no OKF frontmatter), and log.md carries both Aspects with okf_type: Log.

Everything Knowledge Catalog already does for technical metadata (search, IAM, lineage, cross-project discovery) applies equally to OKF bundles, alongside the data they describe.

The okf AspectType

The okf-aspect.json schema in the sample code defines the AspectType. It carries 13 fields covering the full OKF v0.2 spec:

#	Field	Type	Purpose
1	`okf_type`	string	The OKF document type (freeform, e.g. `BigQuery Table`, `Metric`, `Attested Computation`).
2	`generated`	record `{by, at}`	Actor and timestamp for the last meaningful change.
3	`sources`	array of `{id, resource, title, author, usage_count, last_modified}`	Materials the concept derives from, with credibility signals.
4	`verified`	array of `{by, at}`	Verification events. A `human:` actor marks the highest trust tier.
5	`status`	string	Lifecycle state: `draft`, `stable`, or `deprecated`.
6	`stale_after`	datetime	Absolute point in time (RFC3339 with an explicit offset) on or after which the content is stale.
7	`usage_window`	record `{from, to}`	Period the source usage counts were measured over.
8	`runtime`	string	How an Attested Computation runs (e.g., `bigquery`).
9	`parameters`	array of `{name, type, required}`	Typed named holes a caller may fill. The only surface a caller may vary.
10	`computation`	string	Path to a file holding the computation body.
11	`executor`	record `{resource, receipt[]}`	How the computation runs and what evidence it must return.
12	`attester`	record `{resource}`	Deterministic code that takes a receipt and returns a verdict.
13	`extra`	string	Producer-defined frontmatter the template does not model, as JSON `[path, value]` pairs. Keeps the round-trip lossless.

Every field is annotated with a display name, a description, and a mandatory index. Any top-level scalar field in the okf Aspect (okf_type, status, stale_after, runtime, computation, extra) can drive Knowledge Catalog search predicates directly, so aspect:acme-analytics.us-central1.okf.okf_type=Metric returns every OKF Metric in scope. Scalar subfields of record fields (generated.by, usage_window.from, executor.resource, attester.resource) also drive predicates. The array fields (sources, verified, parameters) are not server-side searchable on their subfields; agents narrow on them client-side after entries.get with view=ALL. One caveat for search predicates on datetime-typed fields (stale_after, generated.at, usage_window.from/.to), use a bare date (stale_after=2026-12-31) or a range comparison (stale_after>2026-01-01), not the full RFC3339 timestamp.

Pushing a bundle

kcmd push reads an OKF bundle from git and writes each concept as an Entry in the target Knowledge Catalog EntryGroup. index.md files become Entries too, and each concept is parented to the index above it, so the bundle's directory structure survives as a browsable hierarchy.

kcmd expects a bundle in the Documents Layout: markdown files under a catalog/ subdirectory, and a catalog.yaml at the bundle root that lists the snapshot's entry and aspect types. The sample code's setup.ts generates catalog.yaml from its --entry-group flag (default okf_demo), so a reader wiring the sample to a new bundle passes the flag rather than editing catalog.yaml by hand.

Here is an end-to-end workflow for the Acme Retail bundle that we introduced in the OKF v0.2 blog:

code_block: <ListValue: [StructValue([('code', '# One-time setup (if required): install bun, clone the repo, build kcmd, configure gcloud\r\ncurl -fsSL https://bun.sh/install | bash\r\nexport BUN_INSTALL="$HOME/.bun" && export PATH="$BUN_INSTALL/bin:$PATH"\r\ngit clone https://github.com/GoogleCloudPlatform/knowledge-catalog\r\ncd knowledge-catalog/toolbox/mdcode && npm install && npm run build\r\n\r\n# Authenticate, set project and enable dataplex apis\r\ngcloud auth login\r\ngcloud config set project <your-project>\r\ngcloud config set compute/region <your-location>\r\ngcloud services enable dataplex.googleapis.com\r\ngcloud auth application-default login\r\n\r\n# Push the Acme Retail bundle\r\ncd demo/okf\r\nbun run setup.ts # creates the EG (default \'okf_demo\')\r\nbun run push.ts # pushes okf/bundles/acme_retail into the EG setup created'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dd308310>)])]>

To pick a different EntryGroup name or push a different bundle, pass --entry-group your-name to setup.ts and --bundle path/to/your/bundle to push.ts. For example: bun run setup.ts --entry-group acme-bundle followed by bun run push.ts. This regenerates the manifest, so subsequent push, pull, and cleanup all target the new EG; delete earlier EGs manually with gcloud dataplex entry-groups delete <name> --project <your-project> --location <your-location>.

The Acme Retail bundle is a synthetic OKF bundle for a US retailer's BigQuery estate. It contains nine leaf concepts across six directories (attesters, tables, metrics, computations, policies, skills), each with its own index.md, plus a bundle root with its own index.md and log.md. That's 17 pushed Entries in total; Dataplex auto-creates one <eg>_entry alongside, so gcloud dataplex entries list returns 18 rows.

After the push completes:

Every concept markdown file is a Knowledge Catalog Entry, discoverable by search across the whole project or organization, depending on IAM configuration.
The revenue-ytd Attested Computation appears in the console with its sanctioned SQL, its executor, its attester, its verification history, and the full concept body.
An analyst searching Knowledge Catalog for "revenue" finds Acme Retail's business definition alongside the BigQuery table it computes from, both under one permission model.
A downstream agent that already calls LookupContext for BigQuery table Entries retrieves the bundle's context by adding the OKF entry names to its resources list.

Further, metrics/revenue.md becomes an Entry with two Aspects. The full entries.get response (with view=ALL) looks like:

code_block: <ListValue: [StructValue([('code', '{\r\n "name": "projects/acme-analytics/locations/us-central1/entryGroups/acme-retail/entries/metrics/revenue",\r\n "entryType": "projects/acme-analytics/locations/us-central1/entryTypes/okf-bundle",\r\n "createTime": "2026-08-15T00:48:39.123456Z",\r\n "updateTime": "2026-08-15T00:48:57.234567Z",\r\n "parentEntry": "projects/acme-analytics/locations/us-central1/entryGroups/acme-retail/entries/metrics/index",\r\n "entrySource": {\r\n "displayName": "Revenue",\r\n "description": "Recognized revenue for a period, per Acme\'s FY2026 revenue-recognition policy. Backed by an Attested Computation.",\r\n "labels": {\r\n "finance": "true",\r\n "revenue": "true",\r\n "headline-metric": "true"\r\n },\r\n "location": "us-central1"\r\n },\r\n "aspects": {\r\n "dataplex-types.global.overview": {\r\n "aspectType": "projects/dataplex-types/locations/global/aspectTypes/overview",\r\n "createTime": "2026-08-15T00:48:57.111111Z",\r\n "updateTime": "2026-08-15T00:48:57.111111Z",\r\n "aspectSource": {},\r\n "data": {\r\n "content": "# Definition\\n\\nRevenue for a fiscal year is the sum of `net_amount` over orders that (a) reached `order_status = \'delivered\'`, (b) completed the 30-day return window, and (c) fall in the fiscal year by `order_ts`. Multi-currency orders are converted to USD at the `order_ts` daily reference rate. [^revenue-policy]\\n\\nThe sanctioned computation is [`computations/revenue-ytd.md`](../computations/revenue-ytd.md). Consumers MUST run and attest that computation rather than composing their own SUM. The attester rejects any receipt whose executed SQL does not match the sanctioned form.\\n\\n# Reporting cuts\\n\\n- **By fiscal year:** the sanctioned computation takes `year` as its sole parameter.\\n- **By channel or category:** these are approved narrations, not new metrics. Join the receipt\'s row-level result to `orders.channel` or to `order_lines` × `products.category` client-side. Do NOT rewrite the sanctioned SQL.\\n\\n# Trust and freshness\\n\\n- **Verified:** VP Finance sign-off on 2026-07-01, against the FY2026 policy.\\n- **Stale after 2026-12-31:** Finance re-issues the revenue recognition policy each January. Consumers of this concept after 2027-01-01 MUST re-verify the definition against the new policy before serving.\\n\\n[^revenue-policy]: Revenue Recognition Policy (FY2026)",\r\n "contentType": "MARKDOWN"\r\n }\r\n },\r\n "acme-analytics.us-central1.okf": {\r\n "aspectType": "projects/acme-analytics/locations/us-central1/aspectTypes/okf",\r\n "createTime": "2026-08-15T00:48:57.222222Z",\r\n "updateTime": "2026-08-15T00:48:57.222222Z",\r\n "aspectSource": {},\r\n "data": {\r\n "okf_type": "Metric",\r\n "generated": { "by": "reference_agent/gemini-2.5-pro", "at": "2026-06-30T14:00:00Z" },\r\n "verified": [ { "by": "human:jsmith@acme", "at": "2026-07-01T09:00:00Z" } ],\r\n "status": "stable",\r\n "stale_after": "2026-12-31T00:00:00Z",\r\n "sources": [\r\n {\r\n "id": "revenue-policy",\r\n "resource": "policies/revenue-recognition.md",\r\n "title": "Revenue Recognition Policy (FY2026)",\r\n "author": "human:jsmith@acme",\r\n "last_modified": "2026-06-15T00:00:00Z"\r\n }\r\n ]\r\n }\r\n }\r\n }\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dd4fa490>)])]>

The overview Aspect holds the full body of revenue.md. The okf Aspect carries the structured signal fields, so agents get provenance, source, and OKF type in a form they can filter on directly instead of parsing markdown. Server-side searchEntries filters on the top-level scalar fields and on the scalar subfields of record fields; agents narrow further on the array-element subfields client-side after entries.get. (Aspects and EntryTypes are keyed by project number in real API responses and search predicates; the acme-analytics project ID is shown throughout for readability.)

What pushing your OKF to Knowledge Catalog enables

Once the bundle is in Knowledge Catalog, it provides two capabilities to any agent that reads from the catalog:

Discoverability across the organization. Agents find bundle concepts through the same searchEntries and LookupContext APIs they already use for cataloged data, so an OKF bundle appears alongside BigQuery tables and other resources in every query it matches.
Governance. Bundle Entries inherit IAM from the EntryGroup, so a single agent call returns exactly what the caller is permitted to read, with no parallel permission model to maintain.

Discoverability across the organization
OKF bundle Entries appear in searchEntries results alongside BigQuery tables and other cataloged resources, so an agent already querying the catalog picks up new bundles automatically. To retrieve a concept's body, trust signals, or linked concepts from a match, the agent moves to LookupContext and entries.get.

A LookupContext call looks like this:

code_block: <ListValue: [StructValue([('code', 'POST https://dataplex.googleapis.com/v1/projects/acme-analytics/locations/us-central1:lookupContext\r\n{\r\n "resources": [\r\n "projects/acme-analytics/locations/us-central1/entryGroups/acme-retail/entries/metrics/revenue"\r\n ],\r\n "options": { "format": "yaml", "context_budget": "8000" }\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dd4f89d0>)])]>

The response is a single context field containing a pre-formatted YAML block. The block carries the entry's catalogEntry, its type, its description, its tags as labels, and its overview: the full markdown body of the concept, including its trust and freshness section. LookupContext does not render custom Aspects, so an agent that needs the structured OKF signal fields (okf_type, generated, sources, and the other ten) reads them with entries.get and view=ALL alongside the LookupContext call.

There is no repository clone, no manual Aspect merging, and no re-parse of frontmatter. The agent uses the same API call any Knowledge Catalog client already makes.

An agent traversing an OKF bundle typically follows a three-step flow. An agent that already knows the specific Entry names it needs skips step 1. An agent that already knows the target EntryGroup and wants to enumerate the bundle exhaustively substitutes entryGroups.entries.list for step 1.

searchEntries returns candidate Entry names and descriptions. Its scope accepts a project or organization; narrowing within that scope happens through query terms, including aspect predicates like aspect:acme-analytics.us-central1.okf.okf_type=Metric.
LookupContext on the top few Entry names (up to ten per call) returns the full concept body as pre-formatted YAML; context_budget caps the response size.
entries.get with view=ALL on any Entry returns its structured OKF signals (okf_type, generated, sources, and the other ten) directly, which the agent can then filter or attest on.

When a concept's sources[] references another concept by path, the agent calls LookupContext on that Entry name to walk the reference.

The full response for the Revenue Entry:

code_block: <ListValue: [StructValue([('code', "resources:\r\n -\r\n catalogEntry: projects/acme-analytics/locations/us-central1/entryGroups/acme-retail/entries/metrics/revenue\r\n type: OKF Document\r\n description: Recognized revenue for a period, per Acme's FY2026 revenue-recognition\r\n policy. Backed by an Attested Computation.\r\n overview: |-\r\n # Definition\r\n\r\n Revenue for a fiscal year is the sum of `net_amount` over orders that (a) reached `order_status = 'delivered'`, (b) completed the 30-day return window, and (c) fall in the fiscal year by `order_ts`. Multi-currency orders are converted to USD at the `order_ts` daily reference rate. [^revenue-policy]\r\n\r\n The sanctioned computation is [`computations/revenue-ytd.md`](../computations/revenue-ytd.md). Consumers MUST run and attest that computation rather than composing their own SUM. The attester rejects any receipt whose executed SQL does not match the sanctioned form.\r\n\r\n # Reporting cuts\r\n\r\n - **By fiscal year:** the sanctioned computation takes `year` as its sole parameter.\r\n - **By channel or category:** these are approved narrations, not new metrics. Join the receipt's row-level result to `orders.channel` or to `order_lines` × `products.category` client-side. Do NOT rewrite the sanctioned SQL.\r\n\r\n # Trust and freshness\r\n\r\n - **Verified:** VP Finance sign-off on 2026-07-01, against the FY2026 policy.\r\n - **Stale after 2026-12-31:** Finance re-issues the revenue recognition policy each January. Consumers of this concept after 2027-01-01 MUST re-verify the definition against the new policy before serving.\r\n\r\n [^revenue-policy]: Revenue Recognition Policy (FY2026)\r\n labels:\r\n finance: 'true'\r\n revenue: 'true'\r\n headline-metric: 'true'"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dd4f9a50>)])]>

Governance
Permissions on the EntryGroup use standard Knowledge Catalog IAM. An agent that names both a bundle concept and the BigQuery table it grounds against in one call receives both, each subject to its own existing access control list (ACL), so the response carries only what the caller is already permitted to read. There is no parallel permission model to maintain.

Reading agents use roles/dataplex.catalogViewer, which grants the read paths: entries.get, LookupContext, and searchEntries. The identity that runs kcmd push uses roles/dataplex.catalogEditor, which grants the write paths: entries.create and entries.patch. One EntryGroup per bundle-owning team is the multi-team pattern, and IAM on the EntryGroup cascades to its Entries.

LookupContext resolves the entry names it is given, up to ten per call, within a single location. It does not follow links out of a concept's body, so an agent that wants a referenced concept must name it explicitly. Place the bundle's EntryGroup in the same location as the data it describes to fetch both in one call.

Lifecycle

kcmd push is an idempotent upsert. Re-running is safe (no duplicates, no error), but every push writes every Entry. Concept deletes require an explicit kcmd delete on the Entry, or cleanup.ts to remove the whole EntryGroup at once; cleanup.ts deletes only the EntryGroup and its Entries, so the shared okf AspectType and okf-bundle EntryType stay in place for other bundles that reference them. For continuous ingestion in production, wire a CI job to kcmd push on every commit to the bundle repository, using a service-account credential with roles/dataplex.catalogEditor on the target EntryGroup.

