Cloud Blog

Google’s Continued Disruption of Malicious Residential Proxy Networks

Thu, 02 Jul 2026 14:00:00 +0000

Background

Today, in coordination with the FBI, Lumen, and others, Google took action against the NetNut residential proxy network, also known as Popa. This action builds on our disruption of the IPIDEA proxy network that took place in January 2026, and is a continuation of Google’s objective to dismantle malicious residential proxy networks.

Actions Taken

As a part of this disruption we took the following actions:

Disabled Google accounts and associated Google services used by NetNut for malware command and control (C2), which directly violates Google’s Terms of Service and Acceptable Use Policy.
Shared technical intelligence on NetNut software development kits (SDKs) and backend C2 infrastructure with platform providers, law enforcement, and research firms to help drive ecosystem-wide awareness and enforcement.
We ensured Google Play Protect, Android’s built-in security protection, automatically warned users and disabled applications known to incorporate NetNut SDKs, and the system will continue to protect users against future install attempts. These efforts to help keep the broader digital ecosystem safe supplement the protections we have to safeguard Android users on certified devices.

We believe our coordinated actions have caused significant degradation to NetNut’s proxy network and its business operations, reducing the available pool of devices for the proxy operator by millions. In addition to selling access to the network under the NetNut brand, NetNut has a robust reseller program that allows whitelabeling of its network. Google has high confidence that many popular residential proxy brands are in fact whitelabeling the NetNut botnet. While we expect this disruption to have a larger ripple effect across the residential proxy ecosystem, observations after the disruption of IPIDEA proved that individual networks can appear resilient. What we have observed is that when faced with the degradation of their own botnet, proxy operators begin buying capacity from their competitors, effectively becoming a reseller. We recognize that creating a lasting disruption in this fluid ecosystem means we must scale our efforts to target the infrastructure of several interconnected providers. We will continue to observe the composition of the NetNut network and map out how its peers adapt to this action.

Why it Matters

NetNut is among the largest and most popular residential proxy networks. Estimating the size of residential proxy networks is extremely challenging, but Google Threat Intelligence Group (GTIG) estimates the size of the NetNut network to be at least 2 million devices, distributed across the world. Public reporting by KrebsOnSecurity and others, confirmed by Google, illustrates that NetNut populates its botnet by distributing SDKs for devices commonly found in homes, such as smart TVs and streaming boxes. GTIG has also identified NetNut botnet plugin components for large-scale botnets such as Badbox 2.0.

Residential proxy networks sell the ability to route traffic through IP addresses owned by internet service providers (ISPs), allowing attackers to mask malicious activity by hijacking these IP addresses. A robust residential proxy network requires controlling millions of residential IP addresses to sell to customers for use. To accomplish this, operators need code running on home devices to enroll them into the malicious network as exit nodes. Home devices become part of proxy networks either because they are pre-installed with malware before purchase or because users unknowingly download applications containing hidden proxy code. This creates serious risks for unsuspecting device owners, as their home IP addresses can be used by attackers as a launchpad for hacking and other unauthorized activities. Consequently, users can have their legitimate traffic flagged as suspicious, or blocked by their service providers.

In a single week during June 2026, GTIG observed 316 distinct threat clusters using suspected NetNut exit nodes, including cybercriminal and espionage groups. These bad actors can use NetNut to mask their origin IP address when accessing victim environments, accessing their own infrastructure, and conducting password spray attacks. Furthermore, when a consumer device becomes an exit node, unauthorized network traffic passes through it. This means bad actors can access other private devices on the same home network, effectively exposing them to Internet threats. Public reports by Synthient, Spur, Nokia Deepfield, and others have documented the use of NetNut to infect devices with variants of Mirai DDoS botnets.

Empowering and Protecting Consumers

Consumers should be extremely wary of applications that offer payment in exchange for "unused bandwidth" or "sharing your internet." These applications are primary ways for malicious proxy networks to grow, and could open security vulnerabilities on the device’s home network. We urge users to stick to official app stores, review permissions for third-party VPNs and proxies, and ensure built-in security protections like Google Play Protect are active.

Consumers should be careful when purchasing connected devices, such as set top boxes, to make sure they are from reputable manufacturers. For example, to help you confirm whether or not a device is built with the official Android TV OS and Play Protect certified, our Android TV website provides the most up-to-date list of partners. You can also take these steps to check if your Android device is Play Protect certified.

Future Work

As we noted earlier this year, the residential proxy industry appears to be rapidly expanding, and this coordinated disruption is not the end of our work combating malicious residential proxy networks. This industry is deeply connected and operators depend on overlapping botnet networks that are constantly resold. While point-in-time disruptions are a critical tool to protect our users, continued and coordinated effort is needed to reduce malicious proxy networks in the long run. We encourage mobile platforms, ISPs, and other tech platforms to continue sharing intelligence and to take direct action to block malicious C2 infrastructure.

SOCRadar powers rapid threat detection with AlloyDB and Gemini Enterprise

Wed, 01 Jul 2026 19:00:00 +0000

Editor’s note: SOCRadar is a leading cybersecurity company that provides threat intelligence to businesses worldwide. As the volume of cyber threats continued to grow, SOCRadar needed to modernize its data infrastructure to deliver faster insights to its customers. By migrating from PostgreSQL to AlloyDB, SOCRadar achieved a 20x performance boost, reduced operational overhead, and is now better positioned to innovate and grow.

How SOCRadar supercharges rapid threat detection with AlloyDB

SOCRadar provides external threat intelligence to help organizations across 30+ countries defend against cyberattacks. On the front lines of cybersecurity, timely intelligence is everything and a delay of a few minutes can mean the difference between a blocked exploit and a full-scale breach.

As SOCRadar’s business scaled and cyber threat volumes exploded, their on-premises, self-managed PostgreSQL database hit a wall. The database simply couldn't keep pace with the simultaneous demands of high-velocity data ingestion and heavy, real-time analytical queries. This created a severe data bottleneck, slowing down the delivery of critical insights to customers and pulling engineers away from innovation to focus on constant manual database tuning.

Evaluating database alternatives: The hunt for scalability

The engineering team realized their traditional PostgreSQL environment had reached its absolute performance limits. To scale, SOCRadar needed a high-performance fully managed database that could dramatically slash operational overhead while elegantly handling a complex, hybrid workload.

They evaluated alternatives and selected Google Cloud's AlloyDB for PostgreSQL. Because AlloyDB is fully PostgreSQL-compatible, it offered a low-risk migration path while promising a specialized architecture built to handle both high-volume transactions and real-time analytics simultaneously. To accelerate the transition, SOCRadar partnered with NGC, a Premier Business Partner, who meticulously validated the architecture before executing a precision cutover with minimal downtime.

Taming a "triple-threat" workload

Migrating to AlloyDB transformed how SOCRadar processes massive, diverse cyber telemetry. Today, AlloyDB effortlessly manages what SOCRadar’s engineering team calls a "triple-threat" query environment, maintaining sub-second lookup latency even as processing volumes scale.

To understand the performance leaps, it helps to separate the system’s velocity (handling live data streams) from its depth (analyzing historical data):

High-Velocity Transactional Ingestion (OLTP): The platform constantly ingests real-time telemetry from thousands of disparate, fast-moving sources—including Dark Web forums, botnet logs, and social media feeds. AlloyDB handles these continuous INSERT and UPSERT operations with a 3.2x boost in live ingestion velocity, ensuring that the newest threat indicators are immediately recorded and available for detection.
Real-Time Operational Point-Reads: When a security analyst is actively investigating a live incident, speed is everything. Baseline performance testing under zero-load conditions for random ID lookups on indexed fields (e.g., querying a specific Indicator of Compromise by ID) showed that standard queries requiring 3 to 3.5 seconds were completed in just 1 second on AlloyDB.
Deep Analytical Aggregations (OLAP): When a client requests a complex sectoral report such as correlating the most prevalent attack vectors in the finance sector over an entire year, the database must execute deep scans across vast historical datasets. Leveraging AlloyDB’s built-in In-Memory Columnar Engine, these analytical queries run up to 20x faster than standard PostgreSQL.

More than just speed: Reclaiming 45 TB and 75% of DBA time

While the raw performance gains were massive, the operational and financial impact completely changed how SOCRadar's engineering team works day-to-day.

Thanks to AlloyDB's advanced automation, including intelligent memory management and write-ahead log (WAL) optimization, the need for constant, manual database tuning evaporated. The database administrator's (DBA) workload dropped significantly, requiring a system health check just “about once every two or three days." This freed up 75% of SOCRadar’s DBA resources, allowing them to pivot away from maintenance and focus entirely on core platform innovation.

Financially, AlloyDB’s dynamic storage management solved a massive cost efficiency issue. Unlike traditional database environments that lock you into paying for fixed, provisioned storage even after data is purged, AlloyDB automatically scales storage down to match actual data footprints. By clearing out legacy, unnecessary logs, SOCRadar was able to instantly reclaim over 45 TB of storage, achieving massive, automated cost optimization.

Fighting alert fatigue with integrated Gemini Enterprise Agent Platform

Beyond scaling infrastructure, AlloyDB has allowed SOCRadar to redefine the core architecture of their threat response using artificial intelligence.

Security operations centers (SOCs) globally are plagued by "alert fatigue"—the sheer volume of security alarms makes it easy to miss a critical attack. To solve this, SOCRadar integrated Gemini Enterprise Agent Platform as a core component of their solution architecture, linking it directly to their Alarm Management framework running on AlloyDB.

By running Gemini AI-native filtering directly on their active data workloads, SOCRadar can automatically distinguish between true positives and benign false alarms. The AI categorizes, filters, and routes alerts before they ever reach the end-user. This ensures security analysts are insulated from noise and receive only the most critical, validated, and actionable intelligence.

Expanding capabilities: The future of agentic threat hunting

With a high-performance foundation firmly established, SOCRadar’s dedicated AI team is transitioning from passive analytics to active automation. The company is currently testing Agentic AI workloads, with plans to roll them into production in subsequent phases.

By integrating Real-time Data Agents with Gemini Enterprise and AlloyDB, SOCRadar is transforming with autonomous agents that don't just store data, but actively hunt threats, reason over context, and take action. Their upcoming production roadmap includes:

Natural Language Querying (NLQ): Allowing analysts to conduct rapid threat hunting using conversational language, lowering the technical barrier to querying massive database sets.
Intelligent Semantic Similarity Search: Leveraging native vector embeddings and Gemini Enterprise to allow Data Agents to independently surface hidden patterns across historical logs that traditional keyword searches would miss.
Automated Incident Summarization: Instantly transforming hundreds of lines of complex, deeply technical logs into concise, plain-language executive summaries for security analysts during critical incidents.

By consolidating transactional velocity, historical depth, and built-in AI intelligence into a unified platform, SOCRadar has eliminated its data bottlenecks and built a highly automated, future-proof framework for global cybersecurity defense.

Ready to modernize your database infrastructure? AlloyDB provides a fully managed, PostgreSQL-compatible database with high performance for transactional, analytical, and AI workloads. Learn how you can reduce costs, eliminate management overhead, and build intelligent applications.

AlloyDB AI Functions - now with revolutionary performance boosts and cost savings

Wed, 01 Jul 2026 18:00:00 +0000

AlloyDB is an AI-native database—it isn’t just a passive data store, it intelligently understands and processes your data. With AlloyDB, you get industry-leading vector and hybrid search, near 100% accurate natural language-to-SQL capabilities to build conversational agents, tools to enable you to build with your agentic IDEs of choice, and the ability to bring the intelligence of foundation models like Gemini directly to your data through AI functions.

In this blog post, we discuss the massive breakthroughs in AI function processing alongside a suite of brand-new AI functions.

But first: what exactly are AI functions? They bring Gemini’s world knowledge to your AlloyDB data. Consider the challenge of managing raw user feedback: it’s unstructured, and difficult to parse through. Before this data can be leveraged for search, it may require pre-processing and entity extraction. Rather than maintaining complex custom pipelines for knowledge extraction, you can use Gemini’s generation capabilities directly within AlloyDB to transform raw text into structured, searchable insights. For example, here is how you can use ai.generate to instantly turn raw feedback into clean, structured JSON (see more examples here):

code_block: <ListValue: [StructValue([('code', "SELECT\r\n log_id,\r\n raw_content,\r\n -- Use Gemini 3.0 to reason through the raw user feedback and extract structure\r\n ai.generate(\r\n model_id => 'gemini-3.1-pro-preview',\r\n prompt =>\r\n 'Analyze this raw customer feedback entry. Extract the country, service name, and a 1-sentence summary of the feedback. Return as JSON.'\r\n || raw_content) AS structured_feedback\r\nFROM raw_feedback_logs\r\nWHERE user_type <> 'internal';"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4cb56160>)])]>

Here is a sample result:

log_id	raw_content	structured_analysis
1001	2025-12-16 08:00:01 [ERROR] Service: OrderSvc \| DbConnectionTimeout: Failed to acquire connection from pool "primary-shard-04" after 5000ms.	{"errorCode": "DbConnectionTimeout", "serviceName": "OrderSvc", "rootCause": "The service failed to acquire a database connection from the primary shard pool within the 5000ms timeout limit."}
1002	2025-12-16 08:05:12 [WARN] Service: IdentityProvider \| 401 Unauthorized: Bearer token validation failed for user_id=9942. Signature mismatch.	{ "error_code": "401", "service_name": "IdentityProvider", "root_cause": "The bearer token validation failed due to a signature mismatch." }
1003	2025-12-16 08:12:45 [CRITICAL] Service: AnalyticsEngine \| OutOfMemoryError: Java heap space. Allocation of 1.2GB array failed. Heap usage 99%.	{ "error_code": "OutOfMemoryError", "service_name": "AnalyticsEngine", "root_cause": "The service exhausted available Java heap memory attempting to allocate a 1.2GB array." }
1004	2025-12-16 08:25:33 [ERROR] Service: WebFrontEnd \| 404 NotFound: Resource /api/v3/users/profile/settings not found. Upstream returned 404.	{ "error_code": "404", "service_name": "WebFrontEnd", "root_cause": "The requested API resource for user profile settings was not found by the upstream service." }
1005	2025-12-16 08:35:50 [WARN] Service: NotificationGateway \| GatewayTimeout: External provider "SendGrid" failed to respond within 30s. Retry scheduled.	{"error_code": "GatewayTimeout", "service_name": "NotificationGateway", "root_cause": "The external provider SendGrid failed to respond within the 30-second timeout limit."}

More functions to summarize and analyze sentiment

Our core AI functions —ai.generate, ai.rank, ai.if, and ai.forecast—are now Generally Available. To learn more about use cases for the first three, refer to this blog post. To explore the forecast function in action, check out this deep dive.

Building on this momentum, we have introduced three brand new functions: ai.summarize, ai.agg_summarize, and ai.analyze_sentiment.

ai.analyze_sentiment: Automatically classifies the emotional tone of text as positive, negative, or neutral.
ai.summarize: Condenses lengthy text into its most essential information while preserving the original tone and nuance.
ai.agg_summarize: An aggregate tool that processes multiple rows within a column to generate a single, unified summary for an entire group (e.g., via a GROUP BY clause).