Getting started

OKF defines what a trustworthy bundle looks like. Knowledge Catalog makes it reachable across the organization. To get started, check out the following resources:

Read the OKF v0.2 spec and browse the Acme Retail bundle.
Author a small bundle for one domain your team owns.
Sync it into your Knowledge Catalog project using the sample code's setup.ts (which registers the resources) and push.ts (which delegates to kcmd).
Point your existing agents at Knowledge Catalog. New context becomes reachable through the same LookupContext and searchEntries calls they already use.

Dynamic capacity management for AI infrastructure

Wed, 26 Aug 2026 13:30:00 +0000

The internet connected billions of people and mobile devices, putting computers in every hand. Now, we’re in the middle of the next big technology shift, deploying millions of autonomous AI agents to work alongside employees and end users. Today, we announced new FinOps controls for Gemini Enterprise to help organizations manage project-level AI spend and eliminate token shock. But the sheer scale of the agentic era is placing new constraints at every layer of the stack, including infrastructure. AI workloads are notoriously difficult to architect, resource-intensive, and bursty, which can also lead to scaling bottlenecks and large pools of underutilized — or misutilized — compute resources.

Organizations need insights to help them extract more value from their infrastructure investments. In this blog, we outline best practices for dynamic capacity management — scheduling and utilization strategies to help you run enterprise and AI applications on a single, flexible foundation with predictable cost and performance. These capabilities are designed to augment our on-demand, Spot and committed use discount (CUD) consumption models, which provide flexible pricing and discounting for your workloads. Let’s jump in.

Here's a quick summary

Three ways you can implement dynamic capacity management:

Schedule capacity for planned events. Schedule mission-critical resources (GPUs, TPUs and select VM families) ahead of planned events using calendar mode, or optimize costs for batch jobs with flexible start times using flex-start mode in Dynamic Workload Scheduler. Once you obtain the capacity, those resources are guaranteed for the specified duration.
Maintain service continuity by creating a fallback plan for every application. Define automated, prioritized hardware fallback lists using managed instance groups (MIGs) so your apps automatically pivot to the next approved compute option when your preferred option isn’t available.
Automate your entire capacity management lifecycle on a single, adaptive control plane. Google Kubernetes Engine (GKE) provides an agent-native environment to orchestrate the entire process — from fallback lists using Custom ComputeClasses, to granular hardware slicing with dynamic resource allocation, so agents can rapidly spin up in secure sandboxes and containers while it dynamically reallocating resources on the fly.

Why architectural flexibility matters

Ninety percent of enterprises want to deploy agents within the next three years, but only 17% of IT leaders feel confident their current IT setup can handle the load. Because these workloads have unique performance needs, organizations are racing to adopt specialized infrastructure, including accelerators (GPUs, TPUs) and CPUs with customized compute, memory, and storage ratios. However, agents also require access to enterprise applications and databases — often at a volume and scale that vastly exceeds typical human usage. Handling the intense demands of both agents and the applications they interact with requires a dynamic infrastructure. Infrastructure teams can leverage custom-designed processors like Google’s Axion to meet these needs, but hardware isn’t a complete solution. They also need ways to use that infrastructure wisely, solving execution inefficiencies to enable more flexibility across the stack.

How to overcome infrastructure constraints

Achieving this kind of flexibility requires a two-pronged approach: securing resources for the demand you can predict, and building automation to respond to the demand you can't. Combining the two, you can preschedule capacity for planned events and your infrastructure can adapt to unexpected changes without manual intervention.

1. Schedule capacity for planned events

You can secure mission-critical capacity ahead of scheduled milestones, offline training, or anticipated demand surges using Dynamic Workload Scheduler. By scheduling the resources you need up front, you optimize your spend and ensure you get access to the compute resources you need. Dynamic Workload Scheduler supports hardware accelerators (TPUs and GPUs) and select CPUs with two distinct modes:

Flex-start mode: Use this for latency-tolerant workloads like batch processing, model training, or offline fine-tuning. Instead of requiring resources immediately, you submit a defined duration request and the system intelligently queues your job, provisioning the resources as soon as capacity becomes available. This maximizes cost-efficiency and drastically improves your ability to obtain high-demand accelerators.
Calendar mode: Use this for mission-critical, time-bound events like a major product launch, a scheduled migration, or a seasonal traffic surge. By specifying the exact start and end dates of your event, you create a future reservation. This guarantees the requested capacity will be available when the event begins.

2. Maintain service continuity by creating a fallback plan for every application

Not every spike in traffic is predictable. You also need to plan for unexpected traffic from, say, a breaking news cycle or a sudden market shift that drives a surge in user activity. To help your services get the resources they need without interruption, you need a fallback plan — an automated, prioritized sequence of acceptable hardware configurations. This strategy:

Decouples your workloads from a single VM shape, size, or configuration. This allows them to run without manual intervention if your preferred option is unavailable
Allows you to execute a progressive tech refresh by adopting the newest VM generations as your primary choice while keeping older generations as an automatic fallback option.

If you run non-containerized workloads on Google Compute Engine, you can dynamically manage capacity with instance flexibility in managed instance groups (MIGs) and bulk VM creation. Instance flexibility lets you specify multiple machine types for your VM instances rather than being limited to a single machine type.

How it works: If your preferred machine type is temporarily unavailable, the MIG automatically provisions a compatible alternative from your list based on real-time capacity. When combined with location flexibility — by specifying multiple zones your MIGs can search within a region — you can drastically improve your provisioning success rate. If your MIGs use Spot VMs, Compute Engine automatically integrates with Spot capacity signals to prioritize machine types that offer longer estimated uptimes and lower risk of pre-emption.

You can also extend instance flexibility to your block storage layer by setting baseline disk defaults and configuring disk overrides so your storage adapts when a VM falls back to a different machine type.

How it works: Most of the time you can simply rely on our default options, omitting ‘disk type’ from the instance template entirely. However, for data disks that will outlive their associated VMs, it’s possible to enable a fast, durable Hyperdisk across multiple VM generations.

While Compute Engine provides instance flexibility for organizations working with virtual machines, GKE goes a step further and automates the entire capacity lifecycle from a single control plane. With GKE custom ComputeClasses, platform teams can design multi-dimensional fallback lists, automatically combine different VM machine families, sizes, and ratios, scale across multiple zones, and shift between on-demand and Spot VMs. By using Dynamic Workload Scheduler as a capacity target, and custom ComputeClasses to define the policy and priority, you can fully automate the capacity management lifecycle.

How it works: Once you’ve set up ComputeClasses, GKE automatically detects when a preferred node configuration is unavailable and falls back to your pre-approved alternative options in order of priority. When active migration is enabled, GKE gracefully migrates workloads back to higher-priority node configurations as capacity becomes available. For short-lived disks such as boot disks, GKE dynamically picks the right defaults based on the instance family. However, for long-term disks that will outlive the VM, you can use Hyperdisk.

Another GKE feature, dynamic resource allocation, helps eliminate wasteful, all-or-nothing hardware assignments by letting developers define advanced rules that dictate how resources are consumed.

How it works: Instead of claiming an entire GPU or TPU, your application specifies its exact parameters — such as total memory or number of cores — and the system allocates the perfect slice of hardware, helping to maximize utilization and reduce costs.

Take the next step toward dynamic infrastructure

Scaling AI shouldn’t mean linearly scaling your infrastructure budget or accumulating more tech debt. As these examples show, the right tools can help you overcome constraints and dramatically alter the value you get from your compute investments. Here are three steps to get started:

Audit your workloads for immediate cost-savings: Identify any applications currently tightly coupled to a single VM family, machine type, or availability zone, and map out viable alternative hardware shapes. Look beyond your existing configurations to evaluate new compute options that might better serve or act as alternatives based on your workload-level objectives. Then use Compute Engine MIGs, bulk VM creation or GKE Custom ComputeClasses to adopt them automatically, integrating them into your fallback lists.
Commit to a minimum spend for deeply discounted prices: Receive automatic discounts for sustained use, or up to 63% off when you sign up for Compute flexible committed use discounts, where your discount is tied to the resources you use regardless of the specific machine type or location.
Engage your account team: Reach out to your Google Cloud account team to craft a tailored capacity management strategy and configure your automated fallback lists.

FinOps for the AI era: New flexible billing and cost controls for agents

Wed, 26 Aug 2026 13:30:00 +0000

Editor's note: A product image was updated after initial publication.

As AI takes on more complex work, business leaders face a new challenge: enabling rapid innovation using agents while protecting their margins and budgets. To get a real return on AI, financial operations (FinOps) and cost management must evolve alongside technology, giving you clear visibility, proactive cost controls, and flexible payment models that fit your needs.

That’s why today we’re introducing expanded billing flexibility and new cost management tools for agent workloads across Gemini Enterprise and developer tools like Google Antigravity in Gemini Enterprise and Android Studio.

Flexible payment options: You can mix our existing, predictable per-user seat subscriptions with a new pay-as-you-go option in Gemini Enterprise app that lets you run agent workloads without hitting quota limits mid-task.
Developer access, one place to manage your AI: Google Antigravity and Android Studio AI use is now included in your Gemini Enterprise subscription (available for select customers and rolling out broadly soon), giving your developers more without giving you more to manage. Usage across Antigravity, the platform, and the app rolls up into a single view instead of separate licenses and billing silos.
Pay less as your usage grows: If your AI workloads are steady or climbing, Flexible Savings Plans let you commit to a monthly spend you're comfortable with and take 10–20% off your token costs — no minimums, no maximums, and no new billing silo to manage.
Consolidated spend guardrails: You can now set hard monthly caps on AI spend and projects, estimate agent runtime costs, and catch sudden budget spikes before they hit your invoice.

Give your teams flexibility without losing control over spend in Gemini Enterprise

Every organization operates differently. Even within the same business, no two teams consume AI in the same way. Your business users might rely on steady, everyday productivity tools. Meanwhile, your technical teams might run AI agent workloads in bursts.

To help align costs with how work actually gets done, you can combine these payment and licensing choices and features across Gemini Enterprise:

Option	How it works	Why it helps optimize spend
Gemini Enterprise app per-user seat subscription	You pay a fixed monthly fee per user, which includes daily quota pools that are shared across your entire project.	Predictable budgeting. It provides finance teams with a clear, steady monthly baseline for teams with consistent daily productivity needs.
[New] Gemini Enterprise app pay-as-you-go consumption edition *available for select customers and rolling out broadly soon	There is no upfront commitment or base subscription fee, meaning you pay strictly for the compute and tokens your teams consume at standard model API rates.	Only pay for what you use. Your spend scales up and down automatically with real usage, ensuring you never pay for empty seats when project demand dips.
[New for Antigravity in Gemini Enterprise] Consolidated pooled quotas	Daily usage allowances are pooled project-wide, letting business apps, developer tools, and custom agents draw from the same shared quota. Pooled quota is always exhausted first, and admins can control if overages are allowed, at which point it’s charged at pay-as-you-go rates.	Maximized resource usage: Unused daily allowances from business users automatically absorb heavy developer or custom API agent demands, so no quota allowance goes to waste.
[Coming soon] Deferred execution pricing *available for select workloads soon	Mark eligible agent workloads as deferred, and our intelligent scheduler in the Gemini Enterprise Agent Platform runs them during off-peak capacity windows.	Substantial discounts for work that can wait: AI workloads can run on separate, off-peak capacity, you pay up to half the inference cost and bypass standard quota limits entirely – letting you run substantially more agentic volume under the same budget.

Equip developers with advanced agentic tooling under a single Gemini Enterprise subscription

We’re rolling out access to Google Antigravity in Gemini Enterprise, an agent-first developer platform that brings powerful agentic coding and agent-building capabilities to technical teams, included with Gemini Enterprise subscriptions for eligible customers. In addition, Android developers can leverage the Google Antigravity quota included in their Gemini Enterprise subscriptions natively in Android Studio, the agentic IDE for professional Android development.

To be more efficient with agentic coding costs, we are pooling developer tools quota included in each Gemini Enterprise subscription and making it available across the whole Google Cloud project so your teams can benefit from the capacity you’re already purchasing. Your developers get access to advanced agentic tools, while you maintain centralized governance and control.

For a closer look into what’s new with Antigravity in Gemini Enterprise and how customers are putting it to work in production, take a look at our deep-dive.

Budget smarter with Gemini Enterprise Flexible Savings Plans (FSPs)

If your organization has steady or growing AI workloads, Gemini Enterprise Flexible Savings Plans offer a simple, spend-based commitment model across Gemini Enterprise usage. FSPs are designed to lower token costs while keeping budgets flexible:

Programmatic savings: Receive 10% off for 1-year or 20% off for 3-year commitments for monthly spending across Gemini Enterprise.
Tailored to your pace: With no minimum or maximum spend requirements, you can determine a monthly commitment that fits your current traffic and make adjustments as your usage increases over time.
Enterprise Agreement (EA) friendly: FSP spend seamlessly draws down against your existing Google Cloud EA, giving lines of business dedicated budget control without fragmenting your broader cloud commitments.

Gemini Enterprise Flexible Savings Plans are already available for self-serve customers and customers on enterprise agreements.

Give your teams the freedom to build while maintaining financial discipline

As a leader, your goal isn't to restrict the potential value of AI – it's to remove the financial and operational risk that you face without managed AI costs. You should be able to give engineering, marketing, and operational teams the freedom to innovate with agents, but you should also have the visibility to trust what those agents are doing and the safety nets to protect your budget.

To bridge this gap, we've built robust, native governance tooling directly into the Google Cloud Billing Console around three simple goals:

1. Plan before you scale: The Google Cloud Pricing Calculator lets you estimate anticipated costs in Gemini Enterprise across per-user licenses, developer tools, and background agent runtimes. It gives you the numbers you need to build clear business cases upfront before project work begins.

2. Enforce boundaries without micromanaging spend: Instead of spending time tracking daily usage variations across project teams, let these tools do the monitoring for you:

Early anomaly detection: If a project’s AI spending trends higher than normal, the system flags the deviation with root cause analysis and pinpoints the top 3 SKUs driving the increase so you can see exactly what changed.

Billing Console showing an Early Anomaly alert with the Root Cause Analysis (RCA) breakdown highlighting the driving SKUs

- Project-level spend caps: When a project needs defined financial boundaries, you can set a firm monthly spend limit directly in the Google Cloud Billing Console. If a project hits its limit, the agent's API calls temporarily pause – protecting your budget without affecting the rest of your production infrastructure. Automated email alerts at 50%, 80% and 100% of the budget keep you informed of your progress against the spend limit.

- Overage controls: If a spend cap triggers, you can choose to resume work with a single click in the console. Alternatively, if your priority is continuous operation, you can turn on overages so excess usage smoothly transitions to consumption rates, which can draw directly against your FSP to keep overage unit costs heavily discounted.

Enabling overage pay-as-you-go for a project.

3. Get visibility into business value: Use centralized billing reports paired with the FinOps agent to generate natural-language cost insight summaries of where your budget went, making it simple to show ROI to leadership.

AI spending reporting in Google Cloud Console

Go deeper with AI cost optimization

To build a full-stack FinOps strategy that optimizes the cost, latency, and performance of your models and infrastructure, explore our detailed architecture specifications and frameworks:

How to outsmart infrastructure constraints with dynamic capacity management: Discover how to optimize your compute investments with capabilities in Google Kubernetes Engine and Google Compute Engine that automatically schedule and reallocate resources to avoid interruptions, over-provisioning, and over-reliance on any one hardware configuration.
Expanding Google Antigravity for Enterprise Customers: Read our developer tooling deep-dive to see how technical teams are accelerating software delivery with agent-first workflows.
What sports cars can teach us about optimizing AI spend: More tokens doesn't always mean better AI. Read our conversation with Mike Clark, Director of Product Management for Gemini Enterprise Agent Platform, on how to balance horsepower with efficiency and get the highest return out of every dollar you spend on AI.
Protection during usage spikes: Your heavy workloads can surge during peak hours without forcing you to pay for expensive, dedicated infrastructure that sits idle the rest of the time. As your AI usage grows, Gemini models can automatically scale on demand without hitting artificial rate limits – processing up to 50 million tokens per minute. Read more about Provisioned Throughput.

Your chance to start building AI agents from the absolute basics

Wed, 26 Aug 2026 09:07:00 +0000

Have you been hearing a lot about "AI agents" lately but aren't sure how to actually start building them? You don't need a background in machine learning or years of software experience to get started. The best way to learn is by doing, which is why we built Agent Valley.

Agent Valley is a free, 5-week live learning series designed to take you from scratch to building your very own hands-on agent systems. And instead of staring at boring terminal lines, you’ll be building and playing inside a tiny, low-poly virtual world!

Meet your instructor

You’ll be learning directly from Annie Wang, one of our top Google DevRel Engineers. She designed this course from the ground up to be fully hands-on, interactive, and beginner-friendly. If you want to learn how AI systems are built by the people actually designing them at Google, this is your chance.

How we'll learn together

You’ll learn by building in a split-screen workspace on your laptop. On Day 1, you'll describe and summon a custom low-poly companion that serves as your play character and save file. As you guide your companion through the valley's five districts, a live Runtime Inspector sits right beside the game, showing you exactly what the AI is thinking, deciding, and costing in real-time. Setup is completely zero-stress. Google will provide the environment for running these exercises, so you can dive straight into building.

Agent 101 Live with 5 modular sessions (Jump in anytime!)

Week 1: The Summoning Grove (CONTROL) · Get started by summoning your companion and learning how to keep its memory and traits consistent across a conversation.
Week 2: The Buildyard (DECOMPOSE) · Learn how to break a big project down so multiple AI assistants can work together in parallel without stepping on each other's toes.
Week 3: Market Street (COORDINATE) · Open up a virtual shop! You'll learn how to write reliable code so transactions and returns go smoothly without crashing.
Week 4: The Archive (REMEMBER) · Give your companion a memory. Learn how to help your agent remember past details without getting confused or making things up.
Week 5: The Night Market (LIVE) · The grand finale. Learn how to make your agent react live to events in the world (like fireworks or stage lights) while keeping the system fast and affordable.

Join the livestream

5 Tue starting Sep 1 · 10:00 AM (Pacific Time)
Anyone new to AI agents who wants to learn by coding and playing.
RSVP Here: goo.gle/agent101

Bringing gVisor sandboxes to distributed Ray clusters

Tue, 25 Aug 2026 16:00:00 +0000

The reinforcement learning (RL) ecosystem is rapidly adopting Ray as the unified compute runtime for complex post-training workflows. Across Google Cloud, we see customers using Ray for workloads ranging from multimodal data pipelines to frontier RL. But as agentic and reasoning models evolve, a critical bottleneck has emerged: orchestrating secure, isolated sandboxes at scale to safely execute dynamic rollouts, code generation, and multi-turn tool interactions. Today, in partnership with Anyscale, we are excited to introduce an experimental library for Ray that leverages agentic AI technologies being developed at Google to bring native, high-performance sandboxing directly into distributed Ray clusters.

Sandboxes as Ray Primitives

Ray has become a common runtime for orchestrating post-training workloads. Frameworks including veRL, NeMo-RL, SLIME, MILES, and SkyRL already use Ray to coordinate distributed trainers, inference engines, rollout workers, and other components.

When we designed Ray Sandboxing, an important goal was to make it fit naturally into the existing Ray programming model rather than introduce a separate abstraction for isolated execution. A sandbox has many of the same properties as other resources managed by Ray: it needs to be placed on a machine, assigned resources, created and destroyed, recovered from failures, and scaled with the surrounding workload. This led us to represent each high-level sandbox through a Ray Actor:

The Ray scheduler decides which node should run a sandbox and reserves the corresponding CPU and memory resources. The sandbox Actor manages its lifecycle, while gVisor provides the isolated execution environment on that node.

Starting in Ray 2.58, framework authors and researchers can manage sandboxed environments using the same Ray APIs and patterns they already use for the rest of their workload. For example:

code_block: <ListValue: [StructValue([('code', 'import ray\r\nfrom ray.experimental import sandbox\r\n\r\nray.init()\r\n# Create a gVisor sandbox environment and return an actor handle for a proxy actor\r\nsb = sandbox.create(\r\n cpu=1.0,\r\n memory="512Mi",\r\n image="python:3.12-slim"\r\n)\r\n# Execute code inside the sandbox\r\nresult = ray.get(sb.exec.remote("python -c \'import sys; print(sys.version)\'"))\r\nprint(result.stdout)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dd4f8710>)])]>

This creates a gVisor sandbox from an OCI-compatible image and returns a Ray Actor handle. Calls to exec are normal Ray Actor calls, so the sandbox can live anywhere in the cluster. The created actor is a proxy that will forward the operations to gVisor.

The sandbox API covers the basic lifecycle needed by agentic workloads:

Create environments from OCI container images
Set CPU and memory limits
Configure environment variables, working directories, and networking
Execute commands
Read, write, upload, and download files
Inspect sandbox state
Terminate or delete environments.

For lower-level use cases, SandboxRuntime provides direct access to local gVisor sandboxes and lets users modify the OCI specification before it is handed to gVisor. Here is an example how this API can be used to build a pool of local sandboxes inside of an actor:

code_block: <ListValue: [StructValue([('code', 'import ray\r\nfrom ray.experimental.sandbox.runtime import SandboxRuntime\r\n\r\n@ray.remote\r\nclass SandboxPool:\r\n def __init__(self, size: int = 3, image: str = "python:3.10-slim"):\r\n self.runtime = SandboxRuntime()\r\n self.sandboxes = [\r\n self.runtime.create(image=image, memory="512Mi")\r\n for _ in range(size)\r\n ]\r\n\r\n def run_command(self, index: int, command: str):\r\n return self.runtime.exec(self.sandboxes[index], command)\r\n\r\n def close(self):\r\n for sb_id in self.sandboxes:\r\n self.runtime.delete(sb_id)\r\n\r\n# Deploy an actor managing a pool of local sandboxes\r\npool = SandboxPool.remote(size=3)\r\nresult = ray.get(pool.run_command.remote(0, "python3 -c \'print(\\"Hello from pool!\\")\'"))\r\nprint(result.stdout)\r\nray.get(pool.close.remote())'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dd87d390>)])]>

Why gVisor?

Running model-generated code means treating the code inside the environment as untrusted. Ray Sandboxing uses gVisor, Google's open-source application kernel, as its initial sandbox runtime. gVisor implements a substantial portion of the Linux system-call interface in userspace, putting an additional isolation boundary between workloads and the host kernel. It is OCI-compatible, works with standard container images, and does not require exposing a Docker daemon or host Docker socket to the sandbox.

This combination is particularly useful for agentic workloads: environments remain lightweight enough to create dynamically while providing stronger isolation than executing generated code directly in ordinary containers. gVisor also provides sub-second sandbox startup and low per-sandbox memory overhead, making it possible to use sandboxes as relatively fine-grained distributed resources.

In future versions of Ray, we plan to extend support to other sandboxing runtimes such as Agent Substrate or Kata Containers.

Try Ray sandboxing on GKE

Check out the Ray documentation to learn more about Ray Sandboxes. To try out these sandboxing capabilities on GKE, head over to the Ray sandboxing User Guide. Have feedback or ideas? Join the discussion on the GitHub issue to collaborate on the future of Ray for reinforcement learning.

Now introducing Gemini Enterprise for Legal

Tue, 25 Aug 2026 12:00:00 +0000

Few professions are as exacting as the practice of law. A team reviewing a contract or building a case works inside strictly privileged information, firm-specific playbooks, and a body of law that changes constantly. The work thrives on nuanced, professional judgment — and the systems supporting it inherit real obligations: ethical walls that cannot be crossed, matter permissions that cannot be flattened, and a duty of confidentiality that does not bend for convenience.

General-purpose AI, however capable, does not meet that standard on its own. Foundational model intelligence is necessary. For legal work, it is nowhere near sufficient.

What makes the difference is the system built around the model: skills that enhance a firm's own expertise, connections into the systems where matters actually live, agents that complete work rather than return suggestions, and an open ecosystem to extend all of it — with governance running underneath all four. Each is valuable alone. Only in combination do they produce something a firm or a legal department can put into production and actually rely on.

Today we're bringing that to legal practice with Gemini Enterprise for Legal, part of our new suite of purpose-built industry solutions.

Bringing Gemini Enterprise to your legal practice

Four components of Gemini Enterprise for Legal

Developed alongside industry leaders, Gemini Enterprise for Legal provides an integrated, fully governed environment configured for rapid deployment across firms and corporate legal departments:

1. Purpose-built skills for legal work. Skills are reusable packages of instructions and context, designed by domain experts, that teach an agent to run a specialized task while enforcing your firm's playbooks, citation rules, and house style. They cover contract review and redlining, playbook creation, regulatory horizon scanning, legal research, DSAR fulfillment, and more — and they are where a firm's institutional knowledge becomes something the platform can execute rather than something a partner has to re-explain.

2. Connections to trusted systems and data. Secure MCP connectors link agents to the document management systems, case repositories, research services, and industry applications legal teams already rely on — inheriting each platform's existing user permissions and access controls rather than working around them.

3. Agents that act within the data. Skills and connections come together in agents that carry work through: pre-built agents from Google and leading legal software providers deploy out of the box. Specialized agents handle legal and policy research, regulatory screening, and contract drafting — bringing deep legal expertise onto a platform with centralized governance.

4. An open partner ecosystem. Every firm and legal department practices differently. Partnerships with global systems integrators and legal-tech specialists — Accenture, Deloitte, Devoteam, Factor Law, KPMG, Tribe.ai, Valtech, Zazmic, Zencore, and 66degrees — let organizations customize, integrate, and scale across complex enterprise architectures without vendor lock-in.

Running underneath: a governed control plane. A single dashboard for legal IT and risk teams that natively enforces security policies (VPC, CMEK), maintains private data isolation, and holds every output to verifiable grounding with traceable citations.

Unlocking high-value workflows with domain-specific skills

Gemini Enterprise for Legal shifts AI from passive querying to agentic execution, automating high-volume, precision-critical workflows such as:

Proactive regulatory horizon scanning: Keeps legal and compliance teams ahead of global mandates by autonomously tracking legislative updates, court dockets, and supervisory bodies. It cross-references emerging changes against enterprise policies to flag exposure gaps and generate updated policy drafts for immediate practitioner review.
Automating data discovery and DSAR response: Modernizes privacy workflows by compiling personal data across fragmented enterprise systems in seconds. It eliminates the manual toil of Data Subject Access Requests (DSARs), and allows for adherence to regulatory timelines while minimizing operational risk.
Accelerating contract review and negotiation: Compresses turnaround times for inbound vendor agreements, NDAs, and complex M&A documentation by benchmarking terms against enterprise playbooks. It surfaces high-risk clauses and potential exposure, enabling attorneys to focus on strategic negotiation and high-value judgment.
Building and updating contracting playbooks: Transforms legacy agreement archives into dynamic, actionable playbooks instantly. It automatically extracts key terms, fallback positions, and institutional knowledge to maintain portfolio-wide term consistency and lower negotiation variance across the enterprise.
Redacting documents for motions to seal: Eliminates the manual burden of preparing court filings and redacting legal documents. It intelligently identifies sensitive terms and PII for rapid practitioner confirmation, dramatically accelerating filing timelines while safeguarding confidentiality.
Drafting NDA documents: Elevates contract creation through structural fidelity validation that enforces firm standards and logical document hierarchies. It allows legal teams to rapidly generate and evolve non-disclosure agreements with complete formatting confidence and minimal review overhead.

Open ecosystem of connectors across the legal technology stack

Legal work is only as good as its sources, and legal data carries permissions that have to travel with it. Gemini Enterprise for Legal connects directly to core legal systems via secure MCP connectors. Crucially, access is bound by existing role-based access controls, document-level permissions, and trusted data controls inherited from document management and ediscovery systems.

Productivity and collaboration:

Google Workspace: Connects seamlessly with Google Docs, Gmail, Drive, and Sheets to analyze matter communications, correspondence, and surface internal files while enforcing enterprise access controls.
Microsoft 365: Integrates directly with Word, Outlook, and SharePoint to triage inquiries, redlines, and securely ground work product across emails and matter folders without breaking workflow context.

Document management:

iManage: Gives Gemini Enterprise for Legal permission-bound, auditable access to governed iManage content, including matter history, documents, and institutional knowledge, eliminating the need for bulk exports or custom integrations.
NetDocuments: Enables Gemini Enterprise to search and analyze an organization's knowledge and expertise while preserving each user's existing permissions and ethical walls. Source documents never leave the governed NetDocuments environment.

Contract lifecycle and execution:

Docusign: Integrates agreement metadata, active approval workflows, and contract repositories to surface obligations, track renewal dates, and streamline drafting-to-execution lifecycles.

E-discovery and litigation intelligence:

Everlaw: Connects Gemini Enterprise to litigation and investigations evidence in Everlaw, allowing legal teams to search and analyze their data, uncover case insights, and build timelines directly in Gemini Enterprise, with responses grounded in the underlying documents and access governed by each user’s existing Everlaw permissions.
RelativityOne: Allows legal teams to stand up workspaces, organize case data, and manage operations within a secure perimeter.

Primary law, research, and public dockets:

Thomson Reuters HighQ: Connects Gemini Enterprise for Legal with HighQ, helping legal teams securely access and reference relevant HighQ content within their workflows.
Free Law Project’s CourtListener.com: Provides access to millions of federal and state court opinions, PACER dockets, judicial profiles, and oral arguments.
Courtroom5: Delivers jurisdiction-aware civil litigation datasets, procedural rules, and deadline calculation logic.

Specialized legal AI and intellectual property:

Harvey: Bridges Harvey’s legal reasoning intelligence into Gemini Enterprise, supporting complex legal reasoning and research across Vault projects.
Solve Intelligence: Links Gemini Enterprise to worldwide patent and non-patent literature, SEP technical standards, and prior art databases for patent drafting and claim charting.
Legora: Agentic operating system for legal work, supporting lawyers in research, review, and drafting across complex matters

Third-party agents and implementation partners

Every firm and legal department practices differently. Through our open platform, organizations can deploy pre-built partner agents or collaborate with systems integrators to scale custom capabilities:

Deloitte: Contract Summarize Pro Agent that synthesizes complex contracts into clear summaries for rapid insight and informed decision-making. Clause Guard contract redlining agent to accelerate turnaround times, and minimize risk in contract management.
Eudia Knowledge agent: Accelerates high-stakes legal and contracting work by combining institutional intelligence with a suite of agents that execute deep legal research, high-volume document analysis, and regulatory compliance screening.
Global systems integrators & tech partners: Strategic partnerships with Accenture, Deloitte, Devoteam, Factor Law, KPMG, Tribe.ai, Valtech, Zazmic, Zencore, and 66degrees ensure legal teams can customize, integrate, and scale these capabilities across complex enterprise architectures without vendor lock-in.