Here’s an example of how to use ai.agg_summarize to consolidate a product reviews for products on a retail website:

code_block: <ListValue: [StructValue([('code', 'SELECT productname, ai.agg_summarize(review) as reviews_summary\r\nGROUP BY productname;'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4cb56700>)])]>

Here is a sample result of summarized reviews for two gaming console products:

productname

reviews_summary

AlphaCore Console

Users praise the stunning 4K graphics, smooth 120Hz frame rates, and the highly ergonomic controller design.

However, several reviews express frustration over the loud cooling fan noise during extended gaming sessions.

Overall, it is considered a top-tier console despite minor thermal and noise complaints.

NeoCore Console

Customers love the exceptional battery life and vibrant OLED display for handheld gaming on the go.

A significant number of users noted that the UI can feel sluggish and the game library is currently limited.

It represents great value for casual gamers but power users may find the performance lacking.

The power of LLMs on your data: now significantly faster and cheaper

We now have achieved unprecedented performance and cost breakthroughs in AI function processing. Previously, running a foundation model call for every single row in a massive database introduced cost and latency constraints. We have shattered these barriers by introducing two breakthrough capabilities:

Smart Batching for AI Functions: This AI Function Acceleration capability provides intelligent batching of AI function calls for optimal performance and quality. This efficiency is achieved by deduplicating prompt overhead; the LLM's boilerplate instructions are transmitted once per batch rather than repeated across every individual row. A question you may have is - “Why not do this in my own application layer?”. That’s because, AlloyDB intelligently determines the right batch size for optimal results - if you underestimate the batch size, you won’t reap gains for cost and latency, and if you overestimate the batch size, the prompt to the LLM could get bloated and lead to hallucinations, or you could exceed the model's token limits. In addition to calculating the perfect batch size for every request, AlloyDB also handles retries automatically out of the box, ensuring your pipeline stays resilient. We did some testing internally and saw massive gains; for example, an up to 2,400x performance boost (processing 10,000 rows/sec) over traditional row-at-a-time LLM calls. This is currently available for the ai.if and ai.rank functions, with support for additional functions coming in the future.

Let’s look at an example of using Smart Batching / Acceleration with ai.if to solve this use case: Imagine a customer on a gadget retail site searching for a camera that can handle an underwater depth of '60 meters or deeper.' Traditional hybrid search will pull the closest semantic and full-text matches, but it misses the hard constraints of numerical data—meaning it might serve up a camera that works only at 20 meters depth. By using AlloyDB’s ai.if-based intelligent filtering, the database actually understands the nuance of depth and makes the query return products that meet or exceed that 60-meter depth criteria. Notice how, in the example below, you don’t need to specify the batch size - AlloyDB handles all the optimizations under the hood when using ai.if.

code_block: <ListValue: [StructValue([('code', "-- Smart Batching / AI Function Acceleration \r\nSET google_ml_integration.enable_ai_function_acceleration = on;\r\nSELECT productid, productname, category,description\r\nFROM products AS p\r\nWHERE\r\n ai.if(\r\n 'Evaluate if the product description indicates that the product is waterproof at depth 60m or deeper. Description:'\r\n || description);"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4cb568e0>)])]>

Here is a sample result on a hypothetical gadgets site. Notice how the expanded descriptions of products really match the criteria of working at a depth of 60 meters:

Optimized AI Functions: For even greater efficiency, we’ve introduced an optimized mode, starting with ai.if. By deploying a small, proxy model that utilizes your embeddings and is trained on your specific LLM outputs, we can process decisions natively within the database. This drastically reduces the need to call the external LLM - and based on some of our internal tests, we saw staggering gains; for example, up to 100,000 rows processed per second (a 23,000x improvement) and costs slashed by 6,000x (down to 1/10th of a cent). For technical insights on this technique, including when it works best and when not, refer to this blog post. AlloyDB does the following when using optimized ai.if:

Trains a proxy model: AlloyDB trains a lightweight proxy model on a sample of your data. This happens in the background when you use the PREPARE statement with ai.if function to train the model for optimized queries.
Executes the query: When you use the EXECUTE statement, AlloyDB uses the trained proxy model to process the query locally.
Falls back to the LLM: If the accuracy of the model is low, or if AlloyDB can't find a model, AlloyDB automatically falls back to using the LLM.

Let’s look at the same example of searching for a camera that can handle an underwater depth of 60 meters or deeper using optimized ai.if. Here we train a proxy model using the PREPARE statement and then EXECUTE the statement thereafter.

code_block: <ListValue: [StructValue([('code', "-- Prepare the Optimized Function / Proxy Model\r\nPREPARE waterproof_camera_60m AS\r\nSELECT productid, productname, category, description\r\nFROM products AS p\r\nWHERE\r\n ai.if(\r\n 'Evaluate if the product description indicates that the product is waterproof at depth 60m or deeper. Description:'\r\n || description,\r\n description_embedding);\r\n\r\n-- Run the Proxy Model\r\nEXECUTE waterproof_camera_60m;"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4cb56e50>)])]>

You see the same products that truly match the criteria of working at a depth of 60 meters - as shown in the screenshot above. Here’s a tabulated version for the first three products, so you can look at the descriptions more closely:

productname	description
Pulsetron Action Camera MZ314	Conquer your next adventure with this camera. Don't let the elements hold you back; dive up to 60 meters deep or withstand rugged trails with its shock-resistant, adventure-ready chassis. Every jump, every turn, every splash is rendered flawlessly smooth with advanced Horizon Lock stabilization, ensuring your footage tells the story with unparalleled fluidity.
Hyperbyte Action Camera LG688	Capture the world in breathtaking detail, even when the action is at its most intense. This camera packs a formidable 1-inch sensor into a remarkably tough, pocket-sized frame. Shoot stunning 5K video and crystal-clear 20MP stills that rival professional equipment. Dive deeper than ever before with robust waterproofing at 60 meters.
Alphasync Action Camera WW897	This formidable, compact camera shrugs off the elements, while the massive 1-inch sensor translates every breathtaking moment into stunning 5K video and crystal-clear 20MP stills. Conquer any environment – from the deepest dive to the highest peak – thanks to its 60 meter waterproofing and revolutionary Horizon Lock, ensuring your footage remains impossibly steady.

See it in action!

Watch how this all comes together in this demo video.

Getting started is easy

Ready to bring unprecedented speed and cost-efficiency to your AI workloads?

New to AlloyDB? Discover AlloyDB with a 30-day free trial.
AI functions quickstart: Enable a few quick prerequisites and start calling functions like ai.if, ai.generate, or ai.analyze_sentiment directly within your SQL queries. Check out these practical examples to begin.
Boost performance and optimize costs: To unlock the biggest performance and cost gains, follow our guide on optimized functions. This is available in preview for ai.if, and will be expanding to more functions soon. For technical insights on this technique, including when it works best and when not, refer to this blog post.
Scale your throughput: Use smart batching to accelerate AI functions (available in preview for ai.if and ai.rank) or array-based functions (generally available for all LLM-based AI functions) to handle bulk prompting smoothly.

What’s new with Google Cloud

Wed, 01 Jul 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.

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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.

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.

Get started with the Claude apps gateway for Google Cloud

Wed, 01 Jul 2026 16:00:00 +0000

Anthropic's agentic coding tool Claude Code has worked with Google Cloud for a while now. An individual developer could easily point CLAUDE_CODE_USE_VERTEX=1 at a Google Cloud (GCP) project, grant the role roles/aiplatform.user, and inference stays inside your Google Cloud perimeter.

That flow works great when it’s just you, or a handful of engineers. But rolling it out across an organization forces you to deal with enterprise friction: you have to manage per-developer cloud credentials, push a managed-settings.json to every laptop over MDM, and not be verified with zero per-developer usage attribution or easily enforceable spend caps.

The Claude apps gateway closes that gap. It is a self-hosted service, shipped with the same claude binary, that sits directly between your local Claude Code clients and Google Cloud. This post breaks down exactly why you should run it and what a secure deployment looks like on Google Cloud.

(Note: If you want to jump straight to the code, the full walkthrough lives in the Claude apps gateway on Google Cloud docs.)

Why run the gateway

Run the gateway to centralize the governance that developers and platform admins otherwise each carry alone such as identity, policy, cost, and routing. Here's what that looks like in practice.

Identity. The /login request routes through your identity provider (IdP ) - Google Workspace or any OIDC/OpenID Connect one - and the gateway swaps the token for a short-lived session. No sensitive information lands on the developer’s laptop — such as service-account keys, API keys, or ANTHROPIC_VERTEX_PROJECT_ID. Onboarding is as simple as adding a user to an IdP group; offboarding by removing them, and their next session refresh fails on the spot.

Policy. Your RBAC (role-based access control) rules live once in gateway.yaml, resolved per group and enforced server-side. The gateway re-checks availableModels on every /v1/messages call, so editing local managed-settings.json changes nothing — and rule updates reach the whole fleet within the hour.

Telemetry. Every claude_code.token.usage metric carries the verified email and groups from the session JWT (signed session token), not the spoofable client-set OTEL_RESOURCE_ATTRIBUTES. The gateway ships them over OTLP/HTTP to a collector you run — Cloud Monitoring, Grafana, Datadog, whatever you use.

Spend limits. Set daily, weekly, or monthly caps per user, group, or org via the admin API; the gateway meters tokens against a Cloud SQL ledger and returns a 429 at the cap. Costs are at list price, so treat them as a runaway-usage guardrail, not a bill reconciliation (committed-use discounts and negotiated rates don't show up).

Routing. Calls go out under a single Cloud Run service identity. Set region: global for Agent Platform's global endpoint, or add a second upstreams: entry to fail over on 5xx/429/timeout in list order. Either way, inference stays in your GCP project — quota, Data Processing Agreement, and billing all unchanged.

How it fits together

A developer's local or deployed claude process sends inference traffic to the gateway over HTTPS. The gateway is a stateless container on Cloud Run as shown below.

The gateway validates its own session bearer — Google Workspace is only contacted at sign-in and token refresh — checks policy, and forwards the request to Agent Platform using the Cloud Run service account. Cloud SQL holds device-code sign-in state and the spend ledger; an OTLP collector receives the attributed metrics.

Setting it up on Google Cloud

The full walkthrough, every gcloud command and the complete gateway.yaml reference, is in the Claude apps gateway on Google Cloud docs. The short version:

Step 1: Provision the GCP foundation
Enable the Agent Platform, Cloud SQL, and Secret Manager APIs; create a claude-gateway service account with roles/aiplatform.user; stand up a small Cloud SQL Postgres database instance for state. The gateway authenticates to Agent Platform as the Cloud Run service identity — you do not create a service-account key. Finally, create a new OAuth client (type Web application) in the Google Cloud console: in this example, the gateway authenticates developers against Google Workspace as an OIDC relying party, and this client is what issues it a client_id and client_secret for that handshake. Those two values feed the oidc: block in the next step. You'll later add the authorized redirect URI once the gateway URL is known.

Step 2: Configure the gateway
Write gateway.yaml pointing at your Google Workspace OIDC client, the Postgres connection string, and Agent Platform as the upstream. Store it in Secret Manager, along with the OIDC client secret, the Postgres URL, and a JWT signing key.

code_block: <ListValue: [StructValue([('code', 'listen:\r\n port: 8080\r\n public_url: https://<your-cloud-run-service-url> # the Cloud Run service URL — with --ingress=internal this resolves only inside your VPC / corporate network\r\noidc:\r\n issuer: https://accounts.google.com # Google Workspace\r\n client_id: <client-id>.apps.googleusercontent.com\r\n client_secret: ${OIDC_CLIENT_SECRET} # from Secret Manager\r\n allowed_email_domains: [yourco.com]\r\n\r\nupstreams:\r\n - provider: vertex\r\n region: us-east5\r\n project_id: <your-project>\r\n auth: {} # ADC via the Cloud Run SA, NO key file'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c564a30>)])]>

Then register https://<public_url host>/oauth/callback as an authorized redirect URI on the Google OAuth client — it must match listen.public_url exactly:

Step 3: Deploy to Cloud Run
gcloud run deploy with the service account attached, the Cloud SQL connection on the VPC, and the config mounted from Secret Manager. The container is stateless and scales horizontally behind the Cloud Run load balancer. GKE works equally well if that's already your platform, and only the deployment manifest changes.

code_block: <ListValue: [StructValue([('code', 'gcloud run deploy claude-gateway \\\r\n --service-account="claude-gateway@${PROJECT_ID}.iam.gserviceaccount.com" \\\r\n --set-secrets=/etc/claude/gateway.yaml=gateway-config:latest \\\r\n --ingress=internal \\ # private — developers reach the gateway over the corporate network (VPN/Interconnect into the VPC)\r\n --no-invoker-iam-check # the gateway runs its OWN OIDC; clients carry no GCP token'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c564160>)])]>

Developers connect over the corporate network; you may front the service with an internal Application Load Balancer — see Cloud Run private networking.

Either public or internal, your developers must be able to access whatever URL you configure or you can rely on the default URL from Cloud Run. For the below example we will use https://claude-gateway.example.internal

Step 4: Onboard a developer
Push forceLoginMethod: "gateway" and forceLoginGatewayUrl to developer machines via managed settings. This is how /login knows where to connect, with no manual URL entry. For an org rollout, that's your MDM channel. For a first trial without MDM, the developer can write the file by hand at /Library/Application Support/ClaudeCode/managed-settings.json on macOS (or /etc/claude-code/managed-settings.json on Linux) if they have local admin permissions:

code_block: <ListValue: [StructValue([('code', '{\r\n "forceLoginMethod": "gateway",\r\n "forceLoginGatewayUrl": "https://claude-gateway.example.internal"\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c564f10>)])]>

At Claude Code startup, the developer then presses Enter on the pre-filled gateway sign-in screen to confirm the URL.Confirm the device code on the gateway's verification page in the browser, and get redirected to Google Workspace to sign in. After that, the developer completes the device-code flow in the browser against Google Workspace. If setup ends correctly, you will be able to see Cloud Gateway in the terminal view as shown below.

What's next

At this point you should have a better understanding of how to configure and use Claude apps gateway on Google Cloud. Here are some next steps you may want to consider:

Full config reference: every gateway.yaml field is in claude-apps-gateway-config. Per-IdP setup and the GKE track live in claude-apps-gateway-deploy and claude-apps-gateway-on-gcp.
Group-scoped policies: front the gateway with a groups-capable IdP, set groups_claim, and add match: { groups: [...] } policies above the catch-all to give different teams different model lists and tool permissions.

For now, thanks for reading! And if you have any additional questions or feedback, feel free to reach out on socials (Roy Arsan - Linkedin, X and Ivan Nardini - LinkedIn, X)

Happy building!

Google named a Leader in 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for third year in a row

Wed, 01 Jul 2026 16:00:00 +0000

For the third consecutive year, Google has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms. This recognition comes on the heels of Google Cloud Next 2026, where we feel we showcased a fundamental evolution in how enterprises interact with data: the shift from a reactive system of intelligence to a proactive system of action. As organizations rapidly evolve to incorporate autonomous AI into daily operations, Looker and Google are redefining the modern stack by bridging the gap between data insights and automated business workflows. By anchoring this agentic transition in a foundation of enterprise-grade trust, Looker’s Agentic solution continues to serve global organizations, from fast-growing startups to large enterprises, by transforming raw data into trusted, actionable business value.

Our momentum in this agentic era relies on two core pillars: a universal semantic layer that establishes a foundation of truth, and Gemini’s deep reasoning capabilities that turn that truth into autonomous business action.

Download the complimentary 2026 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms.

The universal semantic layer agents

At the core of the agentic transition is Looker’s semantic layer. In a world where hallucinated data and conflicting metrics can kill business, LookML is critical for customers such as YouTube, Telenor and Allo Fiber, deploying agents into production at scale and keeping them grounded in verified enterprise truth.

Key governance strengths include:

Unified analytics governance: Centralized, code-based semantic layer that guarantees metric consistency across the organization. Pairing this single source of truth with hierarchical permissions and a new certification framework controls content trust levels and enables proactive platform auditing.
Governed semantic layer for in-database analytic and graph modeling: Native integration with in-database analytic models, including BigQuery Graph and Snowflake semantic views, enables organizations to define, version-control, and manage complex relational and graph-based data relationships natively within LookML while maintaining a consistent semantic truth across external applications.
Enterprise lifecycle management: Native, git-based version control supports continuous integration testing and seamless multi-environment management before production deployment.
High concurrency architecture: Elastic resource allocation mitigates usage spikes, allowing teams to deliver scalable insights during heavy user demand.

Powered by Gemini’s reasoning capabilities

True agentic business intelligence requires deep cognitive reasoning. Looker’s purpose-built BI generative AI capabilities with Gemini 3 serve as Looker’s native AI fabric, unlocking two significant advantages across the enterprise:

For business users: It enables complex, multi-layered strategic analysis through advanced, natural-language abstract reasoning.
For developers: It embeds directly into the daily workflow to dramatically accelerate analytics engineering with reliable LookML auditing and automated code writing.

Leaping into the Agentic BI era with Looker and Gemini

Gemini is powering the reimagination of the Looker stack from agentic semantic modeling, data explorations, Dashboard Agents, to Conversational Analytics. At Google Cloud Next 2026, we showcased our latest Looker product innovations:

Looker everywhere: We are expanding Looker's footprint beyond traditional interfaces through headless BI architectures, highlighted by Looker’s Managed MCP offering. This capability brings our semantic intelligence directly to external platforms, as showcased by PayPal, which successfully scaled accurate conversational analytics to 3,000+ users via Claude Desktop and Looker MCP. Developers can also leverage the Conversational Analytics API to build, secure, and deploy custom, trusted data agents within any proprietary application or third-party agent platform.
Looker BI Agents: Our specialized conversational agents allow users to query complex data models across applications using plain natural language, while dashboard agents embed these interactive conversational experiences directly into existing dashboards. Fully integrated with Gemini Enterprise, these agents can be deployed straight into your corporate workspace workflows. Furthermore, across all environments, these agents enable users to orchestrate autonomous agentic workflows and monitor execution, helping ensure teams can interact with LookML-governed metrics and trigger automated actions right where they already cooperate.
AI-powered self service: We’ve completely reimagined Explore Mode, combining an intuitive drag-and-drop canvas with conversational analytics. Tools like the Visualization Assistant use natural language to design beautiful charts on the fly, while the Insight Assistant auto-generates narratives to surface key trends in seconds. This sits alongside now generally available (GA) paginated reporting and tabbed dashboards.
Vibe-coding with the LookML Agent: This specialized AI agent and new VS Code extension enable full-lifecycle LookML development, management, and deployment entirely outside of Looker in any VS Code-based IDE. The agent accelerates semantic modeling by translating natural language into LookML, generating models directly from existing BigQuery/AlloyDB datasets, and integrating easily with existing agentic skills. Allowing developers to develop and interact with Looker within a single interface delivers a highly cohesive and accelerated environment.
Universal semantic layer: With the new in-database analytics model support, LookML can support graph models and complex semantic ontologies with BigQuery Graph and Snowflake Semantic Views. LookML can flex to a broad set of governed use cases for data agents for industries like retail, supply chain, cybersecurity, with graph relationships alongside table-based models for scale-out BI agents.

By combining Looker's analytical governance with Google's Agentic Data Cloud, we help ensure your autonomous AI agents operate on verified enterprise metrics, not hallucinated guesswork. Whether you are deeply embedded in the Google Cloud ecosystem or leveraging Looker across a multi-cloud data architecture, Looker provides the openness, scale, and semantic grounding required to power the future of business.

Download the report: Read the full 2026 Gartner Magic Quadrant for ABI Platforms to explore the detailed vendor evaluations.
Explore agentic Looker: Read our full recap of Looker Updates for Agentic BI at Next '26.

_{Gartner Magic Quadrant for Analytics and Business Intelligence Platforms - Anirudh Ganeshan, Edgar Macari, Christopher Long, June 29, 2026}

_{GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.}

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

New IDC study: The business value of Mandiant Consulting

Wed, 01 Jul 2026 16:00:00 +0000

Security leaders are now expected to protect business growth and clearly articulate security value to their board of directors, in addition to managing risk. While translating technical defense into measurable financial returns can be challenging, Mandiant Consulting can help you bridge the gap.

Organizations that engaged with Mandiant Consulting reported an average annual benefit of $4.3 million, driving a 268% three-year ROI, with a payback period of just 4.1 months, according to a new IDC Business Value White Paper commissioned by Mandiant. IDC based these findings on its standard ROI methodology and qualitative and quantitative interviews of current Mandiant customers, applying standard financial models. The interviewed organizations are large, highly-complex environments with an average of $17.3 billion in revenue and 74,000 employees.

At Google Cloud, we strongly believe that security is a strategic business enabler that can directly impact your bottom line.

One healthcare organization interviewed by IDC reported that its partnership with Mandiant completely changed the dynamic of its commercial conversations. "Mandiant has enabled us to engage more confidently with customers and position our security posture as a market differentiator, with security now consistently ranking among the top three reasons clients choose us. It has also contributed to reducing our insurance costs by $50,000 per year,” said the healthcare organization.

Frontline threat intelligence in action

CISOs consistently struggle with internal resource constraints and skill deficits, and internal security teams rarely have the time to track every emerging threat group. Mandiant addresses this by distilling findings and delivering frontline threat intelligence and guidance derived from over 500K hours of global incident investigations last year.

Instead of trying to monitor everything, resource-constrained teams can focus their limited hours on the specific threats that are most relevant to them and likely to target their specific industry and build specific targeted defenses. A retail organization highlighted how working with Mandiant experts allowed them to actively defend against targeted campaigns, like those from the Scattered Spider cybercrime group.

"One of the most significant accomplishments from using Mandiant has been their ability to help us create detection use cases specific to Scattered Spider based on their industry knowledge. This has enabled us to monitor, detect, and neutralize related attacks, which is a key reason we have avoided incidents,” the organization told IDC.

To ensure detections are built on solid foundations, many organizations also use Mandiant to run deep technical audits across their identity infrastructure — including Active Directory, privileged account management, and multi-factor authentication (MFA). This independent verification provides crucial reassurance to leadership, an energy-sector organization told IDC.

"Mandiant provides external assurance that our cyberprogram is thorough and validated from a risk management perspective. Their validation and recommendations have helped us reinforce that messaging to our board. They are highly professional, risk aligned, and among the most trusted,” they said.

Quantifying the business and operational impact

By synthesizing customer experiences, IDC quantified the broader operational advantages seen by customers who worked with Mandiant:

59% reported greater preparedness to successfully address cyberattacks.
45% reported overall improvement in cyber-resilience.
36% reported more efficient security analyst teams, allowing internal staff to focus on more strategic, growth-oriented initiatives.

To learn more about how security is a business enabler, register for our July customer webinar.

_{Source: IDC Business Value White Paper, Sponsored by Google, The Business Value of Mandiant Consulting (Doc #US54605426-BVWP, July 2026)}

Google Cloud confirmed to offer a safer choice for EU public sector organizations with Dutch DPIA approval

Wed, 01 Jul 2026 16:00:00 +0000

At Google Cloud, we are committed to providing public sector organizations around the globe with cloud technology that is highly flexible, scalable, and built with market-leading standards for data protection, sovereignty, and security. We understand that for public sector organizations in the European Union, confidence in data protection is not just a preference — it’s a prerequisite.

Today, we’re excited to announce a major milestone that reinforces this commitment for Google Cloud.

Dutch government DPIA confirms strong privacy foundation for Google Cloud

We have successfully collaborated with SLM Rijk, the Dutch government's strategic vendor management agency, who completed their rigorous data protection impact assessment (DPIA) of Google Cloud.

This engagement confirms Google Cloud’s strong commitment to strengthening trust in its privacy posture across the Dutch public sector. Given that all the key points raised during the DPIA have been successfully addressed (see SLM Rijk’s summary here), and that their DPIA concluded that there are no known high data protection risks when the recommended measures are implemented, the Dutch central public sector is now officially enabled to use Google Cloud with a clear path from a privacy-assessment perspective.

Accordingly, we encourage Dutch central public sector prospects and customers to engage with us to learn more about Google Cloud.

More broadly, we believe this outcome provides a strong foundation for public sector organisations across the Netherlands and beyond, to confidently evaluate and adopt Google Cloud, unlocking modernization and digital transformation securely.

This achievement builds upon our strong track record in the region, including the successful completion of the Dutch DPIA on Google Workspace. This previous success affirmed the safe use of Workspace across the Dutch public sector and educational institutions. Together, these assessments demonstrate Google's continued commitment to helping public sector organisations meet their privacy, security, and compliance requirements while benefiting from the innovation and scalability of Google Cloud.

Continued support for all customers on their compliance journeys

We welcome independent assessments that help strengthen trust, transparency, and accountability. The Dutch government's DPIA process represents an important example of constructive collaboration between public institutions, independent experts, and cloud providers to advance privacy protections.

Google Cloud remains committed to helping customers meet their compliance obligations while providing secure, transparent, and privacy-conscious cloud services.

We will continue investing in privacy-enhancing technologies, transparency initiatives, and customer controls to support organisations across Europe and around the world.

We know first-hand that conducting DPIAs can be a complex task, and we remain firmly committed to helping our customers navigate DPIAs with resources at our comprehensive DPIA Cloud Resource Center.

Beyond Static Prompts: Building Scale-Proof, Polymorphic Multi-Agent Systems with Google's ADK

Wed, 01 Jul 2026 14:00:00 +0000

As enterprise generative AI transitions from simple, conversational chatbots to autonomous multi-agent workflows, developers face a critical bottleneck: scale.

In a production environment, an enterprise agent often needs to navigate hundreds of heterogeneous data structures, dynamic business rules, and shifting API schemas. The standard blueprint relies on "Static Prompting"—pre-loading all potential JSON schemas, Pydantic classes, or tool definitions directly into the agent’s system instructions.

However, as your task complexity grows, this architecture breaks down. It leads to context window bloat, soaring token costs, and a sharp degradation in accuracy known as Attention Diffusion—where the model mistakenly mixes fields from dormant schemas into active requests.

To solve this issue, we need to decouple an agent's reasoning capabilities from its structural data requirements. This post introduces an architecture for Context-Aware Polymorphic Schema Validation, a design pattern that leverages a centralized metadata registry to dynamically inject context and enforce strict schema validation at runtime by using Google's Agent Development Kit (ADK) and Gemini Flash.

The Pitfalls of Static Agent Architectures

When managing structured inputs and outputs in high-cardinality enterprise environments, traditional LLM orchestration frameworks introduce severe operational friction:

Context Window Bloat & Latency Cascades: Standard architectures require all potential data schemas to be pre-loaded into the agent's initial prompt instructions. This "Static Prompting" creates massive context bloat, which directly drives up token costs, induces unnecessary operational latency, and degrades the model's reasoning density by crowding the focus window with irrelevant metadata.
Attention Diffusion in High-Cardinality Spaces: Large language models struggle to cleanly isolate highly similar data structures when contained within a single large prompt. In complex environments, agents frequently experience attention diffusion, mistakenly populating fields or enforcing validation rules from an inactive schema into an active production payload.
Synchronous Maintenance and Code Debt: Traditional approaches treat the system prompt (inference) and the guardrail (validation) as two separate, disconnected code silos. Because these live in isolated codebases, any slight modification to a business requirement necessitates manual, parallel updates to both the prompt structure and the validator code, creating high operational friction.
Nondeterministic Multi-Agent Handoffs: Multi-agent systems frequently lack a deterministic verification check before routing state. Sub-agents are often invoked without an automated mechanism verifying that the shared session state actually meets their specific structural prerequisites, resulting in "silent failures" where agents initialize with malformed context and have no autonomous recovery mechanism.

The Architecture: Just-in-Time Polymorphic Orchestration

Instead of expecting the LLM to hold every business rule in memory, this architecture treats schemas as externalized, discoverable metadata assets. The system splits the execution lifecycle into two clean phases: Context Discovery and Dynamic Validation.

1. Centralized Metadata Registry

All schemas are externalized out of the code and the prompt, and they're stored within a central registry (such as Cloud Storage) as high-density Schema Descriptor JSONs. Each descriptor contains the following:

Field Definitions: Semantic names and natural language descriptions.
Mapping Rules: Declarative logic that details how informal user inputs translate to downstream system parameters.
Polymorphic Validation Hooks: References to specific programmatic validation rules (like regex constraints and range boundaries) that are bound directly to the field metadata.

2. The Dynamic Discovery & Validation Loop

Instead of starting with a massive, 20,000-token prompt, the agent initializes with a lightweight, 200-token Discovery Prompt utilizing Google's ADK. The following lifecycle sequence details the exact transaction loop as the system transitions from initial user discovery to metadata enforcement:

The transaction loop shifts smoothly across four lifecycle phases to process input text:

Phase 1: Context Discovery (Steps 1–3): The orchestration agent kicks off with a minimal system prompt. It engages in a brief fallback loop with the user solely to distill their core intent (like identifying that the user requires a "Service Agreement") without holding any heavy schema constraints yet.
Phase 2: Metadata Resolution (Steps 4–6): After the intent is crystallized, the agent executes an automated tool call (load_descriptor) to fetch the isolated schema rules out of the Central Metadata Registry (Cloud Storage). Then the agent instantly overwrites the active session memory state with this highly specific metadata.
Phase 3: Metadata-Driven Assembly (Steps 7–14): The system enters an active evaluation loop. The agent evaluates data gaps, asks for a precise field (e.g., "Effective Date"), and then it pushes the user's raw conversational input directly to a separate Polymorphic Validator–a validation tool that runs on Cloud Run.
- If validation fails: A deterministic error code loops directly back to the agent to trigger conversational self-correction.
- If validation passes: The field is safely committed into the session's master JSON payload.
Phase 4: Finalization (Steps 15–16): Only when the cumulative master payload matches the strict metadata criteria with 100% compliance does the orchestrator release the state. The release triggers the secure downstream enterprise API payloads or it executes a clean multi-agent handoff.