Gemini Enterprise for Legal offers leading firms a way to manage modern legal work with a secure agentic platform

Developed alongside leading global law firms

We are working closely with leading law firms, including Cleary Gottlieb, Freshfields, Weil, and Williams & Connolly, to ensure these capabilities address the realities of sophisticated legal practice.

“Cleary is committed to embedding AI into our workflows in strategic and competitive ways. Using Google’s Gemini Enterprise, which can slot in seamlessly with other daily work tools, we can unlock greater efficiencies for our teams and help them deliver even higher quality work for our clients.” — Jeff Karpf, Managing Partner, Cleary Gottlieb.

“The legal sector is entering a period of accelerated change and transformation. For Freshfields the opportunity lies in how effectively we combine frontier technology like Gemini Enterprise for Legal with our expertise, robust governance and institutional knowledge to create value for our clients. Our strategic, multi-year partnership with Google Cloud is helping us accelerate that work and enhance how we deliver legal services.” — Alan Mason, Global Managing Partner, Freshfields.

“We’re thrilled to partner with Google Cloud in the early adoption of Gemini Enterprise for Legal. We look forward to integrating Google’s technology to streamline workflow and further support our litigators in shaping outcomes critical to our clients’ futures.” — Joe Petrosinelli, Chairman, Williams & Connolly.

"Our collaboration with Google gives us early access to emerging capabilities while allowing us to help shape the platform based on the realities of sophisticated legal practice. The result is technology that helps us continue to deliver the innovative, high-quality service our clients expect from Weil. We are looking forward to working with Google Cloud engineers and the Gemini Enterprise product team as we further innovate and evolve our AI capabilities." — Ramona Nee, Incoming Executive Partner, Weil.

Weil scales judicial insights with Benchmark, built on Gemini Enterprise

Built on an enterprise-grade foundation

Confidentiality is not a feature of legal technology; it is the precondition for using any at all. Because Gemini Enterprise for Legal runs on Google Cloud infrastructure and the Gemini Enterprise platform, the permissions and access controls your firm already maintains are the boundaries the platform operates within — not settings it asks you to reconstruct.

Client data, firm-specific playbooks, intellectual property, custom agents, and model outputs stay private to your organization, and are never used to train or fine-tune Google's foundation models. Because we operate the full stack, from infrastructure and models through the application layer, we can tune performance and cost together — so expanding what your teams can take on does not mean expanding spend at the same rate.

This is just the beginning

The launch of Gemini Enterprise for Legal represents another defining step in delivering on the promise of Gemini Enterprise: bringing the best of Google AI to every professional, for every workflow, natively tailored to the way they work.

Gemini Enterprise for Legal is available in preview today, launching alongside our new purpose-built solution for Financial Services. We invite law firms and legal teams to explore how Gemini Enterprise can transform their most critical workflows, with solutions for Healthcare, Life Sciences, and other Professional Services on the horizon.

Now introducing Gemini Enterprise for Financial Services

Tue, 25 Aug 2026 12:00:00 +0000

Protecting capital in today's markets requires immense speed and precision. A financial analyst preparing a deal memo works across licensed market data, internal models, and confidential client files. General-purpose AI lacks the real-time accuracy, verifiable data lineage, and strict security that financial institutions demand. While model intelligence is necessary, without deep integration into trusted financial systems, it is not sufficient.

Making AI genuinely useful inside an industry requires four things, together: domain expertise encoded into reusable skills, secure connections to the systems and data the work depends on, agents that can act inside real workflows, and an open ecosystem that extends and scales all of it — with governance running underneath all four. Each is valuable alone. Only together do they produce something an institution can actually put into production and see true return on investment.

Today, we are delivering on this vision with Gemini Enterprise for Financial Services, bringing Google’s agentic AI directly into the workflows of capital markets and corporate banking.

Bringing Gemini Enterprise into the workflows of capital markets and corporate banking

Four components, built for financial work

Gemini Enterprise for Financial Services delivers an integrated, secure environment configured for rapid deployment with four core components:

1. Purpose-built financial skills. Skills are reusable packages of instructions and context that teach an agent to run a specialized task the way your institution runs it — applying custom formatting to a report, pulling a specific data cut, following a defined research methodology. They are available inside the Financial Research agent and to any agent your teams build.

2. Secure Model Context Protocol (MCP) connectors. Direct integrations, using MCP, into essential financial platforms and licensed data sources, configured inside your own environment. Access stays bound by the entitlements you already maintain — licensed data stays licensed, and permissioned data stays permissioned.

3. Agents that act. At its core is the Financial Research agent which is a Google-built, Google-managed agent that runs end-to-end research with full explainability. It ships with more than 50 foundational skills and exposes its reasoning through confidence scores, explicit methodologies, data snapshots for auditing, and precise source citations. Analysts can use it directly in the Gemini Enterprise app or wire it into existing agent workflows through Agent-to-Agent (A2A) APIs, and it connects to enterprise data sources over MCP to produce reports and documents in the formats your teams already use. Alongside it, out-of-the-box partner agents cover other workflows and extend the capabilities further.

4. An open partner ecosystem. Scale with global systems integrators and specialized fintech providers including 66degrees, Accenture, Artefact, Capgemini, Cognizant, Deloitte, Genpact, GFT Technologies, Infosys, KPMG, PwC, Quantiphi, Slalom, Tribe AI, and Zencore to customize and integrate the platform into your own architecture, without vendor lock-in.

Running underneath: a governed control plane. A single dashboard for IT and risk teams that natively enforces security policies (VPC, CMEK), maintains private data isolation, and holds every output to verifiable grounding with traceable citations.

Unlocking high-value workflows with domain-specific skills

Whether used by private equity specialists, wealth managers, or compliance teams, the solution adapts to diverse workflows like credit risk assessment, portfolio monitoring, market news synthesis, and investigative financial research:

Elevate advisor insights: Equips relationship managers and advisors with AI-generated insights, personalized recommendations, and tailored artifacts, enabling higher-quality conversations and fostering loyalty.
Deepen Know Your Customer (KYC) research and analysis: Modernizes onboarding and Know Your Customer (KYC) workflows across private banking and prime brokerage by using multi-format ingestion (PDFs, Excel, SEC filings) to map complex corporate hierarchies, evaluate risk personas, and resolve ultimate beneficial owners (UBOs).
Enhance portfolio resilience: Helps trading desks deal with sudden macroeconomic shocks. It reduces complex bond portfolio risk exposure analysis to a sub-5-minute execution, complete with automated duration-hedging strategy suggestions.
Uncover credit market opportunities: Transforms credit data into actionable trade ideas by identifying and isolating potential mispricings. This enables teams to expand trading volumes while lowering back-office risk and underwriting latency.
Accelerate bond issuance: Compresses client pitch presentation timelines from days to minutes so that fixed-income and underwriting teams can proactively target prospects, increase deal capacity, and secure a crucial first-mover advantage to help win more business.

Open ecosystem of connectors across the financial technology stack

Gemini Enterprise connects directly to core financial systems via secure MCP connectors. Access is bound by existing role-based controls, ensuring verifiable grounding and precise source citations:

Productivity and collaboration:

Google Workspace: Enables seamless analysis and live artifact generation across Docs, Sheets, and Slides while adhering to enterprise DLP policies.
Microsoft 365: Integrates directly with Excel, Word, and PowerPoint to populate financial models, research memos, and client pitch decks.

Market data and financial fundamentals:

Daloopa: Provides the structured, source-linked financial data layer that enables finance professionals and AI tools to produce accurate and auditable results.
FactSet: Enables secure, authorized access to FactSet's multi-asset class financial and non-financial datasets, powering reliable AI-driven workflows with fully auditable, compliant insights.
Finnhub: Provides real-time financial APIs, global fundamentals, and earnings call transcripts for in-depth financial research.
Fiscal.ai: Delivers institutional-grade financial data within minutes of earnings, covering financials, news, ownership, segments & KPIs, filings, and earnings call transcripts.
Guidepoint: Connects to primary research insights and expert network transcripts to inform and validate investment theses.
LSEG: Provides access to a broad range of trusted financial data, analytical models, indices and news. Enabling customers turnkey access to trusted financial intelligence across every stage of the investment lifecycle.
S&P Global: Integrates cited, verifiable S&P Global data for a range of workflows, from financial analysis, to peer benchmarking, industry research, and more.

Risk, ratings, and private markets:

Moody’s: Brings ratings, default risk models, and real time news fused into one lens for counterparty risk assessment.
MSCI: Connects to proprietary indexes, data and models spanning public and private assets and also provides risk analytics and factor exposures.
PitchBook: Provides comprehensive data and research on private equity, venture capital, credit, M&A, and public markets, including, company financials, deal terms, valuations, and fund performance.

Regulatory and corporate records:

SEC Edgar: Delivers instant, verifiable retrieval of statutory filings, 10-Ks, 10-Qs, and 8-Ks with precise citation mapping.
Dun & Bradstreet: Accelerates commercial onboarding and KYB verification through direct access to global corporate hierarchy records.

Digital assets and indices:

CoinDesk Data and Indices: Supplies institutional-grade digital asset pricing, benchmark indices, and crypto market intelligence for multi-asset strategies.

Introducing Gemini Enterprise for Financial Services

Third-party agents and implementation partners

Organizations can deploy out-of-the-box partner agents or collaborate with global systems integrators to scale custom capabilities without vendor lock-in:

D&B Business Verification agent: Accelerates commercial onboarding and strengthens KYC compliance.
FlowX agents: Automate loan pack completeness check, document reconciliation and many other mission critical processes for financial institutions.
Obin Financial agent: Helps asset management, commercial lending, and insurance teams accelerate complex financial analyses.

S&P Global agents: Data Retrieval Agent for multi-step analysis, report generation, research workflows, and the Horizons Agents that help turn complex energy and sustainability data into fast insights for finance workflows.
Global systems integrators and tech partners: Strategic partnerships connect firms with specialist FinTech and leading global systems integrators, including 66degrees, Accenture, Artefact, Capgemini, Cognizant, Deloitte, Genpact, GFT Technologies, Infosys, KPMG, PwC, Quantiphi, Slalom, Tribe AI, and Zencore to manage custom configurations and deploy specialized capabilities at a global scale.

Developed alongside leading global financial institutions

We are developing these capabilities in close collaboration with financial institutions, including Deutsche Bank and CME Group, to ensure they reflect the operational realities of the industry.

“As a design partner for the Financial Research agent, Deutsche Bank has helped shape this capability in view of the realities of a highly regulated industry – from data protection and governance to the workflows our teams use every day,” said Marie-Jeanne Deverdun, Chief Technology, Data and Innovation Officer, and Member of the Deutsche Bank Management Board. “Starting in the Corporate Bank, we see significant potential to reduce manual research effort, improve the consistency and auditability of outputs, and give our teams more time for client conversations. This is an important step in applying AI where it can make a practical difference: safely, responsibly and at scale.”

This launch builds on the rapidly growing momentum of Gemini Enterprise, with many leading financial institutions like BNY, Citi Wealth, Lloyds Banking Group, Macquarie Bank, and Signal Iduna using it to equip their workforce with advanced, agentic workflow tools to drive growth and efficiency.

Built on an enterprise-grade foundation

Because Gemini Enterprise for Financial Services runs on Google Cloud infrastructure and the Gemini Enterprise platform, organizations get the security, governance, compliance, and cost-management capabilities they expect from an enterprise platform.

Customer data, business rules, intellectual property, custom agents, and model outputs remain private to their organization. Your data is never used to train or fine-tune Google’s foundation models. Furthermore, our full-stack approach - from infrastructure and models to the application layer - allows us to optimize performance and cost, helping organizations maximize the value of their AI investments.

This is just the beginning

Gemini Enterprise for Financial Services is available in preview today, launching alongside our new purpose-built solution for Legal. We invite enterprise leaders in financial institutions to explore how Gemini Enterprise can transform their most critical workflows, with solutions for Healthcare, Life Sciences, and other Professional Services on the horizon.

Empowering autonomous agents with advanced security governance

Mon, 24 Aug 2026 16:00:00 +0000

AI agents are the ultimate insiders. We grant them permission to read emails, query databases, and trigger API calls. They don’t just retrieve information, they take action.

Agents offer incredible potential for increased productivity and better customer experiences, but they also come with new security concerns. In our new State of AI infrastructure report, 79% of tech leaders cite security, governance, or operations as their most significant challenge to scaling inference.

While there’s still a crucial role for traditional security tools, the threat model has fundamentally changed. Autonomous workflows have redefined enterprise risk, so it's crucial that we give agents the access they need without compromising security.

The agentic paradox

The path to success starts with viewing governance as a driver for innovation. To be useful and secure, an agent needs access — and also guardrails. Yet 35% of senior IT decision makers cite insufficient security for multi-system access as a primary issue preventing agentic deployment.

Agents expand the surface area that defenders need to protect, and can introduce new threats, including tool poisoning and indirect prompt injection, where an attacker can hijack an agent’s logic through the data it processes. Managing the dynamic permissions that agents need to succeed at their tasks can also be a significant challenge, particularly as legacy security wasn’t designed for today’s automated threats.

Securing the chain of thought

Along with securing more identity and access issues, it’s important for defenders to secure both the network layer and the model.

Security leaders are increasingly shifting their focus from preventing breaches to verifying provenance to guard against misuse, including indirect prompt injection.

From an infrastructure perspective, what are your top security concerns related to AI?

From blocking to managing

We’ve looked at the new security challenges posed by agentic AI. You can’t solve them by simply locking down the system, as that defeats the purpose of autonomous agents.

Many organizations are turning to integrated, full-stack cloud platforms to give them greater oversight. 69% of surveyed executives now rate a full-stack platform as a critical requirement, and 80% say data compliance is the primary factor dictating that choice.

By adopting frameworks like the Secure AI Framework (SAIF) and moving to a central control plane, purpose-built platforms such as Gemini Enterprise Agent Platform, organizations can manage risk in three main areas:

Secure-by-default design: Embedding security directly into the AI development process to proactively guard against threats including prompt injection.
Agent governance and oversight: Adopting purpose-built permission and identity management for agents — giving greater control over agent interactions, exposing blind spots and limiting risks tools.
Human-in-the-loop control: Enforcing clear rules that automatically flag when an agent requires human approval before moving forward with a critical action.

Governance will guide you to success

The true value of a modern security foundation is its ability to encourage innovation. By embedding robust governance directly into a unified foundation, organizations can deploy agents with confidence across their most sensitive, business-critical workloads.

The leaders of the agentic era are re-architecting their stack to use security as a launchpad — empowering them to innovate securely and scale faster than their competition.

Find out more about how enterprise leaders are rethinking security for the agentic era in the State of AI infrastructure report.

New AI-powered quick assessments in Migration Center turbocharge modernization

Mon, 24 Aug 2026 16:00:00 +0000

Technology leaders are under mounting pressure to modernize infrastructure, control multi-cloud operational spend, and build data foundations for generative AI. However, the discovery required for that level of transformation can entail weeks of manual spreadsheet analysis, mapping in-house infrastructure, and reconciling siloed, piecemeal cost estimates across disparate teams and sources. To help, we’re announcing AI-powered Quick Assessments in Migration Center, which delivers near-instant total cost of ownership (TCO) modeling and automated service mapping.

Compare this to legacy assessment processes, which can stall digital transformation initiatives before they even launch. Manual discovery can delay migration timelines by months, increase engineering overhead, and often miscalculates complex financial models. By replacing manual discovery with AI-assisted automation, IT gains instant, actionable visibility into the TCO and return on investment (ROI) for a given migration initiative.

Now, organizations can generate comprehensive migration financial models in minutes rather than months. Teams ingest raw infrastructure data or cloud billing reports and quickly receive an optimized target bill of materials (BOM), service mapping coverage, and projected savings. Decision makers interact with an agentic assistant that explains underlying financial assumptions, recommends technical cost optimizations, and exports ready-to-share executive reports.

Inside the AI-powered Migration Center

This is made possible with AI-assisted Quick Assessments alongside enhanced Cloud Billing assessment capabilities, both integrated into the new AI-powered Migration Center.

AI-assisted Quick Assessment for on-premises workloads

Designed for enterprise customers and partners, AI-assisted Quick Assessment automates on-premises infrastructure evaluation to provide rapid financial modeling. Let’s walk through these new capabilities:

Instant Compute Engine TCO estimates convert VMware inventory exports (such as RVTools) or aggregated infrastructure inputs into precise Compute Engine cost targets:

Migration Center’s Quick TCO Estimator

Migration Center’s Quick TCO Estimator results page

Advanced architecture modeling supports latest-generation Gen4 compute instances and high-performance Hyperdisk storage pools:

Migration Center’s Quick TCO Estimator detailed results page

Customizable financial controls allow teams to adjust on-premises baseline cost assumptions to match internal accounting standards:

Migration Center’s Quick TCO Estimator detailed results page (continued)

Context-aware agentic chat recommends tailored technical cost optimizations aligned with your specific business constraints (such as regional location or compliance needs), clearly explaining the underlying logic and financial assumptions.

Migration Center’s agentic chat capabilities

Migration Center’s agentic chat capabilities (continued)

Automated Business Case and Google Sheets export generates ready-to-use reports capturing the complete recommended BOM, TCO comparison, and ROI analysis:

Migration Center’s business case

The path forward

Modernizing your infrastructure starts with fast and accurate data. Migration Center’s Gemini-powered features simplify cloud evaluation, empowering IT decision makers to build defensible business cases generated by machine-learning.

Try Migration Center directly in the console today, or take a free migration and modernization assessment to evaluate your workloads and accelerate your strategic cloud journey with Google Cloud.

Cloud CISO Perspectives: Sticking to security fundamentals in the AI era

Fri, 21 Aug 2026 16:00:00 +0000

Welcome to the first Cloud CISO Perspectives for August 2026. Today, Chris Betz explains why the AI era makes it more important than ever to lean into security fundamentals.

As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and you’d like to receive the email version, you can subscribe here.

aside_block: <ListValue: [StructValue([('title', 'Get vital board insights with Google Cloud'), ('body', <wagtail.rich_text.RichText object at 0x7ff5dd466c10>), ('btn_text', 'Visit the hub'), ('href', 'https://cloud.google.com/solutions/security/board-of-directors?utm_source=cgc-site&utm_medium=et&utm_campaign=FY26-Q2-GLOBAL-GCP39634-email-dl-dgcsm-CISOP-NL-177159&utm_content=-&utm_term=-'), ('image', <GAEImage: GCAT-replacement-logo-A>)])]>

How to stay strong with security fundamentals in the AI era

By Chris Betz, CISO, Google Cloud

Chris Betz, CISO, Google Cloud

As AI accelerates the capabilities of adversaries, foundational strength becomes the primary differentiator between resilience and vulnerability. It’s a dangerous and unfortunately common misconception that traditional security fundamentals are becoming obsolete. For CISOs, the challenge is to adopt new AI technology securely while scaling essential, effective defensive practices to move at the speed of the adversary.

For both attackers and defenders, AI has been a catalyst for optimization and innovation. While traditional automation has allowed us to perform repetitive tasks at scale, AI enables both sides to execute highly-specific, customized actions at massive scale and unprecedented speed.

Collectively, these technologies reduce the attack surface and contribute to the deep context that defensive AI needs to be a business enabler — and create the necessary conditions for successful AI-powered defenses.

We can see the threat developing almost in real-time. Adversaries are deploying new malware with just-in-time AI that dynamically generates malicious scripts and obfuscates code mid-execution to evade detection. They use sophisticated vishing and deepfakes for identity theft and business email compromise. We even see unauthorized AI tools lead to the rise of shadow agents.

Defending against AI powered security threats requires more than accelerating current security practices; it means stepping back and beginning with the security foundation and layered defenses. It’s critically important to build and use a layered defense with the right guardrails — foundational cybersecurity building blocks that we’ve been investing in for years.

Doubling down on this foundation: technologies like multi-factor authentication (MFA), Zero Trust frameworks, consistent system patching, and comprehensive detection and response. Collectively, these technologies reduce the attack surface and contribute to the deep context that defensive AI needs to be a business enabler — and create the necessary conditions for successful AI-powered defenses.

Revolutionizing vulnerability management

In just a few short years, identifying and fixing vulnerabilities has evolved from a mostly laborious, manual process to one driven by AI tools discovering vulnerabilities at volumes never seen before. Further, the time to exploit window has essentially been eliminated.

However, it’s not enough to merely discover vulnerabilities, especially at today’s volumes. You still need to prioritize fixing those that have the most critical impact on your systems and networks first, and that necessitates an equally-rapid response in smart mitigation.

Organizations use multiple models to scan for flaws and then suggest high-quality code fixes that engineers can quickly move into production, leveraging capabilities like AI Threat Defense. AI allows us to automate the entire software development lifecycle, from discovery to testing and deployment, ensuring that our defensive posture evolves faster than the threats targeting us.

Enhancing threat modeling

We’re also seeing the fundamental concept of threat modeling have an outsized impact. Doing threat modeling well requires bringing context together from your code, your cloud architecture, system design, and network pathways.

While it isn’t easy, using AI can scale our ability to bring that data together into a coherent picture. Teams have been experimenting with multi-AI models to collect system information and enumerate threats.

As I noted in June, engineering teams at Google Cloud now route product launches through an agent-based security review pipeline. High-risk indicators automatically get flagged for human review, while we’ve replaced static threat models with dynamic product dossiers that update in real-time.

The CISO as a strategic business leader

The most effective security leaders that I know today are more than just technologists: They are strategic business leaders. The intense global focus on AI vulnerabilities has brought cybersecurity to the forefront of boardroom and executive attention like never before.

This visibility is an opportunity to lead. We CISOs are expected to communicate with clarity, from the board to the C-suite to the security teams who look to them on a daily basis, demonstrating their ability as capable strategists who can navigate the complexities of AI while safeguarding the organization's growth.

By aligning security fundamentals with business objectives and using AI to enhance defense, we can lead our organizations securely into the future.

To learn more about building and maintaining strong security foundations in the AI era, read our newest Defender’s Advantage: Cyber Snapshot Report.

aside_block: <ListValue: [StructValue([('title', 'Learn something new'), ('body', <wagtail.rich_text.RichText object at 0x7ff5dd6ef0d0>), ('btn_text', 'Watch now'), ('href', 'https://www.youtube.com/watch?v=CmGWIwgHR60'), ('image', <GAEImage: Cloud-CISO-Perspectives-logo-A>)])]>

In case you missed it

Here are the latest updates, products, services, and resources from our security teams so far this month:

Driving AI threat readiness with Wiz: Announcing new Wiz capabilities that can help organizations prepare for the AI era by expanding visibility and accelerating response, so your security teams can defend at machine speed. Read more.
PQC in Plaintext: Google Cloud’s post-quantum cryptography roadmap: We’ve long been actively working on and rolling out post-quantum cryptography in our infrastructure. Here’s our updated Google Cloud roadmap to migrate to PQC by 2029. Read more.
How Google Cloud detects, contains, and protects against emerging threats: Learn more about how Google Cloud empowers you with the tools, governance, and infrastructure you need to securely deploy workloads and maintain long-term trust. Read more.
Privacy-first medical AI with MedPerf and Google Cloud: Discover how Google Cloud and MedPerf use Confidential Computing to enable secure, privacy-first collaborative medical AI evaluation. Read more.
More cryptanalysis makes us all safer: Recent advances in frontier AI models do not signal the downfall of cryptography. Here’s why they’re best viewed as additional cryptanalysts. Read more.
How layered defenses harden Chrome against abusive notifications: Learn how Chrome Security has collaborated with Firebase Cloud Messaging (FCM) and Safe Browsing to significantly reduce notification abuse, and improve the security and quality of the web ecosystem for everyone. Read more.

Please visit the Google Cloud blog for more security stories published this month.

aside_block: <ListValue: [StructValue([('title', 'Join the Google Cloud CISO Community'), ('body', <wagtail.rich_text.RichText object at 0x7ff5dd6eccd0>), ('btn_text', 'Learn more'), ('href', 'https://rsvp.withgoogle.com/events/google-cloud-ciso-community-interest-form-2026?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY25-Q1-global-GCP30328-physicalevent-er-dgcsm-parent-CISO-community-2025&utm_content=cisop_&utm_term=-'), ('image', <GAEImage: GCAT-replacement-logo-A>)])]>

Threat Intelligence news

Staying ahead of adversarial AI through agentic source code review: To help defenders implement agentic approaches similar to our approach at Google Cloud, we are sharing the details of our Agentic Vulnerability Discovery Harness architecture for the first time. AVDH can also be used alongside CodeMender’s ongoing scanning to create a two-layered defense strategy. Read more.
Cloud threat highlights from the first half of 2026: In the first half of 2026, Wiz's Research and CIRT teams tracked threats affecting thousands of cloud environments. We saw a notable increase in the volume of activity, with supply-chain attacks running at a previously unseen scale and developer toolchains and AI infrastructure drawing serious attention. Read more.
Batten down your packages: Mitigation guidance for supply chain compromise: GTIG and Mandiant have tracked ongoing and increasing open source software supply chain compromise campaigns over the past several years. Here are our mitigation and hardening recommendations to secure software supply chains, including insights we have developed as a result of supporting customers. Read more.
Multi-brand vishing extortion targets financial services and enterprise cloud environments: Telemetry and infrastructure analysis reveal that UNC6671 has not disbanded. Instead, the threat group has diversified its operations across multiple extortion fronts including Redact, Pink, Helix, and Falcon and continues to rely on voice phishing to target enterprise employees. Read more.
Keyv and cacheable npm package hijacked in supply chain attack: Wiz Research is actively investigating an ongoing software supply chain attack affecting multiple keyv/cacheable npm packages. Read more.
Inside the Metabase SQLi: Exploited in the wild: Wiz has reverse engineered Metabase CVE-2026-72898 with AI to accelerate defense. Here’s what we learned. Read more.

Please visit the Google Cloud blog for more threat intelligence stories published this month.

Now hear this: Podcasts from Google Cloud

Cloud Security Podcast: All about Project Atlas, Wiz's AI vulnerability research: Near Orfeld, head of vulnerability research, Wiz, discusses how his team uses multi-agent AI systems for discovering high-impact zero-day vulnerabilities in cloud infrastructure. Listen here.
Cloud Security Podcast: How Google eliminates classes of vulnerabilities at scale: How do you build the foundations for a secure Google-scale enterprise that stays secure even if an AI is writing the code and nobody has time to review it? Christoph Kern, principal security engineer, Google, explores what secure-by-design really means in the AI era. Listen here.

To have our Cloud CISO Perspectives post delivered twice a month to your inbox, sign up for our newsletter. We’ll be back in a few weeks with more security-related updates from Google Cloud.

How agents can delegate better

Fri, 21 Aug 2026 16:00:00 +0000

In any organizational behavior class, students will learn that effective delegation is among the most important skills for a seasoned leader. Getting meaningful work done involves careful coordination, starting with a subdivision of projects into manageable tasks, mapped onto the skills of the team, and assigned to the right people.

At Google Cloud, we’re learning a similar lesson when it comes to building and deploying AI agents in enterprise workflows. These workflows are best approached by multi-agent systems that can break apart and execute complex tasks. To do so, AI agents need to become good delegators.

To learn how, we turned to research from Google DeepMind. In their recent study titled Intelligent AI Delegation, they prove how delegation itself involves intelligence: adaptive negotiations, aligning on formal contracts, and security guardrails.

This work opens up new opportunities for customers building AI agents that can communicate, share tasks, and coordinate towards set objectives. Today, we’ll share four principles that emerged from that work, and how you might apply them to your own workflows.

Principle 1: Verify delegated work

If we are to permit AI to delegate tasks, we want it to do more than arbitrarily assign work. Agents should intelligently break down work into tasks that can be reliably verified. In our research, we call this "contract-first decomposition."

Like human delegation, this takes thoughtful deliberation. With people, this might mean a leader understanding their team’s strengths, and perhaps checking their work before it’s completed. For AI, there’s a similar learning curve. The orchestrating AI (the manager that sits atop a multi-agentic system) may consider multiple plans for how best to decompose and assign work, and keep decomposing sub-goals into smaller and smaller chunks until they become sufficiently simple to monitor and verify. Ideally, this should result in a plan where everything can be reliably graded. In reality, however, this may not always be possible to achieve.

Sometimes, it may be necessary to involve subjective assessment of whether work has been completed successfully, in line with expectations. Rather than being a problem, identifying such components helps us determine where human time is best spent, and how best to involve human expert judgement in oversight of agentic systems.

Principle 2: Be smart about cost

The research framework helps us answer a question that keeps coming up with customers: Can this particular task be handled by a smaller, cheaper model? Enterprises are increasingly attentive to cost, and rightly so.

Finding the right balance between performance and budget is tricky. Taking a complex problem, like payroll, and handing it off to a lightweight model, might not be powerful enough for the results you want. On the other hand, it’s unnecessary to route simple tasks, like reformatting a spreadsheet, to a strong reasoning model.

According to the research, an agent that is intelligent about delegation would learn to recognize these scenarios, and match each task to the right tool or endpoint, to achieve the desired result and maximum reliability at a minimum cost. Use of model routing capabilities within API gateways is becoming a popular choice among customers, in addition to the alternative for using client-side proxies (such as LiteLLM).

You can learn more about model routing here.

Principle #3: Respect sensitive data

Many workflows handle private, sensitive data, and AI agents need to respect those boundaries and permissions. For example, if you’re deploying your orchestrator agent for payroll data, you know that agent should never pass along its full set of information to a sub-agent. This not only compromises security, but also bloats the context window for agents and degrades performance. An agent should grant the absolute minimum permissions required to complete that specific assignment, and nothing more.

The challenging part arises when needing to demonstrate, according to our first principle, that work has been reliably completed, without revealing private information. According to the research , advanced cryptography can help address this, via techniques such as zero-knowledge proofs. Zero-knowledge proofs enable one AI agent to prove to the other AI agent that a planned computation was performed correctly, without revealing the data itself. For example, an agent tasked with analyzing a sensitive dataset can generate a succinct non-interactive argument of knowledge that proves a specific property of the result. This enables the delegator to instantly verify the validity of the proof.

Principles #4: Beware the zone of indifference

The zone of indifference is a term coined by Chester Barnard, an American business executive, in his 1938 book called The Function of the Executive. The zone is the space in which an employee will accept a task without questioning it. The task usually falls within their scope, so they unconsciously accept it. For example, if you’re a sales rep and your manager asks you to attend an upcoming pitch with a valued client, you probably wouldn’t push back or think too deeply about it.

As expressed in the research, current AI systems are defined by post-training safety filters and system instructions. As long as a request does not trigger a hard violation, the model complies. But when considering the emerging agentic web, this compliance might actually create a systemic risk. As mentioned in the research, “As delegation chains lengthen (? → ? → ?), a broad zone of indifference allows subtle intent mismatches or context-dependent harms to propagate rapidly downstream, with each agent acting as an unthinking router rather than a responsible actor.”