The Design Pattern in Practice: Declarative Schema Factory

Building this architecture on Google Cloud relies on a declarative configuration pattern, removing structural rules from your core prompt engineering layers entirely:

code_block: <ListValue: [StructValue([('code', '// Example: Centralized Schema Descriptor JSON\r\n{\r\n "domain": "travel_expense",\r\n "fields": {\r\n "amount": {\r\n "type": "float",\r\n "description": "Total transaction amount in local currency",\r\n "validation_hook": "check_positive_bounds"\r\n },\r\n "receipt_id": {\r\n "type": "string",\r\n "description": "Alphanumeric system ID found on the receipt image",\r\n "validation_hook": "regex_match_expense_v2"\r\n }\r\n }\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4ee601c0>)])]>

Architectural Component Mapping

Multi-Agent Coordination (Google's ADK): Google's ADK manages the core multi-agent workflows, state transitions, and tool-calling infrastructure, which enables developers to programmatically intercept execution boundaries.
High-Density Inference Engine (Gemini 3 Flash): Gemini 3 Flash serves as the reasoning backbone. Its low latency, fast token processing speeds, and highly cost-effective execution costs make it the ideal model for running rapid, iterative context-switching loops without inflating token bills.
Externalized Storage Layer (Cloud Storage): Cloud Storage houses the library of JSON descriptors. The storage layer enables system administrators or business analysts to modify validation bounds or onboard completely new business domains instantly by uploading a file—requiring zero code deployment or application downtime.
Polymorphic Validation Hooks (Cloud Run functions): Isolated programmatic constraints live as decoupled serverless endpoints. When an asset field triggers a verification check, the orchestration middleware dynamically calls the targeted function mapped inside the registry descriptor.

Business and Operational Impact

Shifting from a static paradigm to a dynamic, decoupled schema architecture provides immediate advantages for enterprise production environments:

100% Reasoning Density: Because the agent's context window is never cluttered with irrelevant rules or alternate schemas, token consumption drops drastically, latency decreases, and hallucination rates fall to near zero.
Zero-Downtime Adaptability: Need to support a new product variant, an updated database field, or a shifting compliance rule? Simply upload a new or revised JSON descriptor to your central registry. The multi-agent system will adapt to the new business rules on its very next turn without a single line of code being redeployed.
Deterministic State Enforcement: By binding your prompt instructions directly to programmatic validation rules via the registry, you eliminate the risk of silent multi-agent failures. Outbound context payloads are systematically checked and corrected before hitting expensive enterprise applications.

Scaling LLM Inference: Multi-Node KV Cache Offloading with GKE & Managed Lustre

Wed, 01 Jul 2026 07:00:00 +0000

Significant contributors to this article include Sneha Aradhey, Software Engineer, Google Kubernetes Engine, and Michael MacDonald, Sr Software Engineer, Google Cloud Managed Lustre.

Enterprise production environments are shifting to distributed, multi-node architectures to serve long-context window lengths and agentic AI. As these workloads scale, KVCaches often outgrow local CPU RAM and host SSD cache tiers.

To handle this, some setups attempt to pool node-local storage into a distributed layer (such as multi-node pooled NVMe arrays). Pooling SSDs aggregates raw capacity and often leverages spare local drives, presenting clear advantages. However, there are some limitations: the approach requires the compute cluster to manage its own complex data distribution and cross-node replication.

An alternative is to offload the attention state to a dedicated, high-performance external parallel filesystem. We utilize Google Cloud Managed Lustre with the llm-d offloading stack as a cluster-wide decentralized attention cache tier, bypassing host-level capacity limits and eliminating the networking overhead of managing local pooled drives.

With this approach, we achieve efficiency at scale:

Google Cloud Managed Lustre enables over 50% TCO savings and reduces GPU-hour requirements for Llama-3.3-70B inference on a six-node A3 Mega cluster by nearly 60%. These gains are realized by offloading shared, prefilled KV caches to Lustre’s high-performance tier with a 95% cache hit rate.

Benchmark Configuration

Model: Llama-3.3-70B
Context Dynamics: Prompt length of 50,000 tokens, input question length of 256 tokens, and output length of 512 tokens.

Extension of Lustre KV Cache solution with CPU RAM offload

The Managed Lustre KV Cache offload architecture can be extended via integration of offload to CPU RAM. This hybrid approach significantly improves performance compared to CPU offload only, delivering approximately 40% improvement in Time to First Token (TTFT) and a 30% reduction in end-to-end latency, for Llama-3.3-70B inference.

User Guide

Architectural Components

GKE GPU Nodes: Dedicated accelerator resources provisioned exclusively for high-throughput model execution and tensor-parallel operations.
Managed Lustre: A shared, high-bandwidth parallel filesystem acting as a centralized external tier that caches prefilled attention states to eliminate redundant prefill computation.
PVC Evictor: A scalable, distributed garbage collection service that tracks file access patterns and automatically removes Least-Recently-Used (LRU) cache chunks to maintain healthy storage headroom.

Target Models

This guide provides two distinct, validated tracks for deployment depending on your model preference:

Qwen Series: Qwen/Qwen3.5-35B-A3B
Gemma 4 Architecture: google/gemma-4-31B-it

Architectural Diagram

Before You Begin

Before starting this deployment, ensure your Google Cloud project is properly configured:

Quota: Verify you have sufficient quota for the selected accelerators in your chosen region, as well as adequate general CPU, memory, and Managed Lustre quotas.
Validate Required IAM Permissions for Managed Lustre
Prepare your Environment to Connect to Managed Lustre: Complete the “Before You Begin” steps to enable APIs, set up environment variables, and set up your VPC.
- GKE Version: The Managed Lustre CSI driver is supported on GKE versions 1.33 or later. For the best experience and default port (988) usage, GKE version 1.33.2-gke.4780000 or later is recommended.

Overview of Required Steps

Create the GKE Cluster
Create the GPU Compute node pool
Provision Lustre storage
Deploy vLLM Serving Engine with Lustre
Deploy the PVC Evictor
Clean Up

1. Create the GKE Cluster

Create a rapid-channel GKE cluster with Workload Identity and all necessary CSI storage add-ons enabled (Lustre, GCSFuse and Persistent Disk).

code_block: <ListValue: [StructValue([('code', 'export CLUSTER_NAME="<INSERT CLUSTER NAME>"\r\nexport ZONE="<INSERT ZONE>"\r\nexport PROJECT_ID="<INSERT PROJECT>"\r\nexport NETWORK_NAME="<INSERT NETWORK>"\r\n\r\ngcloud container clusters create "$CLUSTER_NAME" \\\r\n --zone "$ZONE" \\\r\n --num-nodes "1" \\\r\n --network "${NETWORK_NAME}" \\\r\n --addons "HorizontalPodAutoscaling,HttpLoadBalancing,GcePersistentDiskCsiDriver,GcsFuseCsiDriver,LustreCsiDriver" \\\r\n --workload-pool "${PROJECT_ID}.svc.id.goog" \\\r\n --enable-managed-prometheus \\\r\n --enable-ip-alias \\\r\n --enable-shielded-nodes \\\r\n --shielded-integrity-monitoring \\\r\n --no-shielded-secure-boot \\\r\n --node-locations "$ZONE" \\\r\n --network="${NETWORK_NAME}" \\\r\n --gateway-api=standard'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d094640>)])]>

2. Create the GPU Compute Node Pool

Provision an GPU VM node pool ( e.g. a3-megagpu-4g, a4-highgpu-4g, etc.).

code_block: <ListValue: [StructValue([('code', 'gcloud beta container node-pools create gpu-vm nodepool \\\r\n --location="$ZONE" \\\r\n --cluster="$CLUSTER_NAME" \\\r\n --project="$PROJECT_ID" \\\r\n --accelerator="type=<INSERT GPU_ACCELERATOR_NAME>,count=<INSERT GPU_COUNT>,gpu-driver-version=LATEST" \\\r\n --machine-type="<INSERT GPU_COMPUTE_VM_MACHINE TYPE>" \\\r\n --num-nodes="<INSERT NODE_COUNT>" \\\r\n --enable-gvnic \\\r\n --no-enable-autoupgrade\r\n\r\n# Fetch cluster credentials\r\ngcloud container clusters get-credentials "$CLUSTER_NAME" --zone "$ZONE"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d094a00>)])]>

3. Provision Lustre Storage (Auto-provisioned)

Before deploying vLLM, you need to provision the Lustre storage. We use an auto-provisioned Lustre instance via a StorageClass and a PersistentVolumeClaim (PVC).

Create a file named lustre-pvc.yaml with the following content:

code_block: <ListValue: [StructValue([('code', 'apiVersion: storage.k8s.io/v1\r\nkind: StorageClass\r\nmetadata:\r\n name: lustre-class\r\nprovisioner: lustre.csi.storage.gke.io\r\nvolumeBindingMode: Immediate\r\nreclaimPolicy: Delete\r\nmountOptions:\r\n - localflock\r\nparameters:\r\n perUnitStorageThroughput: "<CHOOSE_PERFORMANCE_TIER>" # See options below.\r\n network: "<INSERT NETWORK_NAME>"\r\n---\r\napiVersion: v1\r\nkind: PersistentVolumeClaim\r\nmetadata:\r\n name: lustre-pvc\r\nspec:\r\n accessModes:\r\n - ReadWriteMany\r\n resources:\r\n requests:\r\n storage: <INSERT CAPACITY_GiB> # Range from 9000Gi to 84016000Gi, increments and ranges are Lustre tier-dependent.\r\n storageClassName: lustre-class'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d094eb0>)])]>

Notes: Performance tier options are “125”, “250”, “500”, and “1000”. Per-tier capacity ranges and increments can be found here.

Apply this manifest to provision the Lustre instance and observe provisioning:

code_block: <ListValue: [StructValue([('code', '# 1. Submit the file to the cluster (finishes instantly)\r\nkubectl apply -f lustre-pvc.yaml\r\n\r\n# 2. Watch the live provisioning stream until it says "Bound"\r\nkubectl get pvc lustre-pvc -w'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee47fb2c10>)])]>

4. Deploy vLLM Serving Engine with Lustre

Step 4a: Create the Hugging Face Access Secret

Before submitting the deployment manifest, you must provision your Hugging Face API token as a secure secret within the cluster.

Run the following command, replacing `<INSERT_HF_TOKEN>` with your token:

code_block: <ListValue: [StructValue([('code', 'kubectl create secret generic hf-token-secret \\\r\n --from-literal=token="<INSERT_HF_TOKEN>" \\\r\n --namespace=default'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee47fb2490>)])]>

Step 4b: Create the vLLM Deployment Manifest

This complete Kubernetes manifest deploys the vLLM engine, configures the llmd-fs-connector for high-performance KV-caching, and mounts your parallel Lustre storage (lustre-pvc).

Common Manifest (Choose between Qwen3.5 or gemma-4)

Replace example values between <> with appropriate values for your environment.

code_block: <ListValue: [StructValue([('code', 'apiVersion: apps/v1\r\nkind: Deployment\r\nmetadata:\r\n name: vllm-storage\r\n namespace: default\r\n labels:\r\n app: vllm-storage\r\nspec:\r\n replicas: 1\r\n selector:\r\n matchLabels:\r\n app: vllm-storage\r\n template:\r\n metadata:\r\n labels:\r\n app: vllm-storage\r\n spec:\r\n nodeSelector:\r\n cloud.google.com/gke-accelerator: nvidia-h100-80gb\r\n tolerations:\r\n - key: "nvidia.com/gpu"\r\n operator: "Exists"\r\n effect: "NoSchedule"\r\n securityContext:\r\n fsGroup: <YOUR_NON_ROOT_GID>\r\n runAsUser: <YOUR_NON_ROOT_UID>\r\n volumes:\r\n - name: lustre-storage\r\n persistentVolumeClaim:\r\n claimName: lustre-pvc\r\n - name: shm\r\n emptyDir:\r\n medium: Memory\r\n sizeLimit: "200Gi"\r\n containers:\r\n - name: vllm-storage\r\n image: vllm/vllm-openai:v0.23.0-cu129\r\n volumeMounts:\r\n - mountPath: /mnt/files-storage\r\n name: lustre-storage\r\n command:\r\n - "/bin/bash"\r\n args:\r\n - "-c"\r\n - |\r\n set -x\r\n export USER=vllm\r\n export LOGNAME=vllm\r\n pip install --user msgpack\r\n pip install \'llmd-fs-connector==0.23\' --extra-index-url https://llm-d.github.io/llm-d-kv-cache/simple/\r\n \r\n vllm serve <MODEL_NAME> \\ # google/gemma-4-31B-it OR Qwen/Qwen3.5-35B-A3B\r\n --download-dir /model/models \\\r\n --load-format auto \\\r\n --kv-transfer-config \'{\r\n "kv_connector": "MultiConnector",\r\n "kv_role": "kv_both",\r\n "kv_connector_extra_config": {\r\n "connectors": [\r\n {\r\n "kv_connector": "OffloadingConnector",\r\n "kv_role": "kv_both",\r\n "kv_connector_extra_config": {\r\n "cpu_bytes_to_use": 64424509440,\r\n "lazy_offload": true\r\n }\r\n },\r\n {\r\n "kv_connector": "OffloadingConnector",\r\n "kv_role": "kv_both",\r\n "kv_connector_extra_config": {\r\n "spec_name": "SharedStorageOffloadingSpec",\r\n "spec_module_path": "llmd_fs_backend.spec",\r\n "shared_storage_path": "/mnt/files-storage/llmd-kv-cache/",\r\n "threads_per_gpu": 32,\r\n "block_size": <BLOCK_SIZE> # 256 for gemma or 528 for Qwen3.5\r\n }\r\n }\r\n ]\r\n }\r\n }\' \\\r\n --distributed_executor_backend "mp" \\\r\n --port 8000 \\\r\n --max_num_batched_tokens 16384 \\\r\n --enable-chunked-prefill \\\r\n --max-model-len 32000 \\\r\n --gpu-memory-utilization 0.92 \\\r\n --tensor-parallel-size "4" \\\r\n --prefix-caching-hash-algo sha256_cbor \\\r\n --enable_prefix_caching \\\r\n --enforce-eager \\\r\n --no-disable-hybrid-kv-cache-manager\r\n env:\r\n - name: HUGGING_FACE_HUB_TOKEN\r\n valueFrom:\r\n secretKeyRef:\r\n name: hf-token-secret\r\n key: token\r\n # ... probes ...\r\n resources:\r\n requests:\r\n nvidia.com/gpu: "4"\r\n limits:\r\n nvidia.com/gpu: "4"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee47fb2cd0>)])]>

Note: Qwen-3.5 specifically requires a block size of 528 to avoid fragmentation, while Gemma 4 functions perfectly with the default 256.