This has serious implications, because it means intelligent delegation requires “dynamic cognitive friction.” This means validating the information provided to agents to ensure that they are accurate, relevant, controlled and efficient.

This way, an agent can recognize when a request is ambiguous enough to warrant stepping outside their zone of indifference to challenge the delegator, or request human verification. Human participation and oversight similarly presume a degree of cognitive friction and active engagement, though this must be carefully managed, so as not to over-burden the users of the system. Human time is valuable and should only be invoked when necessary.

Looking ahead

At Google Cloud, our long-term goal is to integrate agents naturally and efficiently into organizations, which will mean delegating to and from human experts and respecting boundaries. Together, we believe this will deliver business value beyond what individual agents can handle.

Ready to navigate the agentic web? Read Google DeepMind’s report paper, Intelligent AI Delegation, on arXiv.

_{Note: A special thanks to Matija Franklin, Simon Osindero from Google DeepMind, and Vishal Agarwal, Andrea Morange from Google Cloud, for their contributions.}

Expanding Google Antigravity for enterprise customers

Thu, 20 Aug 2026 17:30:00 +0000

Since announcing Google Antigravity in Gemini Enterprise Agent Platform at I/O in May, we’ve heard helpful feedback from our customers. Your developers want easy access to coding agents across surfaces. Your enterprise governance team wants security controls and license management. And your finance team wants pooled usage so that no prepaid token ever goes unused. Now, everybody finally gets what they want:

Antigravity is available now as part of eligible Gemini Enterprise app subscriptions, including out-of-the-box administrative and spend controls.
New IDE extensions let developers use Antigravity in the IDEs of their choice, including VS Code.

Unify AI developer tools and enterprise-grade controls in one subscription

Equipping your developers with advanced agentic tools shouldn't mean managing separate add-on licenses, invoices, billing consoles or security settings. With AI developer tools included in Gemini Enterprise subscriptions, administrators can easily enable Antigravity and Android Studio for users with eligible Gemini Enterprise Standard, Plus, and Standard Emerging Market licenses, and maintain full governance with spend, security, observability and usage metrics consolidated in the Gemini Enterprise admin console.

Unblock your developers while controlling spend

With billing flexibility and cost management tools in Gemini Enterprise, you can ensure your developers have the resources they need while managing costs:

Granular spend thresholds: Administrators can set monthly project-level budget caps directly in the Billing console, with additional per-user and team controls rolling out later this year.
Pooled quotas: Shared token pools provide flexibility to high-demand teams, preventing purchased quota from sitting idle across the organization.
Overage enablement: To maintain continuous developer workflows when pooled quotas are met, administrators can opt into overages with monthly spend caps, smoothly transitioning excess usage to standard consumption-based rates.
Usage metrics: Centralized usage tracking provides visibility into token consumption, API calls, and developer activity, enabling organizations to continuously optimize their AI investments.

Safeguard your organization’s code and data with built-in privacy and security

Gemini Enterprise subscriptions bring Google Antigravity under Google Cloud’s standard security and compliance protections. Administrators and IT teams can set clear boundaries around workspace access, enable full audit logging, and enforce data privacy from a single console:

Configurable security policies: Enforce security and compliance controls, such as workspace sandboxing, and browser and MCP server access, to help ensure AI agents operate safely within authorized enterprise environments.
Central audit logging: Enable comprehensive audit logging with a single toggle, capturing prompts, agent responses, and metadata for compliance reporting.
Data privacy: Maintain data ownership under Google Cloud’s Terms of Service, ensuring all agent activity executes strictly within your secure cloud boundary.

Bring agentic coding directly into your team’s preferred development environments

Starting today your developers can use Antigravity across the surfaces they already know and use — including Visual Studio Code, Visual Studio (preview), Jetbrains (preview) and Zed IDEs (preview) via the new IDE extensions as well as the Antigravity 2.0 desktop app, and the Antigravity CLI.

Throughout all surfaces, administrators can enforce corporate identity standards while removing setup friction for technical teams via native support for Workforce Identity Federation (WIF) and Application Default Credentials (ADC).

AGY IDE extension demo

What our customers are saying

From rapid code generation to end-to-end task automation, Google Antigravity is giving engineering teams the momentum of cutting edge AI development backed by the stability, governance, and scale of Google Cloud. Here is how leading enterprise customers and partners are driving measurable outcomes in production:

“Deploying Antigravity in Gemini Enterprise allows Accenture to arm our engineers with Google DeepMind’s premier technology on the secure, trusted foundation of Google Cloud. Abstracting away operational complexity ensures our teams don't have to choose between developer speed and enterprise-grade governance — freeing them to deliver high-velocity engineering and transformative value for our clients.” — Chetna Sehgal, Global Practice Lead, Accenture Google Business Group

“At AirAsia and across the Group, we’re all about empowering our people. Bringing highly capable Gemini models directly into our daily workflows with Antigravity 2.0 does exactly that. We are putting the most advanced AI capabilities into the hands of our entire workforce, from software engineering to finance, marketing, legal, HR and much more. This empowers both our developers and critical back-office teams to innovate at an unprecedented pace and drive proven time-savings across the board.” — Nikunj Shanti, CTO, AirAsia Next

“Enterprises are moving beyond AI experimentation and expecting measurable business outcomes. With Gemini Enterprise and next-generation developer tools like Antigravity 2.0 and Antigravity CLI, we see significant opportunities to further embed agentic AI, particularly the advanced reasoning capabilities of Gemini models, directly into software delivery workflows. This goes beyond productivity as it enables faster decision-making, higher code quality, and reduced technical debt at scale. What stands out is how these capabilities are helping our teams evolve from writing code to orchestrating outcomes, strengthening every phase of the software development lifecycle while scaling innovation securely and responsibly.” — Rakesh Aerath, President, Asia Pacific Global Delivery Centers of Excellence, CGI

“Every developer workflow is unique, and agentic AI should adapt to the engineer, not the other way around. With Google Antigravity supported across developers' preferred IDEs, the desktop app, and the CLI, Cognizant can seamlessly embed agentic engineering across our global delivery centers. It gives our teams the freedom to choose their preferred surface while delivering high-velocity, secure software for our clients.”— Rajesh Varrier, President, Global Operations and Chairman & Managing Director, Cognizant India

“Antigravity has played a key role in advancing our AI-first strategy at Datamatics. Over the last few months, our teams have used it to rapidly build and deploy multiple applications, accelerate solution development, and embed AI into core business processes. From AI Impact Hub to analytics and sales enablement solutions, it has helped us move beyond experimentation to real execution, delivering measurable business outcomes while enabling teams to innovate faster and at scale.”— Vijay Venkatachalam, Vice President, Information Systems Group (ISG), Datamatics

"Embedding Google Antigravity's autonomous capabilities directly into Gemini Enterprise development environments allows our internal and Forward Deployed Engineering teams to automate complex tasks. Supported by the platform’s new FinOps and governance controls, our teams can confidently focus on orchestrating high-value, secure and cost-efficient outcomes for our clients at scale." — Faruk Muratovic, US AI & Engineering Strategy and Services Leader, Deloitte

“Adopting Antigravity places Wipro at the leading edge of the AI-driven software development lifecycle. Combining Antigravity 2.0, CLI, and the new IDE extensions with a seamless developer experience and superior code accuracy fits naturally into our AI-first engineering strategy. Working with Google Cloud allows us to accelerate software delivery and bring next-generation value to our global enterprise clients.” — Debashish Ghosh, Vice President and Global Head, Google Partnership, Wipro

Get started with Antigravity in Gemini Enterprise

Google Antigravity in Gemini Enterprise is available today for eligible Gemini Enterprise Standard, Plus, and Standard Emerging Market licenses, with broader support coming soon.

For administrators: Visit the enterprise setup guide to enable AI Developer tools for Google Antigravity and Android Studio.
For developers: Start building with Antigravity 2.0, the Antigravity CLI, or your preferred IDEs via Antigravity IDE extensions.

Google is a Leader in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Thu, 20 Aug 2026 16:00:00 +0000

We are thrilled to announce that Google has been recognized as a Leader for the third year in a row in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms (CNAP). We believe this placement in the Leaders quadrant validates our commitment to providing an accessible, developer-centric platform that accelerates onboarding and supports rapid prototyping across modern workloads.

Our vision for an application-centric cloud focuses on enabling developers to prioritize writing code and building agents or traditional apps by removing infrastructure complexity. Google Cloud provides a unified execution environment supporting serverless, containerized, and agentic deployment options. We believe our placement highlights Google's unique readiness to power both standard enterprise microservices and the next generation of autonomous AI applications.

Some key features and capabilities of our platform are highlighted below.

From idea to implementation

Generative AI has ushered in a wave of vibe coding, allowing anyone to go from an idea to a deployed application in a fraction of the time it used to take. To make this even smoother, Google Cloud integrates its serverless infrastructure with AI vibe-coding and prototyping tools. We also simplify access to Google Cloud resources with tools like managed MCP servers and agentic skills — packaged sets of instructions, scripts, and resources to teach an AI how to complete specialized, multi-step workflow.

One-click prototyping in Google AI Studio: Developers can build and deploy full-stack applications directly within Google AI Studio, making it a great environment for prototyping and experimentation. With a single click, you can instantly package and publish your vibe-coded applications to Cloud Run.
Google-managed MCP servers: To enable AI agents to interact with cloud resources, we support official, fully managed remote MCP servers. An example is the Cloud Run MCP server (via run.googleapis.com/mcp), which allows developers to easily launch endpoints and deploy server-side logic. The MCP tools are deployed with a simple config, skipping cloud builds to launch code in seconds, saving valuable developer time. These fully managed servers are integrated with IAM and VPC Service Controls, and they leverage Model Armor for content security.
Google's official Skills Repository: Level up your agents with additional, condensed expertise on various Google Cloud technologies. Published and available in Agent Registry, the repository includes skills for Cloud Run, the Well-Architected Pillar (security, reliability, and cost optimization), and more.

Ready to try vibe coding yourself? Get hands on with this codelab to build a vibe-coded app and deploy it to Cloud Run.

From implementation to enterprise-ready

Translating prototypes into production-grade, secure, and cost-effective enterprise software is where Google Cloud excels, with a full suite of developer, architect, and platform engineering tools. From designing your application to optimizing day 2 operations, we offer the services and tools to help you build, operate, and deploy applications across their entire lifecycle, and you have the freedom to build with any language, any library, and any framework.

Build

Build with Google Antigravity: At Google, we’re simplifying and expanding our development ecosystem behind the Antigravity harness, collapsing developer silos into a unified orchestration layer. By integrating multi-step AI reasoning directly into the developer workflow, Antigravity natively brings local codebase development to our cloud-native application platforms (e.g., Cloud Run).
Design and deploy with Application Design Center (ADC): Now, you can bridge the gap between developer velocity and enterprise control, using Application Design Center to eliminate manual Terraform and YAML configuration. This platform engineering component helps teams design, standardize, and deploy template-driven applications on Google Cloud. It is also integrated as part of Gemini Cloud Assist design agent and published as an MCP server. With ADC, you can visually design your architecture using Cloud Run services, databases, and event brokers backed by automated Gemini Cloud Assist security templates. Beyond human-guided design, ADC enables programmatic orchestration at the time of no HITL (Human-in-the-Loop), allowing automated pipelines to provision policy-governed Terraform configurations directly and autonomously.

Operate

Intelligent investigations: Integrating Gemini Cloud Assist with native telemetry creates an AI-driven framework for Day-2 incidents. When alerts fire, operators engage Gemini Cloud Assist to instantly synthesize logs and metrics, pinpoint root causes, and generate remediations — context that can be handed off to accelerate support escalations. Crucially, IAM permissions strictly govern all AI recommendations, and help to ensure explicit human-in-the-loop approval are required before any infrastructure changes occur.
Cost analysis and optimizations: Machine learning algorithms learn natural seasonal traffic cycles to detect cost anomalies within minutes, triggering notifications to protect your bottom line without risking destructive infrastructure shutdown.

Deploy

Reliability and high availability: Cloud Run is a regional service by default, but you can deploy an app to multiple regions via a single gcloud command. Integrated with service health, Cloud Run automates cross-region failover and failback. If a service in one region becomes unhealthy, traffic is automatically routed to the next-closest healthy region, failing back once the issue is resolved.
An open platform: As a long-time and top contributor to the Cloud Native Computing Foundation (CNCF), we operate with an open-source-first strategy. By integrating foundational, community-driven technologies, we help enable application portability for enterprise customers who are increasingly demanding multi-cloud flexibility

Ready to start deploying your apps to Google Cloud? Get hands on with these codelabs:

From enterprise-ready to autonomous

AI agents are software’s next frontier. They offer more than just increased productivity and efficiency; they can unlock exponential growth. To provide enterprises with robust agentic deployment options, Google Cloud provides a dedicated infrastructure stack tailored specifically to host, govern, and secure autonomous agent fleets. This stack seamlessly integrates with our Agent Development Kit (ADK) as well as other leading agentic frameworks to give developers maximum flexibility.

Gemini Enterprise Agent Runtime
At the core of this stack is Gemini Enterprise Agent Platform and its dedicated Agent Runtime, which delivers the serverless and containerized deployment options you need for enterprise-scale agent development, including the following capabilities:

Native personalization: Built-in sessions and memory banks manage context and long-term state, preventing costs from ballooning.
Agent observability and tracing: Built on OpenTelemetry (OTel) standards and agentic schemas, turnkey dashboards feature agent topology graphs and interactive trace logs that detail sessions, tool calls, and reasoning paths.
Agent evaluation and simulation: Automated simulation tools allow developers to test agents against golden sets with side-by-side comparisons and simulate thousands of interactions to test edge cases.

Hosting agents on Cloud Run
For customers requiring additional flexibility, granular control, or specific regulatory compliance, Cloud Run serves as an excellent serverless alternative to host your agents. Some of its latest features include:

Cloud Run instances (coming soon): This primitive manages individual, addressable, long-running singleton resources with integrated Cloud Storage volume mounts, allowing persistent background agents to be deployed cost-effectively.
Cloud Run sandboxes: Hard-isolated environments spin up in under 500 milliseconds to safely execute untrusted, model-generated code, protecting the host system from unauthorized access.

Agent security, governance, and auditability
To securely deploy AI agents and prevent unmanaged shadow AI, enterprises need an ironclad governance framework. Google Cloud delivers this through Agent Identity (non-human IAM with cryptographic IDs) to provide an auditable trail of all actions and reasoning; a centralized Agent Registry to manage approved agents, skills, tools and application artifacts, and prevent unauthorized tool integrations; and an Agent Gateway to proxy traffic, enforce Model Armor policies, and actively block destructive actions. These features are available on Agent Runtime today and will be available soon on Cloud Run and Google Kubernetes Engine (GKE).

Ready to start deploying agents? Check out various codelabs featuring Gemini Enterprise Agent Platform here.

Build the future of cloud-native applications

Whether you’re a vibe coder deploying your first full-stack application, a software architect standardizing production microservices, or an enterprise team scaling a fleet of secure AI agents, Google Cloud delivers the simplicity, elasticity, and security you need. Read the full report: Download your complimentary copy of the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms (CNAP).

_{Magic Quadrant for Cloud-Native Application Platforms, By Mukul Saha, Alex Coqueiro, Prasanna Lakshmi Narasimha, Richard Watson, 3 August 2026}

_{Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google.}

_{Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.}

How AlloyDB ScaNN scales vector search to 10 billion vectors

Thu, 20 Aug 2026 16:00:00 +0000

To satisfy the demands of enterprise-grade agentic AI applications, underlying vector databases often struggle to scale effectively as modern use cases can scale to billions of vectors.