Step 4c: Apply and Verify Deployment

To apply this manifest to your cluster, run:

code_block: <ListValue: [StructValue([('code', 'kubectl apply -n default -f vllm-lustre-deployment.yaml'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee47fb2df0>)])]>

Step 4d: Track Model Download Status

Because large models can take some time to download on first boot, track the initialization logs directly by streaming the container logs:

Bash

code_block: <ListValue: [StructValue([('code', 'kubectl rollout status deployment/vllm-storage'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee47fb2b80>)])]>

5. Deploy the PVC Evictor

PVC Evictor Overview

Architecture & Role

The llmd_fs_backend connector offloads KV-cache blocks to Lustre but does not natively delete old cache files. Over time, the cache will fill the shared filesystem. The PVC Evictor acts as an external garbage collector that continuously monitors disk usage and evicts least-recently-used (LRU) files to maintain healthy storage headroom.

Scaling & Sharding

The PVC Evictor supports sharding and can be scaled to multiple replicas to match the capacity and performance of your Lustre instance. As a rule of thumb, you should deploy 1 evictor replica for each 72 TB of Lustre capacity to distribute the eviction load effectively without overwhelming the metadata servers.

For large-scale deployments, the evictor can be configured to run with multiple shards. When running in multi-replica mode, the workload is partitioned across pods, with each pod managing a specific shard of the cache namespace. This prevents redundant metadata scans and race conditions.

High-Performance Resource Requirements

Running the evictor at high scale (e.g., with 16 parallel crawler processes) requires significant CPU and memory resources to handle the rapid scanning and queue management of millions of files. Ensure that the pods are provisioned with sufficient resources (e.g., 12 CPU requests and 8Gi Memory requests) and scheduled on appropriate node types (such as c4-standard-16).

PVC Evictor Deployment Steps

The PVC Evictor is deployed via Helm using the chart located in kv_connectors/pvc_evictor/helm.

Step 5a: Create a Dedicated Node Pool for the Evictor

Running the evictor at high scale requires significant CPU and memory. First, create a dedicated node pool using a high-performance machine type (such as c4-standard-16) to accommodate the 12 CPU and 8Gi memory requests needed per pod.

code_block: <ListValue: [StructValue([('code', '# Create a dedicated node pool for the PVC Evictor\r\ngcloud container node-pools create evictor-pool \\\r\n --location="$ZONE" \\\r\n --cluster="$CLUSTER_NAME" \\\r\n --project="$PROJECT_ID" \\\r\n --machine-type="c4-standard-16" \\\r\n --num-nodes="1"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee47fb25e0>)])]>

Step 5b: Install via Helm (High-Performance Configuration)

Deploy a scaled, high-performance evictor pool with 2 replicas to monitor lustre-pvc. This configuration uses 16 crawler processes per pod to handle massive file namespaces.

Note on Security Contexts: To allow the evictor pod to delete files created by vLLM, it must run with matching security context IDs. Ensure the placeholders <YOUR_NON_ROOT_GID> and <YOUR_NON_ROOT_UID> exactly match the non-root values used in the securityContext of your vLLM deployment to ensure shared POSIX file permissions.

code_block: <ListValue: [StructValue([('code', 'git clone --depth 1 https://github.com/llm-d/llm-d-kv-cache.git\r\ncd llm-d-kv-cache/kv_connectors/pvc_evictor\r\n\r\nhelm install pvc-evictor ./helm \\\r\n --namespace default \\\r\n --set replicaCount=1 \\\r\n --set config.numCrawlerProcesses=16 \\\r\n --set config.deletionBatchSize=5000 \\\r\n --set config.fileQueueMinSize=1000000 \\\r\n --set config.fileQueueMaxsize=2000000 \\\r\n --set config.fileAccessTimeThresholdMinutes=10 \\\r\n --set securityContext.container.runAsNonRoot=false \\\r\n --set pvc.name="lustre-pvc" \\\r\n --set config.cleanupThreshold=85.0 \\\r\n --set config.targetThreshold=70.0 \\\r\n --set config.cacheDirectory="llmd-kv-cache" \\\r\n --set securityContext.pod.fsGroup=<YOUR_NON_ROOT_GID> \\\r\n --set securityContext.container.runAsUser=<YOUR_NON_ROOT_UID> \\\r\n --set resources.requests.cpu=12 \\\r\n --set resources.requests.memory=8Gi \\\r\n --set resources.limits.cpu=15 \\\r\n --set resources.limits.memory=16Gi \\\r\n --set nodeSelector."cloud\\.google\\.com/gke-nodepool"=evictor-pool \\\r\n --set securityContext.pod.seLinuxOptions.level="s0:c0\\,c1"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d43dbe0>)])]>

Critical Parameters Explained:

replicaCount=2: Deploys 2 evictor pods. The Helm chart automatically configures sharding (totalShards=2) when multiple replicas are used.
config.numCrawlerProcesses=16: Runs 16 parallel crawler threads per pod to scan the filesystem rapidly.
config.deletionBatchSize=5000: Deletes files in batches of 5000 to reduce metadata overhead.
config.fileQueueMinSize & config.fileQueueMaxsize: Configures large memory queues (1M min, 2M max) to buffer files for deletion, matching the high crawler throughput.
config.fileAccessTimeThresholdMinutes=10: Aggressively evicts files that haven't been accessed in the last 10 minutes when the cleanup threshold is triggered.
securityContext.container.runAsNonRoot=false: Required if the evictor needs root-like permissions to manage/delete files across different user ownerships on the shared storage.
resources.requests & limits: Allocates 12-15 CPUs and 8-16Gi of memory per pod to ensure the high number of crawler processes do not get CPU-throttled or run Out-Of-Memory (OOM).

Step 5c: Verify and Monitor

code_block: <ListValue: [StructValue([('code', '# Verify pod status\r\nkubectl get pods -l app.kubernetes.io/name=pvc-evictor -n default'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d43d400>)])]>

Step 6: Clean Up

Because this deployment provisions significant and high-cost hardware, be sure to clean up your environment when you are done to avoid unnecessary charges.

Bash

code_block: <ListValue: [StructValue([('code', 'helm uninstall pvc-evictor && kubectl delete -f vllm-lustre-deployment.yaml\r\n\r\nkubectl delete pvc lustre-pvc\r\n\r\n# Delete the cluster (this also deletes the associated node pools)\r\ngcloud container clusters delete "$CLUSTER_NAME" \\\r\n --zone "$ZONE" \\\r\n --project "$PROJECT_ID" \\\r\n --quiet\r\n\r\n# Note: The Lustre StorageClass reclaimPolicy is set to Delete, \r\n# so destroying the PVC or Cluster will automatically clean up the underlying Lustre storage.'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d43daf0>)])]>

Appendix: Reference Configuration for Llama-3.3-70B Benchmark

The following configuration is a representation of the deployment manifest used to generate the Llama-3.3-70B benchmark results referenced in this post. It is provided for completeness and transparency.

Note: This configuration utilizes an earlier iteration of the software stack (vLLM v0.15.0) and specific infrastructure flags that were active in the benchmarking environment at the time the data was collected.

code_block: <ListValue: [StructValue([('code', 'apiVersion: apps/v1\r\nkind: Deployment\r\nmetadata:\r\n name: vllm-storage\r\n namespace: default\r\n labels:\r\n app: vllm-storage\r\nspec:\r\n replicas: 1\r\n selector:\r\n matchLabels:\r\n app: vllm-storage\r\n template:\r\n metadata:\r\n labels:\r\n app: vllm-storage\r\n spec:\r\n volumes:\r\n - name: lustre-storage\r\n persistentVolumeClaim:\r\n claimName: lustre-pvc\r\n - name: shm\r\n emptyDir:\r\n medium: Memory\r\n sizeLimit: "200Gi"\r\n - name: kv-store-disk\r\n persistentVolumeClaim:\r\n claimName: lustre-pvc\r\n containers:\r\n - name: vllm-storage\r\n image: vllm/vllm-openai:v0.15.0\r\n command:\r\n - "/bin/bash"\r\n args:\r\n - "-c"\r\n - |\r\n pip install https://raw.githubusercontent.com/kfirtoledo/llm-d-kv-cache-manager/connector/kv_connectors/llmd_fs_backend/wheels/llmd_fs_connector-0.1.0-cp312-cp312-linux_x86_64.whl; \\\r\n mkdir -p /tmp/prometheus_metrics;\r\n export PROMETHEUS_MULTIPROC_DIR=/tmp/prometheus_metrics; \\\r\n vllm serve meta-llama/Llama-3.3-70B-Instruct \\\r\n --download-dir /model/models \\\r\n --load-format runai_streamer \\\r\n --kv-transfer-config \'{ \r\n "kv_connector": "OffloadingConnector", \r\n "kv_role": "kv_both",\r\n "kv_connector_extra_config": {\r\n "spec_name": "SharedStorageOffloadingSpec",\r\n "spec_module_path": "llmd_fs_backend.spec",\r\n "shared_storage_path": "/mnt/files-storage/llmd-kv-cache/",\r\n "block_size": 1024,\r\n "threads_per_gpu": "64"\r\n }\r\n }\' \\\r\n --distributed_executor_backend "mp" \\\r\n --port 8000 \\\r\n --max_num_batched_tokens 16384 \\\r\n --enable-chunked-prefill \\\r\n --tensor-parallel-size 8 \\\r\n --enable_prefix_caching \\\r\n --gpu-memory-utilization 0.9\r\n env:\r\n - name: HUGGING_FACE_HUB_TOKEN\r\n valueFrom:\r\n secretKeyRef:\r\n name: hf-token-secret\r\n key: token\r\n - name: VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS\r\n value: "3000"\r\n - name: PYTHONHASHSEED\r\n value: "123"\r\n ports:\r\n - containerPort: 8000\r\n resources:\r\n limits:\r\n nvidia.com/gpu: "8"\r\n requests:\r\n cpu: "200"\r\n memory: 1024G\r\n ephemeral-storage: 5120Gi\r\n nvidia.com/gpu: "8"\r\n volumeMounts:\r\n - name: lustre-storage\r\n mountPath: /model\r\n - mountPath: /root/.cache/huggingface\r\n name: lustre-storage\r\n subPath: huggingface-cache\r\n - name: shm\r\n mountPath: /dev/shm\r\n - mountPath: /mnt/files-storage\r\n name: kv-store-disk\r\n # ... probes omitted for brevity ...'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d43d6a0>)])]>

Conversational analytics in BigQuery brings trusted agentic reasoning to everyone

Tue, 30 Jun 2026 18:00:00 +0000

Businesses run on fast decisions, but the teams who hold the answers are often buried under a backlog of routine requests, leaving users waiting in line for insights they need now. Today, we are bringing Conversational Analytics in BigQuery to general availability, so both business and technical teams can query data, run multi-step analyses, and generate visual reports using natural language, right where the data lives. With this release, Conversational Analytics in BigQuery now delivers an agent that behaves like an analyst who knows your business, thinks before it answers, and stands behind its work. Built on Google’s latest Gemini models and BigQuery’s secure, governed foundation, it brings that trusted analyst to everyone in your organization.

Fig 1. Conversational Analytics in BigQuery

Conversational analytics for enterprise data

BigQuery’s conversational capabilities are built-in and available for use instantly, with no setup required.

For deeper, more consistent insights, data professionals can author specialized agents grounded in the exact sources that matter, from projects, datasets, and tables to views, graphs, and user-defined functions. And because your data rarely lives in one place, Conversational Analytics reaches beyond native BigQuery tables to Lakehouse-managed Apache Iceberg tables and cross-cloud Lakehouse sources like Databricks Unity, AWS Glue, SAP and Salesforce, so you can break down data silos and analyze data across clouds from a single conversation.

As a data practitioner, you work with Conversational Analytics right inside BigQuery Studio and Data Canvas, and publish the agents you build to Gemini Enterprise, Data Studio, or your own application through the Conversational Analytics API, putting them in the hands of business users wherever they work.

“At MoneySuperMarket, BigQuery Conversational Analytics has changed how our teams get to insight. Analysis that used to take weeks can now be done in minutes, saving our financial analysts around half a day each week. By making analysis more self-serve, we’re helping teams create faster insight to support better product and commercial decision-making.” - Suzie Millar, Head of Data, Mony Group

Engineered trust and explainability

Accuracy in Conversational Analytics is by design, not aspirational: every agent is grounded in your business context, not a model's assumptions. That context comes from the Knowledge Catalog (glossaries, profile scans, and context bundles), BigQuery Graph for multi-hop queries, and your own verified queries and custom agent instructions. With the new Open Knowledge Format, the wiki your team already maintains can feed straight into Knowledge Catalog. At query time, Conversational Analytics leverages existing embeddings of your column values, generated by AI.GENERATE_EMBEDDINGS, to match your question to the right data, so asking about "Texas" finds rows stored as "TX."

Grounding only earns trust if the user can see it. So every answer is inspectable, providing:

Visible thinking steps: Review the agent's step-by-step reasoning and the exact SQL it generates before it returns an answer.
Context citations: See the precise sources behind every response, including tables, schema definitions, verified queries, and glossary terms used to calculate it.
Proactive disambiguation: When a prompt is vague, the agent asks targeted clarifying questions instead of guessing.
Long-term memory: The agent remembers what your terms and questions mean, so you don't have to disambiguate the same thing twice.

Fig 2. Generating answers that you can trust

Security and governance by design

One common barrier to scaling AI is governance. Reaching tens of thousands of users requires rigorous security, governance, and transparent cost controls. Conversational Analytics inherits BigQuery's governance model, so users only query data they are authorized to see and every query is logged for auditing within the BigQuery compliance framework. On top of that baseline, it supports Access Transparency (AxT), Customer-Managed Encryption Keys (CMEK), Private IP, and VPC Service Controls, and now guarantees data residency for data at rest and for ML processing within EU and US multi-region endpoints.

For your most engaged users, we also deliver the operational controls that scale demands: Configure Google Cloud-native cost controls so no user or project exceeds its allotment, cap an agent's maximum query size in bytes, and track usage through BigQuery labels on jobs.

Fig 3. Agent Observability and Monitoring

The power of BigQuery AI, in plain language

The agent doesn't just retrieve rows, but calls BigQuery's AI functions for you, turning advanced analysis into a question you can ask in plain language.

Find the "why," not just the "what": Ask what drove a change and the agent runs root-cause analysis with AI.KEY_DRIVERS, surfacing the exact segments behind the move.
See what's coming: Move past historical reporting by triggering AI.FORECAST and AI.DETECT_ANOMALIES right in the chat to project trends and flag outliers, with no model to build or manage.
Query your entire data estate: With object tables, the agent reasons over relational data and unstructured files together, PDFs, images, logs, and video, so a single conversation spans your whole estate.

Fig 4. Conversational Analytics leverages BigQuery AI functions

From answering questions to running the investigation

Conversational Analytics agents are moving from human-scale reactive analysis to agent-scale proactive action. You're no longer limited to asking a question and waiting for the answer.

Deep-dive mode: If you ask ‘Why a metric moved?’ the agent will build its own analytical plan, mapping the critical questions, working through a full multi-step investigation with no manual SQL, and minimizing analytical blind spots. The result is a comprehensive report you can download and share.

Fig 5. Deep Dive mode in Conversational Analytics

Agentic workflows: Deploy autonomous agents that monitor your data, reason over events, run multi-step workflows on a schedule, and deliver insights straight to your chat. You can set up a Monday-morning business report or daily anomaly detection across key metrics, each with a custom directive so they investigate only what you care about.