As a fully managed PostgreSQL-compatible database service, AlloyDB is engineered to handle demanding enterprise workloads. Combining Google's infrastructure with the reliability of commercial databases, it delivers high availability, scalability, and includes a cutting-edge analytical engine, optimal for agentic AI use cases. A key part of this is its ScaNN index, which now operates efficiently at a scale of 10 billion vectors. This was achieved through a major architectural enhancement: an innovative four-level tree (preview) paired with efficient memory usage.

The 10 billion vector scale challenge

Scaling to a 10 billion vector workload presents significant memory and computational challenges. Previous AlloyDB ScaNN tree-based index was limited to two- or three-level tree configurations, and attempting to scale those structures led to several bottlenecks:

Increased compute intensity: Larger tree structures demand significantly more operations for both index construction and query traversal.
Memory constraints: The sampling processes required for 10 billion vectors can easily exceed the system's available memory capacity.

Solution: Four-level architecture

The introduction of a four-level tree (preview) is the primary innovation in the recent AlloyDB ScaNN release. This architecture, illustrated in Figure 1, employs a top-down strategy to optimize the balance between accuracy and build efficiency. To maintain high performance and mitigate recall loss, the system integrates key enhancements such as Top-K branch, SOAR, centroid adjustment and balanced tree shape.

Figure 1. AlloyDB ScaNN four-level tree architecture

This design has two primary benefits:

1. Reduced compute intensity via hierarchical partitioning

The four-level architecture drastically reduces compute intensity by using hierarchical partitioning to restrict the volume of vectors scanned during a query. Instead of traversing a flat or poorly segmented space, the multi-layered hierarchy narrows down the search path exponentially. Figure 2 illustrates the search spaces across different tree levels, demonstrating how structural layering optimizes traversal efficiency:

Figure 2. Search space for two-, three- and four-level trees

Two-level: Utilizes coarse partitioning to guide queries, resulting in a basic search complexity of O(N^1/2).
Three-level: Introduces an intermediate layer to further subdivide clusters, narrowing exploration to O(N^1/3).
Four-level: Implements refined, highly granular partitions that optimize traversal efficiency down to O(N^1/4), sufficiently allowing for more than 10-billion vectors.

By dynamically expanding hierarchical layers as the dataset expands, AlloyDB ScaNN maintains ultra-low query latency and avoids computational scale walls from impacting performance.

2. Efficient memory usage

Achieving a 10 billion vector scale requires high memory efficiency. AlloyDB ScaNN uses these strategies to maximize memory management performance:

Balanced tree shape construction: The four-level tree utilizes a balanced configuration to circumvent memory limitations that restrict the size of training datasets. This balanced architecture effectively leverages reduced sampling sizes to construct high-fidelity tree partitions.
Sampling optimization: When the system encounters memory limitations, it generates a condensed sampling set that considers performance and accuracy.

Performance test results

By leveraging the innovative four-level tree architecture in our internal tests, we are able to achieve the following performance results:

AlloyDB can scale to over 10 billion vectors with its ScaNN index.
AlloyDB can deliver <= 51 ms p95 latency and 95% recall at 10 billion vectors with its ScaNN index.

Get started today

Experience AlloyDB ScaNN's four-level tree (preview) architecture today. You can deploy ScaNN for AlloyDB by following our quickstart guide to set up an instance. For optimized, high-speed vector search, refer to the official ScaNN documentation. New users can also explore AlloyDB through our 30-day free trial program. We can’t wait to hear about what you build!

10 questions every startup should answer before moving to production with their AI prototype

Thu, 20 Aug 2026 16:00:00 +0000

It’s never been easier to start an AI-powered startup on Google Cloud.

You grab an API key from Google AI Studio at breakfast, paste it into Antigravity, and by lunch you’ll have a nascent prototype of your product.

But it’s not all one straight line to progress. It's common to bump into these three challenges as you build out your stack:

A leaked API key racks up a large bill in 48 hours.
A "quick" migration from AI Studio to Gemini Enterprise Agent Platform stalls the roadmap for weeks because nobody on the team owns Identity and Access Management (IAM).
The launch works, until the app starts returning HTTP 429 Too Many Requests because of default per-project quotas, and there's no clean path to more capacity without paying a premium.

None of these are unique edge cases. . They're default failure modes of moving fast without a plan, and we've all done it at least once.

Below are the 10 questions every startup should be ready to answer before they scale, grouped into the three phases where decisions can shape your future:

Onboard (setting up your own projects and identities right)
Scale (getting more throughput without breaking the bank)
Govern (keeping costs, keys, and agents from running away).

These ten are scoped to the prototype-to-production transition itself. Each question ends with a short, runnable snippet you can copy into your own project today. Adjacent decisions that matter just as much but aren't specific to that move, your data layer and RAG architecture, CI/CD, network design, are deliberately out of frame here.

Onboard: get the foundation right (in the first hour).

#1 Where should I start: Google AI Studio or Gemini Enterprise Agent Platform?

Both surfaces expose the same Gemini family of models, but they solve different problems.

Google AI Studio (with the Gemini Developer API) is the fastest path from an idea to working code. A browser IDE, an API key, a generous free tier, and no cloud project to configure. It's where most ideas should start, and Google's own guidance says as much.
Gemini Enterprise Agent Platform (formerly Vertex AI) has the same Gemini models (plus 3rd party and OSS ones) with enterprise controls around them: IAM and service-account auth instead of raw keys, VPC Service Controls, Cloud Logging and Monitoring, reserved capacity, regional endpoints, and the compliance surface your first enterprise customer's security review will ask about.

The right answer for most startups is both, sequenced deliberately: first prototype in AI Studio, then migrate before you have real users. The danger for startups is treating them as interchangeable solutions, AI Studio's simple key model does not translate to enterprise controls, and Agent Platform's IAM model might look like overkill until the day it saves you from a stolen-credential incident.

It's less work than it sounds like.

The unified google-genai SDK targets both:

code_block: <ListValue: [StructValue([('code', '# Prototype: Google AI Studio, raw API key\r\nfrom google import genai\r\nclient = genai.Client(api_key="YOUR_AI_STUDIO_KEY")\r\n\r\n# Production: GEAP, no key — uses Application Default Credentials (ADC)\r\nfrom google import genai\r\nclient = genai.Client(\r\n vertexai=True,\r\n project="my-startup-prod",\r\n location="us-central1",\r\n)\r\n\r\nresp = client.models.generate_content(\r\n model="gemini-2.5-pro",\r\n contents="Summarize this contract in three bullets.",\r\n)\r\nprint(resp.text)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5dde11910>)])]>

#2 How do I set up a Google Cloud project without becoming an IAM expert?

The biggest reason startups stall on the migration to Agent Platform isn't the code, it's the operational leap from "here's an API key" to a cloud project with folders, service accounts, org policies, logging, and IAM bindings. If your team doesn't have a dedicated cloud admin, that first project setup can eat a week of engineering time.

Three moves cut that dramatically:

Use an opinionated project template instead of clicking through the console. The Cloud Setup checklist and the Google Cloud Architecture Framework give you a production-grade folder hierarchy (prod / non-prod / dev), a central logging + monitoring project, Security Command Center turned on, and baseline org policies, without you having to design them from scratch.
Enable the APIs you'll actually use, once. Batch it so you're not doing it project-by-project when you need it. The billing-link step is not optional. Every paid API you're about to enable will refuse to activate on a project with no billing account attached, so we handle that first.
Let Gemini pick the roles, but ask it for the narrow ones. You don't have to memorize the roles reference. In the Grant access dialog, Help me choose roles lets you describe the task in plain language, "this service account needs to call Gemini models and read one Cloud Storage bucket", and get predefined roles back with the reasoning shown. One catch worth knowing on day one: by default it suggests roles that cover common journeys, which usually means a service's Admin, Editor, or Viewer. Those are broader than you want. Say "least privileged" or "narrowest access" in the prompt and it returns granular roles instead. Same amount of typing, considerably smaller blast radius when a credential leaks.

Sources: Get predefined role suggestions with Gemini assistance

code_block: <ListValue: [StructValue([('code', '# One-shot: create a Vertex-ready project and turn on the services a\r\n# typical AI startup uses.\r\ngcloud projects create my-startup-prod --name="My Startup (prod)"\r\ngcloud config set project my-startup-prod\r\n\r\n# REQUIRED before enabling billing-dependent APIs (aiplatform, run, etc.).\r\n# Use `gcloud billing accounts list` to find your billing account ID.\r\ngcloud billing projects link my-startup-prod --billing-account=012345-6789AB-CDEF01\r\n\r\ngcloud services enable \\\r\n aiplatform.googleapis.com \\\r\n run.googleapis.com \\\r\n artifactregistry.googleapis.com \\\r\n logging.googleapis.com \\\r\n monitoring.googleapis.com \\\r\n secretmanager.googleapis.com \\\r\n cloudbilling.googleapis.com'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6f4d0>)])]>

Sources: gcloud services enable reference, · gcloud billing projects link (GA), GE Agent Platform environment setup.

If you're a solo founder, resist the urge to build in your personal GCP account. Create a proper organization or self-owned org first, then create the project inside it. That single decision can make everything else, fromIAM to billing and audit, dramatically easier.

#3 I'm on Google Cloud, how should my code actually authenticate: API keys, service accounts, or user credentials?

There's a hierarchy of safety here, and the easiest option is rarely the right one in production.

Raw API keys are fine for local prototyping. They are dangerous in production because they are long-lived, easy to leak into a client bundle or a public repo, and grant unbounded access until you notice.
User credentials via OAuth (application default credentials) are best for interactive tools, CLIs, and any code that runs on a developer's laptop.
Service accounts with least-privilege IAM roles are the right answer for anything running on a server, in a container, or in a scheduled job.

The pattern you're aiming for is one where your code never sees a key at all. It just calls the Google Auth library, which quietly reads Application Default Credentials (ADC) from the environment, a short-lived token minted for whichever service account is attached to your Cloud Run service, GKE workload, or Compute Engine VM. You get enterprise-grade auth without writing any auth code.

code_block: <ListValue: [StructValue([('code', '# On a developer laptop\r\ngcloud auth application-default login\r\n\r\n# On a server (Cloud Run, GKE, etc.) — no login, no key file.\r\n# Attach a service account with just the roles the app needs.\r\ngcloud run deploy my-agent \\\r\n --image=us-docker.pkg.dev/my-startup-prod/agents/api:v1 \\\r\n --service-account=agent-runtime@my-startup-prod.iam.gserviceaccount.com \\\r\n --region=us-central1'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6ced0>)])]>

code_block: <ListValue: [StructValue([('code', '# Application code — notice: no keys, no secrets.\r\nfrom google import genai\r\n\r\nclient = genai.Client(\r\n vertexai=True,\r\n project="my-startup-prod",\r\n location="us-central1",\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6ca50>)])]>

Do one last favor to your future self: give that service account the minimum IAM role your workload actually needs, usually roles/aiplatform.user for calling models, not the broader admin roles. It takes an extra 30 seconds and prevents the credential from becoming a master key if it leaks.

#4 When should I actually stop procrastinating and migrate from AI Studio's API key to Agent Platform's IAM model?

Sooner than you'd like, and the correct trigger is not when it breaks. It's when any of these is true:

Your key has left your laptop (checked into a repo, pasted into a Slack, shipped in a mobile app).
You have more than one person on the team who needs to call the API.
You're spending more than a few hundred dollars a month.
You're about to onboard paying customers.

A potential pitfall that can catch growing startups off guard is simple: a leaked Gemini API key on an account that normally spends $180 a month gets scraped from a public repo and used to run distillation attacks, accumulating tens of thousands of dollars in charges before the owner even sees the first billing alert. The Google Cloud Shared Responsibility Model is unambiguous: the customer is liable for charges incurred with their own valid credentials.

The migration itself is genuinely smaller than the anxiety around it. In google-genai it's the two-line change shown in #1. What takes real time is the project setup around it, which is exactly why #2 exists.

Practical checklist for cutover day:

code_block: <ListValue: [StructValue([('code', '# 1. Revoke every existing AI Studio key that has ever left a laptop.\r\n# (Go to https://aistudio.google.com/apikey and delete them.)\r\n\r\n# 2. Confirm your production code has no api_key= arguments.\r\ngrep -rn "api_key" src/\r\n\r\n# 3. Enable GEAP and confirm ADC works locally.\r\ngcloud services enable aiplatform.googleapis.com\r\ngcloud auth application-default login\r\npython -c "\r\nfrom google import genai\r\nc = genai.Client(vertexai=True, project=\'my-startup-prod\', location=\'us-central1\')\r\nprint(c.models.generate_content(model=\'gemini-2.5-flash\', contents=\'ping\').text)\r\n"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6c190>)])]>

If step 3 prints a response, you're on Agent Platform.

Scale: get more capacity without paying a premium.

#5 Now that I'm shipping, why on earth am I getting all these HTTP 429 errors, and how do I make them stop?

429 Too Many Requests from Agent Platform almost always means one of two things:

You've hit the Dynamic Shared Quota (DSQ) ceiling for your project's tier. DSQ is a shared pool sized against your project's history, new projects start with modest limits by design, to prevent abuse across the platform.
You're calling a global endpoint during a global demand spike, competing with worldwide traffic for shared capacity.

The instinctive reaction is to file a quota-increase ticket. You can do that if you must, but two architectural moves usually solve the problem faster and cheaper.

Pin to a regional endpoint. Over half of startup traffic on Agent Platform defaults to global routing. Pinning to a specific region (say us-central1) sidesteps global contention and typically improves latency at the same time. (One narrow exception, which we'll get to in the next question: if you specifically want Priority PayGo, that feature currently only ships on the `global` endpoint. For everything else, pin regionally.):

code_block: <ListValue: [StructValue([('code', 'from google import genai\r\n\r\n# Global (default): competes against worldwide demand.\r\n# Regional: routes only to the regional cluster, less contention.\r\nclient = genai.Client(\r\n vertexai=True,\r\n project="my-startup-prod",\r\n location="us-central1", # <-- this is the one-line fix\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6c650>)])]>

Add real retry and backoff. A 429 is a retryable signal, not a fatal error. Any production client should have exponential backoff with jitter. The modern google-genai SDK ships this behavior built in, but only if you actually enable it. This is easy to overlook. Don't reach for the classic `google.api_core.retry.if_transient_error` decorator you may have seen on older Vertex code. It's designed for the legacy exception classes and does not recognize the new `google.genai.errors.APIError, so it will silently pass 429s through without retrying. Use the SDK's built-in retry options instead:

code_block: <ListValue: [StructValue([('code', 'from google import genai\r\nfrom google.genai import types\r\n\r\nclient = genai.Client(\r\n vertexai=True, project="my-startup-prod", location="us-central1",\r\n http_options=types.HttpOptions(retry_options=types.HttpRetryOptions(\r\n attempts=5, initial_delay=1.0, max_delay=60.0, exp_base=2.0, jitter=1.0,\r\n http_status_codes=[408, 429, 500, 502, 503, 504],\r\n ))\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6da90>)])]>

How do you see this coming? Preferably not from a user telling you. Agent Platform publishes serving metrics to Cloud Monitoring, and there is a prebuilt dashboard you don't have to assemble: Console → Agent Platform → Dashboard → Model observability. It gives you requests per second, token throughput, first-token latency, and error rates out of the box.

The metric to actually alert on is aiplatform.googleapis.com/publisher/online_serving/model_invocation_count. It carries an error_category label with values of user, system, or capacity. Alerting on capacity isolates genuine throttling from your own bad requests, which a raw 429 count won't do.

One thing worth internalizing, because it trips people up: you cannot build a "warn me at 80% of my quota" alert for Standard PayGo. Under Dynamic Shared Quota there is no fixed per-project number to be at 80% of. A 429 means transient contention for shared capacity, not that you crossed a line. Percent-of-limit alerting only becomes meaningful once you're on Provisioned Throughput, which does expose real limit metrics.

code_block: <ListValue: [StructValue([('code', 'gcloud monitoring policies create --policy-from-file=capacity-alert.yaml'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6f890>)])]>

Sources: Agent Platform metrics list, Model observability dashboard, RetryOptions source, core retry_base.py, genai errors.py, reduce 429 errors, gcloud monitoring policies create, Dynamic Shared Quota.

Follow the Agent Platform rate limits documentation to understand what your project's current ceiling actually is before you assume you've outgrown it.

#6 Which consumption mode do I pay for: Standard PayGo, Priority PayGo, or Provisioned Throughput?

Three consumption models, three completely different workload shapes, and three completely different ways to proceed. Picking the right one can help startups see meaningful savings on AI bills. First let’s define them and then see when they are, or aren’t, a good fit:

Standard PayGo (DSQ): Pay per token from a shared pool; cheap, no guarantees.
Priority PayGo: Pay per token at a premium to jump the queue.
Provisioned Throughput (PT): Prepay for reserved capacity; predictable, use it or lose it.

Consumption type	Best for	Watch out for
Standard PayGo (DSQ)	Early-stage, low-QPS, spiky prototype traffic	429s during spikes; no reliability SLO
Priority PayGo	Bursty, revenue-critical traffic that can't tolerate 429s	Roughly 1.8x the standard token price
Provisioned Throughput (PT)	Steady, predictable, high-volume production traffic	Wasted spend if utilization is under ~40%; overflow to PayGo on spikes

The dominant startup mistake is buying PT too early. Usually this happens the week after a big launch when it feels like traffic will only ever go up. PT is reserved capacity. You pay whether you use it or not, and it only starts paying you back once your baseline is genuinely predictable, not just aspirational.

Here’s a pragmatic sequence:

Weeks one through four on Standard PayGo. Use it to measure your real request shape (tokens per minute at p50 and p99, request bursts, batchable vs. real-time split).
When you get your first bad 429 storm, flip on Priority PayGo for the traffic that actually matters. It's a config change, not a purchase order, nobody in procurement needs to be involved:

code_block: <ListValue: [StructValue([('code', '# Priority PayGo request: use the global endpoint + two extra headers.\r\nfrom google import genai\r\nfrom google.genai import types\r\n\r\nclient = genai.Client(vertexai=True, project="my-startup-prod", location="global")\r\nresp = client.models.generate_content(\r\n model="gemini-2.5-pro",\r\n contents="Rank these support tickets by urgency: ...",\r\n config=types.GenerateContentConfig(\r\n # Priority PayGo headers, per current GEAP docs.\r\n http_options=types.HttpOptions(headers={"X-Vertex-AI-LLM-Request-Type": "shared", "X-Vertex-AI-LLM-Shared-Request-Type": "priority"}),\r\n ),\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6e610>)])]>

3. Once you can predict your baseline TPM, buy PT to cover the flat baseline and let anything above it overflow to PayGo. That's the combined pattern Google recommends for exactly this reason. Best of both worlds, not marketing spin.

Sources: Priority PayGo docs, google-genai HttpOptions source, GEAP REST reference.

#7 Which of my requests actually need to be live, and which should be batch jobs?

Most startup workloads are secretly batch jobs pretending to be real-time. Every one you move off the interactive path frees up DSQ headroom for the traffic that genuinely needs to be fast, the traffic where a user is actually watching a spinner.

Three questions to help you sort your traffic:

Does a human have to see the result within a second? That means: Live inference.
Can the user wait a few seconds and see a spinner? That means: Still live, but a candidate for streaming.
Would the user tolerate "we'll email you when it's ready" or "check back in a bit"? That means: Batch prediction.

Batch prediction on Agent Platform runs in a completely separate queue, does not consume your interactive DSQ, and is typically about half the price of on-demand inference. That's a rare double win: faster live traffic and a lower bill.

code_block: <ListValue: [StructValue([('code', '# Kick off a batch prediction job from a JSONL file in Cloud Storage.\r\n# Each line is one prompt; results land in another Cloud Storage prefix.\r\nfrom google import genai\r\nfrom google.genai import types\r\n\r\nclient = genai.Client(vertexai=True, project="my-startup-prod", location="us-central1")\r\n\r\njob = client.batches.create(\r\n model="gemini-2.5-flash",\r\n src="gs://my-startup-prod-batch/inputs/nightly-summaries.jsonl",\r\n config=types.CreateBatchJobConfig(\r\n dest="gs://my-startup-prod-batch/outputs/",\r\n ),\r\n)\r\nprint(job.name, job.state)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6d3d0>)])]>

Common candidates: nightly document summarization, background classification of new signups, bulk translation, embedding backfills, evaluation runs against your test set. If any of those are on your live path today, moving them is often the single highest-leverage change you can make this week.

Govern: Keep costs, keys, and agents under control.

#8 How do I set spend caps that actually reduce cost, and not just send me polite emails while my bill triples?

Until recently the honest answer was that budgets only notify, and you had to build your own brake pedal. That changed in July. There are now three mechanisms, and you should think of them as layers.

A spend cap budget (Preview). Cloud Billing budgets can now enforce rather than just email. Set a spend cap on a project and, when usage costs cross 100% of the budget, Google pauses the service until you manually lift it. Agent Platform is explicitly on the eligible list, alongside the Gemini API, Cloud Run, and Cloud Run functions. Alerts still fire at 50% and 80%, so the pause isn't a surprise.

Three things to know before you rely on it:

Each cap covers one project and one eligible service. It is not account-wide protection. If you want Agent Platform and Cloud Run both capped, that's two caps.

Enforcement is not instant and is based on estimated costs. Overages past the cap are billed as normal, so set the number below your real ceiling. Lifting it is manual, and service resumption can take up to an hour. It also pauses Provisioned Throughput usage, so if you've prepaid for capacity, a cap hit stops that too.

It's in Preview as of publication, and the eligible-service list is documented as growing. Check the current list before you design around it.

2. A billing budget with a Pub/Sub trigger that disables billing. Still the right tool when you need blast radius the spend cap can't give you: multiple services at once, an entire project, or a service that isn't eligible yet. When the budget hits a threshold, Pub/Sub fires a Cloud Function that detaches the billing account, which stops all billable activity within minutes. Blunter and more dangerous than the native cap — it can leave resources unrecoverable — so reach for it second, not first. Full walkthrough: Automatically respond to budget notifications.

code_block: <ListValue: [StructValue([('code', '# Sketch: create a budget SCOPED TO ONE PROJECT that publishes to Pub/Sub at 50%, 90%, 100%.\r\ngcloud billing budgets create \\\r\n --billing-account=012345-6789AB-CDEF01 \\\r\n --display-name="my-startup-prod hard stop" \\\r\n --budget-amount=2000USD \\\r\n --filter-projects=projects/my-startup-prod \\\r\n --threshold-rule=percent=0.5 \\\r\n --threshold-rule=percent=0.9 \\\r\n --threshold-rule=percent=1.0,basis=current-spend \\\r\n --notifications-rule-pubsub-topic=projects/my-startup-prod/topics/budget-alerts'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6f950>)])]>

Sources: Manage spend cap budgets, Set up programmatic notifications gcloud billing budgets create reference, Cloud Billing budgets concepts, Disable billing with notifications walkthrough, Programmatic notification payload schema.

Two things to get ahead of for, as the defaults can cause unexpected issues:

Limit your budget scope: Without --filter-projects, your budget applies to your entire billing account. A spike in any project will trigger the kill switch for everything.
Deploy locally: The budget notification doesn't specify which project is affected. To ensure the kill switch only affects the intended project, deploy your Cloud Function in the same project you're protecting (e.g., my-startup-prod).

Then wire up a tiny Cloud Function to that topic that calls projects.updateBillingInfo to unlink the billing account when the 100% threshold fires. That is your circuit breaker.

Mechanical ceilings via quota overrides. Even if you never set up the above kill switch, you can cap the rate at which cost can accumulate by setting explicit per-model, per-region quotas below the platform default. If your app never legitimately needs more than 500 requests per minute for gemini-2.5-pro, cap it there in the Cloud Quotas console; a leaked key can't burn what the quota flatly refuses to serve.

#9 Where should I actually keep secrets? (Not in .env files!)

The short answer is: Secret Manager. Not in environment variables, not in .env files, and never in your repo. Grant read access via IAM only to the service account that needs it.

code_block: <ListValue: [StructValue([('code', '# Store a third-party API key (Stripe, OpenAI, whatever).\r\necho -n "sk_live_xxx" | gcloud secrets create stripe-live-key --data-file=-\r\n\r\n# Grant only the runtime service account access to read it.\r\ngcloud secrets add-iam-policy-binding stripe-live-key \\\r\n --member=serviceAccount:agent-runtime@my-startup-prod.iam.gserviceaccount.com \\\r\n --role=roles/secretmanager.secretAccessor'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6de50>)])]>

code_block: <ListValue: [StructValue([('code', '# Application code fetches it at startup; nothing lives on disk.\r\nfrom google.cloud import secretmanager\r\nsm = secretmanager.SecretManagerServiceClient()\r\nresp = sm.access_secret_version(\r\n name="projects/my-startup-prod/secrets/stripe-live-key/versions/latest"\r\n)\r\nstripe_key = resp.payload.data.decode("utf-8")'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6ed90>)])]>

Then two little disciplines that pay for themselves the first time you need them:

Rotation on a schedule and on suspicion. Secret Manager versions are cheap; treat them as immutable and roll forward.
Detection when a secret leaks. Secret Manager notifications and Google Cloud's Sensitive Data Protection can catch keys checked into a repo or pasted into a log stream, before an attacker does.

For any AI application that acts on a user's behalf, calls Gmail on their behalf, reads a Drive folder, hits a third-party SaaS with the user's credentials, do not store a long-lived token. Use OAuth 2.0 with short-lived access tokens and a refresh flow, so that when a user rage-quits or a compromised account gets revoked, the agent loses access at the same time.

#10 How do I stop my brand new AI agent from doing something it absolutely shouldn't?

An agent that can call tools, browse the web, or execute code needs the same defense-in-depth thinking as any other production service, arguably more, because it makes decisions that neither you nor the model can fully predict in advance.

Four layers, none optional once you have real users:

1. Identity for the agent itself. Give the agent its own service account, scoped only to the resources and tools it genuinely needs, the exact same least-privilege principle as any other workload. Agent Engine supports first-class agent identity so every action can be attributed to a specific agent instance in your audit logs.

2. Sandboxed code execution. If your agent runs generated code, a common pattern for data-analysis or "run this Python for me" flows, do not run it in your application process. Use an isolated sandbox so a bad combination can't touch your production data.

code_block: <ListValue: [StructValue([('code', '# Enable server-side code execution inside a sandbox for a request.\r\nfrom google import genai\r\nfrom google.genai import types\r\n\r\nclient = genai.Client(vertexai=True, project="my-startup-prod", location="us-central1")\r\nresp = client.models.generate_content(\r\n model="gemini-2.5-pro",\r\n contents="Compute the correlation between these two columns: ...",\r\n config=types.GenerateContentConfig(\r\n tools=[types.Tool(code_execution=types.ToolCodeExecution())],\r\n ),\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff5ddd6d850>)])]>

3. Prompt and response filtering. Model Armor sits in front of your model calls and screens for prompt injection, jailbreaks, sensitive-data exfiltration, and off-brand output, all of which are essentially guaranteed the moment you have real users being real users.

4. Behavioral monitoring. Security Command Center with threat detection flags anomalies in agent behavior, a service account suddenly calling an API it's never touched before, an agent reaching out to an unfamiliar external host, an unexpected spike in privileged operations. In near-real-time.

None of these are optional once your agent is acting on behalf of a real user or handling real money.

Your homework, so to speak:

Audit for raw API keys in your repo, your notebooks, and your production runtime. Rotate anything that shouldn't be there.
Move any workload that doesn't need a synchronous response to the Batch API.
Turn on the Model observability dashboard and put one alert on capacity errors, so the next 429 reaches you before it reaches a customer.
Set a spend cap on the project, and keep an eye out for 50% and 80% alerts. If usage crosses 100% of the budget, Google will pause the service until you manually lift it.

Do those four things this week and you're already ahead of most startups shipping AI features.

Have a scenario you'd like us to cover next? Reach us at Google Cloud for Startups.

Cloud Blog

What’s new with Google Cloud

Aug 24 - Aug 28

Aug 17 - Aug 21

Aug 10 - Aug 14

Aug 3 - Aug 7

Jul 27 - Jul 31

Jul 20 - Jul 24

Jul 13 - Jul 17

Jul 6 - Jul 10

Jun 29 - Jul 3

Jun 22 - Jun 26

Jun 15 - Jun 19

Jun 8 - Jun 12

Jun 1 - Jun 5

May 25 - May 29

May 18 - May 22

May 11 - May 15

Apr 27 - May 1

Apr 20 - Apr 24

Apr 13 - Apr 17

Apr 6 - Apr 10

Mar 30 - Apr 3

Mar 23 - Mar 27

Mar 16 - Mar 20

Mar 9 - Mar 13

Mar 2 - Mar 6

Feb 23 - Feb 27

Feb 9 - Feb 13

Jan 26 - Jan 30

Jan 19 - Jan 23

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

The four pillars of the Pythian AI operating model

Real-world impact: from database ops to global supply chains

Ready to build your AI operating model?

Deploy personal AI agents with Cloud Run instances

Example: Deploy OpenClaw on a Cloud Run instance

What users are saying

Learn more

How Uber improves network reliability while unblocking cloud migration

Prioritizing critical traffic

Uber's key takeaways

Securing network reliability for AI and beyond

How Vodafone is using gen AI to enhance network life cycle

Simplify your resilience testing strategy with Fault Injection Testing

Why native resilience testing matters

How Fault Injection Testing works

Built for the enterprise

Get started with Fault Injection Testing

Using OKF with Knowledge Catalog to serve context for agents

Knowledge Catalog is the context engine for agents

The okf AspectType

Pushing a bundle

What pushing your OKF to Knowledge Catalog enables

Lifecycle

Getting started

Dynamic capacity management for AI infrastructure

Here's a quick summary

Why architectural flexibility matters

How to overcome infrastructure constraints

Take the next step toward dynamic infrastructure

FinOps for the AI era: New flexible billing and cost controls for agents

Give your teams flexibility without losing control over spend in Gemini Enterprise

Equip developers with advanced agentic tooling under a single Gemini Enterprise subscription

Budget smarter with Gemini Enterprise Flexible Savings Plans (FSPs)

Give your teams the freedom to build while maintaining financial discipline

Go deeper with AI cost optimization

Your chance to start building AI agents from the absolute basics

Meet your instructor

How we'll learn together

Agent 101 Live with 5 modular sessions (Jump in anytime!)

Join the livestream

Bringing gVisor sandboxes to distributed Ray clusters

Sandboxes as Ray Primitives

Why gVisor?

Try Ray sandboxing on GKE

Now introducing Gemini Enterprise for Legal

Four components of Gemini Enterprise for Legal

Unlocking high-value workflows with domain-specific skills

Open ecosystem of connectors across the legal technology stack