Fig 6. Scheduling Conversational Analytics agent workflows

Start talking to your data today

General availability of Conversational Analytics in BigQuery marks an official exit from the static dashboard era. By embedding Gemini’s deep cognitive reasoning directly into the data warehouse, we are enabling a self-managing environment that transforms raw data into active, corporate knowledge. This delivery is a key component of the Agentic Data Cloud, providing a true system of action that moves past retrospective reporting, incorporates security and governance by design and is engineered for enterprise trust.

If you are ready to get started, learn more from our documentation, reach out to your Google Cloud account representative, or get started in BigQuery Studio today to build and deploy your first agent.

What Google Cloud announced in AI this month

Tue, 30 Jun 2026 16:00:00 +0000

Editor’s note: Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more.

Our main focus in June was helping your teams build, scale, and secure AI. Today, we’re sharing a fresh roundup of updates designed to help you run smarter, more secure applications while keeping everything under your control.

We even shared a cool virtual shopping demo at Cannes to show how retailers can make product discovery more exciting. Let’s dive in!

Top announcements

Introducing the Open Knowledge Format: We introduced the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. This is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need.
Collaboration with Apple on its expanded Private Cloud Compute (PCC) systems: Our collaboration with Apple is built on a foundation of deep commitment to privacy that leverages Google Cloud's security and privacy technologies. At the heart of this collaboration is our Confidential Computing portfolio and our Titanium security architecture.
Claude Fable 5: Available on Google Cloud: Claude Fable 5, Anthropic’s latest frontier model, is now generally available on Google Cloud. This launch is the latest proof point of our ongoing commitment to bring the industry's latest models straight to our Agent Platform.
Cloud Atelier: How Gemini Enterprise is helping restyle the retail playbook: This year at Cannes, we showcased Cloud Atelier — a destination-based, virtual shopping experience that highlights how retail brands can turn this classic dilemma into an exciting moment of product discovery.

Thought leadership (editor’s pick):

How Google Cloud Security uses AI internally: To counter machine-speed, AI-driven threats, we’ve worked hard to transition Google Cloud’s security posture to an autonomous, proactive model. By embedding specialized AI agents directly into our software development lifecycle (SDLC), we’ve created automated guardrails that protect code at a scale and speed unreachable by human teams — and we’re taking steps to make those same guardrails widely available.
The 4 lessons that guided AI Threat Defense: We introduced Chris Betz as the new CISO of Google Cloud. For his first Cloud CISO Perspectives, Chris shares four key lessons we learned about using AI to the defender’s advantage while building AI Threat Defense.

News you can use:

5 lessons from red teaming AI applications: To help you build AI securely, Mandiant has developed a proactive, risk-based approach centered on the Good AI Assessment (GAIA) Top 10, outlined in our new report, Secure Development of Generative AI Applications: A Proactive Approach.
How to unlock true ROI in software development – a deep dive into the latest DORA research: To help you evaluate the costs and business benefits of AI, we recently shared the DORA: ROI of AI-assisted software development report. This research offers a practical approach to help your team work through early adoption challenges, align engineering plans, and drive business growth.
Agent Factory Recap: 100X engineering with AI agents in Google Antigravity 2.0: In this episode of the Agent Factory, Shir Meir Lador, Head of AI Engineering, Google Cloud Developer Relations, sat down with Rody Davis, one of Google’s top agentic engineers. They dive into the massive shift from traditional IDEs to agent-first platforms, the reality of code reviews in an AI-driven world, and how to use "skills" to perform at a 100X level.

Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.

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May

We’ve had a busy month! Between announcing Gemini Spark and Gemini 3.5 at Google I/O – and unveiling Google AI Threat Defense, our latest AI-powered cybersecurity solution, we had a lot to share with Google Cloud customers. Keeping up with the latest news takes time, so we gathered the most important announcements, thought leadership, and technical guides in one place to help you quickly catch up.

To learn more about our I/O announcements, here’s everything you need to know for Google Cloud customers, and top news for startups.

Top announcements

Introducing Google AI Threat Defense to help you outpace the adversary: Google Cloud is introducing a comprehensive AI-powered cybersecurity solution — Google AI Threat Defense — an always-on autonomous security platform. Learn more here.

Gemini 3.5: Our latest family of models combines frontier intelligence with action – starting with Gemini 3.5 Flash.
Gemini Omni: Our new model is a leap forward in world understanding, multimodality, and editing, letting you generate any output from any input, starting with video.
Google Antigravity: Google Antigravity’s expanded capabilities and new integration with Agent Platform bring agentic development to your entire organization.
Gemini Spark: For Gemini Enterprise and Workspace customers, Gemini Spark is your 24/7 personal AI agent that helps you work more efficiently by autonomously taking action on your behalf, under your direction.
Google Workspace: Google Pics, our new image generation and editing tool, and new voice features in Gmail, Docs and Keep, help reimagine how you work.
Managed Agents API on Agent Platform: Allows developers to build and run custom agents inside secure, Google-hosted environments that seamlessly integrate with Agent Platform.
CodeMender: A powerful AI security agent provided through Agent Platform, CodeMender can help find and fix vulnerabilities in your code.

Nano Banana 2 and Nano Banana Pro are generally available: Available today via Gemini Enterprise Agent Platform, organizations are already putting the models to work. Learn more here.

Thought leadership (editor’s pick):

Cloud CISO Perspectives: How Google + Wiz changes multicloud strategy for CISOs: Vinod D’Souza, director, Office of the CISO, shares highlights from his RSA Conference fireside chat with Anthony Belfiore, chief strategy officer, Wiz. While threat actors have seen gains from the adversarial misuse of AI, Google and Wiz are tackling these challenges head-on by combining Wiz's deep cloud telemetry with Google's world-class AI and quantum research to help CISOs and their organizations meet the needs of the agentic enterprise era. Read more here.

News you can use:

What Google I/O '26 means for developing agents on Google Cloud: Dig deep into how Gemini Enterprise Agent Platform and the new developer tools shared at I/O fit together, unpack the spectrum of choice for building, and share what we’d actually try first. Learn more here.

Five must-have guides to move agents into production with Gemini Enterprise Agent Platform: Here is a look back at our five-part series covering the architecture patterns and best practices you need to move your agents into production. Learn more here.
How to build an AI-ready security program for the public sector: From industrial control systems to decades-old municipal databases, here’s our CISO guidance to prep AI-ready security programs for the public sector. Learn more here.

April

We hosted Google Cloud Next in Las Vegas on April 22, announcing incredible innovations from Gemini Enterprise Agent Platform to our eight-generation TPUs. We also expanded the Gemini Enterprise app in collaborative ways – now, with new features like Projects, you can work side-by-side with your agents and colleagues.

If you missed the livestream, take a look at our Day 1 recap. It’s been incredible to see how customers have been applying AI in thousands of ways — so far, we’ve counted more than 1,300 examples.

Top announcements

1. Gemini Enterprise Agent Platform: Our new, comprehensive platform to build, scale, govern, and optimize agents. Moving forward, all Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform, rather than as a standalone service, to power the next generation of agent development.

The platform is designed around four core pillars — build, scale, govern, and optimize — that allow teams to collaborate seamlessly. Learn more about Agent Platform here.

2. Gemini Enterprise app has all the key components to let teams discover, create, share, and run AI agents in a single environment. At Next ‘26, we introduced several new capabilities in the Gemini Enterprise app:

Agent Designer uses the same no-code agent designer experience of Agent Platform and lets employees build sophisticated schedule- and trigger-based agents using any enterprise connector. It gives you a virtual flowchart of your agent, allowing you to inspect, test, and approve workflows, ensuring total transparency for executing critical business processes.
Long-running agents are designed to execute complex business processes. They can work autonomously in secure cloud sandboxes, giving agents the ability to orchestrate business logic, write code to build custom tools, and complete multi-step work like reconciliation activities or sales prospect sequencing — without needing constant prompting.
Inbox in Gemini Enterprise provides a central location to monitor, guide, and help manage all of your agent activity, including your long-running agents. Notifications are intuitively categorized into actionable groups like "Needs your input," "Errors," and "Completed.”
Projects create a dedicated space where the agent’s memory is confined to the files and conversations your team adds. By connecting it to data sources including Google Drive, NotebookLM, and Google Group Chats, the agent becomes an expert on a specific topic and can provide team members daily briefings or status updates without digging through months of documents.
Skills create simple shortcuts using an “@” mention for repetitive tasks such as applying brand guidelines, formatting a report, and accessing specific data.
Canvas gives our customers an interactive editor directly within Gemini Enterprise. It allows teams to easily create and edit Docs and Slides, and even export to Microsoft 365 files, within the same experience.
Agent Gallery provides access to third-party agents from partners like Adobe, Atlassian, Lovable, and ServiceNow, and is adding more third-party connectors for Asana, Mailchimp, Workday, and more. These integrations enable your agents to retrieve data and execute tasks with your systems-of-record.

3. AI Hypercomputer: Designed specifically for demanding AI workloads, our AI Hypercomputer is an advanced, purpose-built architecture that unites performance-optimized hardware for compute, storage, networking, open software and machine learning frameworks — as well as flexible consumption models — into a single, integrated system. We are announcing innovations at every layer of the AI Hypercomputer:

TPU 8t, optimized for training, uses breakthrough Inter-Chip Interconnect (ICI) technology to scale up to 9,600 TPUs and 2 PB of shared, high-bandwidth memory in a single superpod. It achieves 3x the processing power of Ironwood and delivers up to 2x more performance/Watt.
TPU 8i, optimized for inference, uses our new Boardfly topology to directly connect 1,152 TPUs in a single pod. It features 3x more on-chip SRAM compared to previous versions to host larger KV caches entirely on-silicon and integrates a specialized Collectives Acceleration Engine. Taken together, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, enabling millions of concurrent agents to run cost-effectively.

4. The Agentic Data Cloud: A new data architecture built for the speed and scale of agentic AI. The Agentic Data Cloud delivers an AI-native architecture, allowing agents to perceive, reason, and act on your behalf in real-time, including:

Cross-Cloud Lakehouse, standardized on Apache Iceberg, is our Lakehouse that enables you to leave your data in AWS or Azure (coming later this year) while querying it instantly — without the friction of vendor lock-in or the cost of data movement
Knowledge Catalog constructs a unified, dynamic context graph of your entire business enabling you to ground agents in all of your business data and semantics. With Smart Storage and the Object Context API, files in Google Cloud Storage are instantly tagged and enriched with metadata before an agent touches them. Then our Knowledge Engine uses Gemini to autonomously tag, define logic and instantly map complex relationships across your entire enterprise, providing the semantic definition your agents have been missing.

5. Protecting the agentic enterprise: Security built for the AI era. Our full-stack AI approach, from the chips to the models, gives you a competitive advantage with better integration and velocity to help protect customers. Not only can Google action insights from the world’s largest threat observatory and Mandiant frontline experts, but we also bring cutting-edge insights and breakthroughs from Google DeepMind, to help make your platforms more secure.

Agentic defense: Three new agents in Google Security Operations can help hunt threats, engineer detections, and provide context on third parties. You can build your own security agents with remote Google Cloud model context protocol (MCP) server support for Google Security Operations, now generally available. You can also access the MCP server client directly from the Google Security Operations chat interface, available in preview.
Protecting AI and cloud apps across any infrastructure with Wiz: Newly expanded AI coverage helps build secure agents across clouds and AI studios. New AI-Bill of Materials in development tools can help secure AI-generated code and mitigate the risk of shadow AI. Learn more.
Securing agents and the agentic web: Model Armor can integrate with Agent Gateway, and new Agent Identities provide more layers of defense against shadow AI. Google Cloud Fraud Defense, the next evolution of reCAPTCHA, offers agent-specific capabilities that can help secure the agentic web as well as the entire user and customer journey.
Trusted Cloud: We’re simplifying permissions with modern IAM, and advancing Google Cloud security with new capabilities in Security Command Center plus new innovations in data and network security.
New partner-supported workflows for Google Security Operations: This new robust cohort of partner integrations includes partners developing their own agentic security operations centers (SOCs).

You can catch up on all our security announcements from Next ‘26 here.

News you can use

Guide to prompting Gemini 3.1 Flash TTS (text-to-speech): The new TTS model introduces a high level of controllability by allowing you to steer the delivery using more than 200 audio tags. We'll share how to get strong results from the model, whether you are building accessible gaming soundtracks, banking systems, or audiobooks. Learn more about the model here.
Ultimate prompting guide for Lyria 3 models: Lyria 3, Google's family of music-generation models, is designed to give you granular control over vocals, instrumentation, and arrangement. So we spent weeks testing against every musical genre and use case we could imagine. We put together this guide to share exactly what we learned and how you can get the best results.
How to find the sweet spot between cost and performance: This guide will walk you through Google Cloud's flexible gen AI infrastructure options, showing you how to find that sweet spot on the efficient frontier between cost and performance. We'll start with the foundational pay-as-you-go (PayGo) models and then explore how to layer on more specialized options to build a robust and cost-effective gen AI strategy.
Essential AI and cloud security now on by default: To support the next generation of AI innovators, we are offering on by default essential AI security and cloud security in Security Command Center Standard.
Securing AI inference on GKE with Model Armor: Here’s how to secure AI inference on Google Kubernetes Engine with Model Armor and high-performance storage.
Cloud CISO Perspectives: AI, security, and the workforce of the future: You can’t bring traditional security to an AI fight, so how do we defend against AI-powered attacks, boost defenders with AI, and secure AI use? Drop in on this RSA Conference fireside chat between Francis deSouza, Google Cloud COO and President, Security Products, and Nick Godfrey, senior director, Office of the CISO.

March

March was a busy month for our AI teams. We launched Gemini Embedding 2, rolled out a highly cost-effective Veo 3.1 Lite model, and officially welcomed the Wiz team to Google Cloud to help redefine security in the AI era.

Alongside these launches, we created comprehensive guides to help you get the most out of these models, from prompting formulas for Nano Banana 2, to practical advice for optimizing your TPU training. Here’s a quick look at the latest news and resources to help your team build what’s next.

Top hits:

Gemini Embedding 2: Our first natively multimodal embedding model: Gemini Embedding 2 is our first natively multimodal embedding model that maps text, images, video, audio and documents into a single embedding space, enabling multimodal retrieval and classification across different types of media — and it’s available now in public preview.
Build with Veo 3.1 Lite, our most cost-effective video generation model: This model empowers developers to build high-volume video applications, at less than 50% of the cost of Veo 3.1 Fast, but with the same speed. This rounds out the Veo 3.1 model family, giving developers flexibility based on needs. For Cloud customers, it’s now available on Vertex AI.

Here’s a fun bonus: Check out our ultimate prompting guide for Veo 3.1 to get started.

Welcoming Wiz to Google Cloud: Redefining security for the AI era: Google has completed its acquisition of Wiz, a leading cloud and AI security platform. The Wiz team will join Google Cloud, and we will retain the Wiz brand. With the addition of Wiz, we will provide customers with a comprehensive platform to secure their cloud and hybrid environments, as well as accelerate threat prevention, detection, and response.
Gemini 3.1 Flash Live: Making audio AI more natural and reliable: We’ve improved 3.1 Flash Live’s overall quality, making it more reliable for developers and enterprises to build voice-first agents that can complete complex tasks at scale. On ComplexFuncBench Audio, a benchmark that captures multi-step function calling with various constraints, it leads with a score of 90.8% compared to our previous model.

News you can use:

The ultimate Nano Banana prompting guide: This is a must-read for anyone working with Nano Banana. We spent weeks testing Nano Banana 2 and Nano Banana Pro against every use case we could imagine to test its limits. We put together this guide to share exactly what we learned and how you can get the best results. Here’s an example formula: [Reference images] + [Relationship instruction] + [New scenario]

A developer’s guide to training with Ironwood TPUs: In this guide, we hear from Lillian Yu, CPA, CA , Product Strategy and Operation, and Liat Berry, Product Manager, on five strategies within the JAX and MaxText ecosystems designed to help developers refine training efficiency and hit peak performance on Ironwood hardware.
How to build production-ready AI agents with Google-managed MCP servers: In this guide, we anchor on a specific example. Cityscape is a demo agent built with Google's Application Development Kit (ADK) that turns a simple text prompt — like "Generate a cityscape for Kyoto" — into a unique, AI-generated city image. Check out the guide to learn more.

February

In February, we’re giving developers more reasoning power with Gemini 3.1 Pro and Claude 4.6, and faster creative scaling with Nano Banana 2. We’re also opening up new training programs and step-by-step guides to help you tackle the hardest parts of the AI lifecycle, from capacity planning to mounting defenses against AI-powered attacks.

Here’s a rundown of our latest news, tools, and resources to help you build what’s next.

Top hits

Pro-level image generation gets faster and more accessible with Nano Banana 2: To build creative that stands out, you need models that naturally integrate into your workflows and scale with ease. Check out our blog to see how this comes to life (and how customers are putting the model to work).

Introducing Gemini 3.1 Pro on Google Cloud: Gemini 3.1 Pro is a clear step forward in reasoning, designed to solve tougher problems, giving you the reasoning depth your business needs. 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.
Announcing Claude Opus 4.6 and Claude Sonnet 4.6 on Vertex AI: Now generally available on Vertex AI, explore our sample notebook to get started and visit our documentation for comprehensive pricing and regional availability details.
New AI threats report: Distillation, experimentation, and integration: John Hultquist, chief analyst, Google Threat Intelligence Group, details what security leaders should know from our newest AI threat report on experimentation, integration, and distillation attacks.

News you can use

A developer's guide to production-ready AI agents: To help developers work through these challenges, we've published a collection of guides covering the full agent lifecycle. These resources first appeared during Kaggle’s 5 days of AI Agents Intensive, and they’ve proven so popular and useful, we wanted to make sure a wider audience had access, as well.
Gemini Enterprise Agent Ready (GEAR) program now available: We opened the Gemini Enterprise Agent Ready (GEAR) learning program to everyone. As a new specialized pathway within the Google Developer Program, GEAR empowers developers and pros to build and deploy enterprise-grade agents with Google AI.
Your guide to Provisioned Throughput (PT) on Vertex AI: Check out this deep-dive blog designed to show you the resources available to you today on Vertex AI, and how you can get started capacity planning.
How AI can boost defenders, from defense in depth to the cyber kill chain (Q&A): We know that defenders are also developing powerful AI tools, but what’s still unknown is what it could mean for enterprise software ownership if companies have to constantly mount AI-directed defenses at AI-powered attacks?

Janurary

We used to have to learn the language of computers. In 2026, they’re learning ours.

We kicked off the year by exploring the future of agentic commerce, where AI agents navigate the web to find and buy products for us. Our leaders call this the "invisible shelf" — a world where commerce isn't tied to a specific website. To make this reality scalable, we announced the Universal Commerce Protocol (UCP), a shared language that allows agents and retailers to understand each other.

We brought that same fluency to our creative and technical tools:

Updates to Veo 3.1 allow creators to use simple inputs — like reference images — to generate precise, mobile-ready video.
Natural language queries: With Comments to SQL in BigQuery, we’re removing the language barrier to data. Engineers can now write queries by describing their intent in natural language, prioritizing the question over the code.

Let’s dive in.

Top hits

1. Gemini Enterprise for Customer Experience (CX): Specifically built for agentic retail, this platform transforms fragmented search, commerce and service touch points into one seamless journey — whether you need a shopping assistant, a support bot, agentic search or help with merchandising.

2. We announced Universal Commerce Protocol (UCP): A new open standard for agentic commerce that works across the entire shopping journey — from discovery and buying to post-purchase support. UCP establishes a common language for agents and systems to operate together across consumer surfaces, businesses and payment providers. So instead of requiring unique connections for every individual agent, UCP enables all agents to interact easily. UCP is built to work across verticals and is compatible with existing industry protocols like Agent2Agent (A2A), Agent Payments Protocol (AP2) and Model Context Protocol (MCP).

3. We updated Veo 3.1, including improvements to Ingredients to Video and Portrait mode: Veo is getting more expressive, with improvements that help you create more fun, creative, high-quality videos based on ingredient images, built directly for the mobile format. This includes:

Improvements to Veo 3.1 Ingredients to Video, our capability that lets you create videos based on reference images.
Native vertical outputs for Ingredients to Video (portrait mode) to power mobile-first, short-form video creation.
State-of-the-art upscaling to 1080p and 4K resolution 1 for high-fidelity production workflows.

These updates are launching in the Gemini app, YouTube, Flow, Google Vids, the Gemini API and Vertex AI.

4. Vibe querying with comments-to-SQL: Crafting complex SQL queries can be challenging. Often, engineers simply want to express their data needs in plain English directly within their SQL workflow. That’s why we’re introducing Comments to SQL in BigQuery. This feature makes writing queries using natural language – ‘vibe querying’ – a reality. Learn more in the blog.

News you can use

Mastering Gemini CLI: Your complete guide from installation to advanced use-cases: We’ve teamed up with DeepLearning.ai and are excited to announce a free course – Gemini CLI: Code & Create with an Open-Source Agent. This course isn’t just for developers; we dive into practical use cases for various tasks such as data analysis, content creation, and personalized learning.
How Google SREs use Gemini CLI to solve real-world outages: In this article, we’ll delve into real scenarios that Google SREs are solving today using Gemini 3 (our latest foundation model) and Gemini CLI—the go-to tool for bringing agentic capabilities to the terminal.
Getting started with Gemini 3: Deploy your first Gemini 3 app to Google Cloud Run: In this blog, we will show you how to vibe code your first app—which leverages the Gemini 3 Flash Preview model and deploy it as a publicly accessible URL on Google Cloud Run. Google AI Studio lets you go from idea to app quickly by using natural language to generate fully functional apps using the power of Gemini 3.
Practical guidance: Building with the Secure AI Framework (SAIF) on Google Cloud: We know that security and data privacy are the top concern for executives when evaluating AI providers, and security is the top use case for AI agents in a majority of industries. To help you build AI boldly and responsibly, here’s our guide to developing AI with the Secure AI Framework (SAIF) on Google Cloud.
The truths about AI hacking that every CISO needs to know (Q&A): How will AI boost threat actors? And what can chief information security officers do about it? Google’s Heather Adkins, vice-president, Security Engineering, explores how securing the enterprise is about to change.

Modernizing financial services with deployment freedom and transformational AI with AlloyDB Omni

Tue, 30 Jun 2026 16:00:00 +0000

The financial services industry (FSI) operates under a unique set of non-negotiable requirements: the need for strict regulatory compliance, sub-millisecond transactional speeds, and security that verges on impenetrable. Historically, organizations have met these standards by relying on brittle, proprietary database systems, leaving them with massive technical debt, operational overhead, and vendor lock-in.

At the same time, financial services companies are facing a series of daunting challenges:

The licensing trap and technical debt: Decades of reliance on legacy commercial databases have left institutions with skyrocketing maintenance costs and restrictive licenses that refuse to scale. In fact, a global investment bank might find that over 70% of its IT budget is swallowed up by decades-old COBOL core banking systems and siloed ledger databases—leaving virtually no capital to develop the real-time, AI-driven fraud detection tools their clients are actively demanding.
The tug of war between sovereignty and innovation: Emerging regulations like EMEA’s Digital Operational Resilience Act (DORA) and strict national data residency laws require institutions to maintain ironclad control over where their data lives. This often creates a massive barrier to public cloud adoption for sensitive workloads, effectively siloing a regional payment processor from modern AI tools simply because they cannot legally move transaction data to a public cloud for processing.
The "insights gap" in real-time operations: While agile fintech upstarts launch with flexible, cloud-native architectures, traditional firms struggle to turn vast data reserves into actionable intelligence. Their data is trapped in legacy environments that hit a performance ceiling during peak market volatility, leaving an investment firm struggling to scale its high-frequency trading ledgers when standard PostgreSQL or legacy systems max out.

As the industry enters the era of Agentic AI — where autonomous AI agents handle complex workflows like real-time risk assessment and automated trading — financial services firms must adopt a fundamentally new database strategy.

To overcome these entrenched challenges, they need to shift their strategy, moving away from proprietary databases that lock them in toward a hybrid, open-standards-based paradigm. This allows them to embrace the best of cloud-native innovation , like empowering real-time agentic AI workloads and edge computing , while maintaining control and residency of their own data on-premises.

At Google Cloud, we designed AlloyDB for PostgreSQL to unify operational data, real-time analytics, and generative AI into a single platform, and you can run it anywhere. Further, it specifically addresses the above mentioned FSI challenges directly through three guiding principles:

The licensing trap -> open standards: AlloyDB is 100% PostgreSQL-compatible, allowing institutions to modernize from expensive, legacy proprietary databases to an open platform that minimizes licensing headaches and vendor lock-in.
Sovereignty -> heterogeneous support: With AlloyDB Omni’s flexible deployment model, organizations can keep up with the complex topologies that characterize global banks, allowing mission-critical applications to run in a hybrid cloud, at the edge, or on-prem in air-gapped environments.
The insights gap -> battle-tested scale: By incorporating architectural lessons from Google's billion-user applications, the cloud-managed AlloyDB service delivers superior performance, running over 4x faster for transactional workloads than standard PostgreSQL. Crucially, the downloadable AlloyDB Omni engine brings this exact same high-concurrency scaling power straight to your local hardware—outperforming standard PostgreSQL by over 2x for transactions—while both deployment models accelerate real-time analytical queries by up to 100x.

Institutions are already realizing the benefits of this new approach:

Cynergy Bank: By migrating from on-prem SQL databases to AlloyDB, the bank successfully modernized a key element of its infrastructure. This critical initiative reduced app account loading times to under three seconds and enabled the integration of data and AI, providing a more personal "human touch" to digital banking and financial services.
Apex Fintech: The company leveraged AlloyDB to speed up margin calculations by 50%, enabling them to calculate risk for 100,000 accounts in just one minute while eliminating the need for a separate analytical system.

To ensure financial institutions can leverage these exact same breakthrough database innovations anywhere—without being forced into a public cloud migration—we built AlloyDB Omni to extend our signature kernel performance directly to your owned infrastructure.

AlloyDB Omni: Strong performance and deployment freedom

Whether running mission-critical applications on-premises, at the edge, or across hybrid clouds, financial institutions shouldn't have to choose between deployment flexibility and database performance. AlloyDB Omni bridges this gap by bringing Google’s breakthrough kernel innovations directly to your infrastructure. By design, it delivers enterprise-grade capabilities across three core dimensions:

True portability and modernization in place: Take absolute control over your data residency. Deploy AlloyDB Omni on-premises or at the edge to help comply with strict data sovereignty laws and regulations. This allows you to upgrade your legacy estates right where they live, avoiding the immense operational risk, latency, and vendor concentration risks of a forced public cloud migration.
Operational simplicity on your terms: Manage your databases like any other modern application. AlloyDB Omni is deployable across containerized environments, bare metal, or VMs. By leveraging tools like our Kubernetes Operator to automate routine provisioning, backups, and failovers, your platform teams gain integrated, API-driven control that elevates the database into a first-class citizen of your infrastructure alongside compute and storage. For non-containerized setups, Omni can be downloaded as a standalone RPM and managed with CLI or Ansible automation, and it is fully validated to run on Google Distributed Cloud (GDC) for the most restrictive air-gapped workloads.
Shattering the PostgreSQL performance ceiling: While standard PostgreSQL is highly trusted, high-concurrency financial workloads often hit a scaling wall. AlloyDB Omni breaks through these limits directly on your local hardware:

Superior transactional scalability: Delivers up to 2x faster transaction processing than standard PostgreSQL, ensuring payment processing and high-frequency trading ledgers maintain ultra-low latency even during volatile operational spikes.
Real-time analytics (HTAP): An intelligent, built-in columnar engine accelerates analytical queries by up to 100x. This enables instant, local business intelligence and reporting directly on live transactional data without the latency of moving it to a warehouse.
Secure, local AI transformation: Build fraud detection, risk modeling, or semantic search applications locally. AlloyDB Omni includes integrated AlloyDB AI vector capabilities—featuring a ScaNN index that is up to 10x faster and 4x more memory efficient than standard PostgreSQL's HNSW index. This allows you to scale generative AI apps while keeping sensitive financial data and foundation models strictly within your secured infrastructure boundaries.

Enterprise-grade security and compliance

Security cannot be an afterthought. We built AlloyDB Omni to exceed the rigorous standards of the finance industry, offering a hardened posture out of the box. AlloyDB includes:

Granular access and auditing: AlloyDB Omni integrates with Active Directory for unified identity management and provides detailed audit logging to track every access event — essential for regulatory audits.
Compliance-ready infrastructure: By utilizing features like Transparent Data Encryption (TDE) at rest, AlloyDB Omni is specifically engineered to help you meet your regulatory compliance obligations.

By providing a platform that is secure by design and that can be flexibly deployed in a variety of configurations, AlloyDB Omni enables financial institutions to stop choosing between stability and innovation and start delivering both.

Next steps

To learn more and get started, please visit https://cloud.google.com/alloydb/omni. You can learn more from the AlloyDB Omni documentation.

AlloyDB Omni is covered by the Google Cloud support plan the customer has chosen for their Google Cloud account; more information on support can be found at https://cloud.google.com/support.

Technology partners, system integrators and ISVs play an important role in helping customers modernize and build differentiated applications., We are extending the AlloyDB Cloud Ready program to now include AlloyDB Omni and enable our partner ecosystem to bring the best of what AlloyDB Omni has to offer to their customers. Customers can trust these validated partner products to work well with AlloyDB Omni, and can focus their time on modernizing database workloads and applications that will drive value for their business.

Get started with AlloyDB Omni by downloading and deploying in your preferred location, including on your laptop!

How Schrödinger sped up molecular discovery by 4x with Alphaevolve

Tue, 30 Jun 2026 16:00:00 +0000

Computational chemistry researchers have traditionally faced a frustrating trade-off when simulating molecular interactions: use fast classical force fields that sacrifice precision or rely on accurate quantum-mechanical methods that run too slowly on large jobs.

Machine-learned force fields (MLFFs) close that gap by training neural networks on high-fidelity quantum data. When it comes to modern drug discovery and materials design, though, there’s demand for even faster processing speeds to handle massive chemical libraries involved. To overcome such performance constraints, Schrödinger partnered with Google Cloud to deploy AlphaEvolve, an evolutionary AI coding agent developed by Google DeepMind that iteratively generates and refines algorithms to find the most efficient code path overcoming the algorithmic bottleneck.

A collaborative duet with AlphaEvolve

Schrödinger — a leader in developing scientific software for over three decades — identified two critical algorithms within their MLFF training pipeline that limited performance: neighbor list computation and Ewald summation. These algorithms aggregate data from atomic neighbors and calculate long-range potentials, but both became limiting factors in training and inference speed.

Schrödinger's primary technical goal was speeding up AI model training for energy and force calculations. Specifically, they targeted the Ewald summation, a critical but computationally demanding function used in molecular mechanics. The Ewald sum was the main performance constraint in Schrödinger's PyTorch code. It had no established vectorized algorithm and often relied on simple for-loops that ran slowly on large simulations.

By incorporating AlphaEvolve into their models, the system could generate a batched implementation of the Ewald summation using parallel batch matrix multiplication. This would evolve the PyTorch code to outperform existing custom kernels.

Evaluation metrics

Schrödinger used a rigorous multi-layered evaluation framework to confirm the evolved code was both performant and scientifically accurate:

Inverse time (primary metric): The core objective was to maximize throughput by reducing calculation time, from a baseline score of 7.9.
Functional correctness: All evolved programs had to pass a full test suite, including regression tests on complex systems such as disordered water models.
Success rate: This was measured by the share of programs that were both functionally correct and faster than the baseline.

“AlphaEvolve allows us to explore larger chemical spaces faster and more efficiently than ever before. Faster MLFF inference carries real business impact, shortening R&D cycles in drug discovery, catalyst design, and materials development, and enabling companies to screen molecular candidates in days rather than months.” — Gabriel Marques, technical lead of machine learning, Schrödinger

Results: a 4x speedup and breaking bottlenecks

By applying AlphaEvolve, Schrödinger replaced simple for-loops in the Ewald summation code with parallel batch matrix multiplication. This optimization raised the program success rate from less than 1% (40 out of 5,000 evaluations) to more than 60%, while improving the performance metric from the baseline of 7.9 to nearly 30.

Optimizing these foundational algorithms delivered a 4x speedup in both MLFF training and inference. This acceleration lets researchers compress molecular screening timelines and directly benefits several key research areas:

Drug discovery: Identifying viable therapeutic candidates quickly to address urgent medical needs.
Catalyst design: Developing efficient chemical processes for industrial applications.
Materials development: Designing next-generation materials with custom properties for electronics and energy storage.

The next evolution

Schrödinger plans to apply this evolutionary approach to custom GPU kernels to test whether AI-generated code can outperform human-engineered implementations.

Read the full technical paper on AlphaEvolve to learn how evolutionary AI agents optimize scientific codebases, or contact the Google Cloud AI team to discuss accelerating your research workflows.

Build agents even faster with Gemini Enterprise Agent Platform’s fully-managed, remote MCP server

Tue, 30 Jun 2026 16:00:00 +0000

A couple of months ago, we announced that over 50 Google-managed MCP servers are available.

Today, we’ll dive into how to use the Gemini Enterprise Agent Platform remote MCP server to securely connect your external AI agents to the resources inside your Google Cloud environment.

Connect your IDE to Google Cloud

Think of the Agent Platform MCP server as a bridge between your favorite external development tools and your Google Cloud architecture.

If you are building an agent in Antigravity CLI or Claude Code, for example, the Agent Platform MCP server allows that agent to securely interact with your Agent Platform resources. That way, your agent can now easily call models from Model Garden, pull down shared prompt templates, or even manage Notebooks directly within your project – all without ever leaving the IDE.

Quicker time-to-value

The speed at which you deliver value is one of your greatest advantages. But sometimes, connecting external development environments to cloud infrastructure forces a trade-off. Developers want to move fast with minimal setup, while IT teams need strict governance over data access.

The Agent Platform MCP server provides a single, standardized interface for your external agents so you can spend less time writing integration code and more time building useful features. And by running entirely within Google Cloud’s secure infrastructure, it gives you ready-to-use endpoints that protect your data while accelerating your development.

Get the best of both worlds:

Build with open standards: Agents you build outside of Google Cloud stay fully compliant with the open MCP specification. Your external IDEs and frameworks can seamlessly interact with your cloud environment without locking you into a proprietary ecosystem.
Centralized discovery: Catalog your assets with Agent Registry in Agent Platform. It acts as your organization's centralized library, so your teams can securely store, search for, and govern their entire inventory of skills, tools, and other AI capabilities.
Easy access with security and governance: Your connections are protected by default. IT teams can leverage native Cloud IAM Deny policies to ensure external developer frameworks only interact with authorized Google Cloud resources.

How it works: Three simple steps to connectivity

Enable the API: The Gemini Enterprise Agent Platform remote MCP server is automatically enabled when you enable the Gemini Enterprise Agent Platform API within your Google Cloud project.

2. Configure your client: Connect your AI application by following our configuration instructions to point to the remote server.

3. Use toolsets: Access a robust, copyable list of Toolset Endpoints to begin interacting with your Agent Platform resources immediately.

Available toolsets:

MCP Toolsets
Endpoint	Description	Tools
/mcp/generate	Generative AI tools	Core generation features
/mcp/predict	Prediction tools	Inference and raw prediction
/mcp/notebook	Colab enterprise notebook tools	Notebook runtime and execution management
/mcp/endpoints	Endpoint management tools	Lifecycle management for model endpoints
/mcp/models	Model registry tools	Model upload, registry, and deployment
/mcp/tuning	Model fine-tuning tools	Finetuning job management and tracking
/mcp/evaluation	Quality evaluation tools	Automated model quality and instance evaluation
/mcp/prompts	Prompt management tools	Prompt engineering and versioning workflows

Get started today

Visit the Agent Platform page to connect your favorite agent frameworks to the Agent Platform MCP server and start building today.

Bringing speed and strong cost performance to the market with Gemini Omni Flash and Nano Banana 2 Lite

Tue, 30 Jun 2026 16:00:00 +0000

Great creative happens when your tools move at the speed of your ideas. To help you create rich, reliable experiences while reducing regeneration time and costs, we’re adding two new models to Gemini Enterprise Agent Platform.

First, we’re announcing the general availability of Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image). This model is the fastest and most cost-efficient image generation and editing model within the Nano Banana model family. Whether you're rapid-firing ideas, A/B testing ad variations, or powering social apps for millions of users, this model gives you the power to explore, iterate, and scale with speed.

We’re also releasing Gemini Omni Flash in public preview. Grounded in Gemini's real-world knowledge, it powers high-quality video generation and conversational editing. Whether you're executing character or product swaps, performing dynamic style transfers, or adding objects and relighting scenes, this model gives you precise control to edit and refine video assets.

Bring your boldest vision to life with Gemini Omni Flash and Nano Banana.

Both models provide some of the best price-performance among market-leading frontier models for image and video generation and editing.

Gemini Omni Flash: High-quality video generation and editing

Gemini Omni Flash brings conversational video generation and editing directly into your applications. Users can easily embed powerful media models into their agentic workflows to create, remix, and refine video without ever switching platforms.

For comprehensive benchmarking information from Google DeepMind, please visit Gemini Omni.

We built Gemini Omni Flash with a focus across these four key areas:

Conversational editing: Swap characters, relight scenes, or alter angles using natural language while natively maintaining original audio and video tracks.
Multimodal input: Combine text, images, and video inputs to guide video generation. Gemini Omni Flash natively generates audio with every video output, while maintaining character, object, and style consistency.
World knowledge and simulation: It combines an intuitive understanding of physics with Gemini's knowledge of history, science and cultural context, bridging the gap from photorealism to meaningful storytelling.
Text and action synchronization: Render legible text and graphics directly into video, syncing kinetic typography and explainer text with on-screen movements.

Note: Support for audio references, video references, last frame, scene extension and higher resolutions for the Gemini Omni Flash via Gemini Enterprise Agent Platform API will be available soon.

To see the full list of model capabilities and how to integrate it check out the documentation and pricing.

Priced at $0.10 per second of video output, Gemini Omni Flash delivers some of the best price-performance for video generation and editing capabilities on the market.

Businesses using Gemini Omni Flash to build next-gen applications and creative agentic workflows:

“We’re excited to bring Google’s newest models, including Gemini Omni Flash and Nano Banana 2 Lite, to Adobe Firefly, our all-in-one creative AI studio – to help creators move faster from idea to finished content. These new models build on Adobe’s strategy to deliver our pro-grade tools and the industry’s top creative AI models in a connected workflow, giving creators flexibility and control over how they bring their creative ideas to life." – Matt Chotin, Senior Director of Product, Adobe

“The thing that immediately caught my attention was the sheer range of what the Gemini Omni Flash model does. The VFX capabilities surprised me, and looking at it as a producer, that brings in some very interesting possibilities. But the hybrid possibilities are what excite me most. You take the crews you have always worked with in the live-action world, and you bring the breadth of what AI can do now onto the same set.” - Nishant Tahilramani, Creative Director, Invideo

“Through our continued partnership with Google, WPP received early access to the new Gemini Omni Flash’s model and integrated it into WPP Open, our agentic marketing platform. Gemini Omni Flash’s multi-modal capabilities—allowing for seamless image, audio, and video input references—combined with intuitive conversational editing, represent a leap forward for controlled AI production. Teams have tested asset localization, precise product swaps, and dynamic style transfers for clients. We are thrilled to partner with Google Cloud to continually push the boundaries of AI-driven creativity and deliver highly adaptable, intelligent work for our clients.” – Elav Horwitz, Chief Innovation Officer, WPP

Nano Banana 2 Lite: Built for cost and speed

Nano Banana 2 Lite can generate an image in as little as four seconds. You can generate and iterate on design concepts in seconds, taking you from a blank page to the perfect layout instantly.

Nano Banana 2 Lite generates images in as little as four seconds.

Significant improvements over Nano Banana (Gemini 2.5 Flash Image)

Nano Banana 2 Lite blends fast image generation with a significant leap in visual quality and capability compared to our legacy model, Nano Banana. We enhanced core capabilities so you can execute complex tasks at high speeds:

World knowledge: Quickly draft accurate contextual scenes, rough data visualizations, and location-specific mockups.
Character consistency: Maintain character identities and object fidelity across multiple swift generations to easily build out storyboarding tools or embed virtual try-ons for ecommerce.
Quick text and localization: Draft copy on the fly by rendering legible text directly into rapid generations to see how typography works across localized ad variations.

To see the full list of model capabilities and how to integrate it check out the documentation and pricing.

Note: Image generation offers the fastest latency. Image editing may experience slightly higher response time.

Fast, cost-efficient image generation with a significant leap in visual quality and capability compared to our legacy model, Nano Banana

Industry leaders building faster visual experiences with Nano Banana 2 Lite:

“Speed is no longer a limitation. When generation is faster than imagination, creators can stay inside the idea instead of waiting on the tool. Nano Banana 2 Lite brings that feeling into the creative process, letting thoughts move into visuals almost instantly. For Artlist’s users, it means less time staring at a progress bar and more time creating, iterating, personalizing, and moving at the speed of culture.” - Idan Yonas, Director of AI Content & Innovation, Artlist

"Nano Banana 2 Lite is fast and reliable, helping designers explore more ideas to craft unique images on Figma Weave's node-based canvas. It's ideal for rapid iteration while staying in the creative flow." - Itay Schiff, Co-founder and Creative Director, Figma

"We have been testing Nano Banana 2 Lite to power real-time image generation within Manus’s autonomous workflows—from slide decks to web pages. Its speed suits these scenarios well, allowing our AI Agent to iterate on visuals quickly and deliver results in seconds. The image quality is also impressive, coming close to the full Nano Banana 2. We look forward to continuing our partnership and building better experiences together." - Tao Zhang, Co-founder and Chief Product Officer, Manus AI.

Safety and enterprise governance

C2PA content credentials and imperceptible SynthID watermarks are enabled by default to help verify content authenticity for both models.

To handle high-concurrency API requests reliably at scale, the Gemini Enterprise Agent Platform offers provisioned throughput (PT) for Nano Banana 2 Lite starting today. Provisioned throughput for Gemini Omni Flash will be rolling out soon.

Start building today

Embed these image and video generation and editing capabilities into your applications and creative workflows today. Explore these resources to start building:

Try the models: Agent Studio within Gemini Enterprise Agent Platform
API documentation: Nano Banana 2 Lite, Gemini Omni Flash
Access Colab notebooks: Nano Banana 2 Lite, Gemini Omni Flash
Pricing: Agent Platform Pricing for both models
Prompting guides: Nano Banana, Gemini Omni Flash
Gemini Omni Flash Prompting Agent Skills

Anomaly detection using dynamic thresholds and two-year-long alerts in Cloud Monitoring

Tue, 30 Jun 2026 16:00:00 +0000

Choosing the threshold of an alert policy can be a headache. You have to analyze historical data, aggregate it into semantically meaningful time series, and choose a threshold that matters. If the workload grows, your previously set static threshold might become too low, and your alert might fire too frequently. New workloads might require setting new thresholds, and setting separate thresholds for separate workloads requires creating separate policies, resulting in the annoyance of managing a fleet of mostly similar policies.

Not to mention, some metrics can’t even be alerted on using static thresholds. If your metric varies by time of day, like many e-commerce metrics do, then no single threshold will work. For example, what do you do if your metric looks like this:

Clearly something went wrong in the middle of that chart… but because the anomalous value is within the normal range of the daily data, no static value threshold can ever catch it.

Introducing long lookbacks and dynamic thresholding

We are pleased to announce that this problem is now solvable for users of Cloud Monitoring alerts with the launch of long-lookback alert policies for PromQL, currently in preview. This highly requested feature update now lets you configure PromQL alert policies to run over two years of metric data stored in Cloud Monitoring, supporting year-over-year and quarter-over-quarter analysis.

One major use case unlocked by two-year lookback horizons in PromQL is dynamic thresholding, that is, policies where the threshold refers to the metric’s history. A simple example is an alert policy that says “alert me if the average over the last 5 minutes is 2x more than the average over the last week.” Instead of setting a static number as your threshold, you set how anomalous each time series must be from its historical data before generating an alert. This allows flexibility in policies, supports naturally changing baselines caused by growth in workloads, and provides a single threshold that works for all workloads. You don’t have to analyze every time series to set alerts properly – just set a factor that signals “anomalous” to you.

Take the above example: To catch that anomaly, you might create a policy that says “alert me if the value over the last 5 minutes is lower than 70% of the value from the same 5-minute span one week ago.” Such a policy would create a threshold that varies by the time of day, and you would catch the anomalous drop:

Dynamic threshold algorithms

Choosing the right dynamic threshold algorithm in PromQL depends on the shape of your source data. Metrics that vary by time of day need a different algorithm than metrics that have little variation.

You can rewrite the below examples to have the historical data query as your threshold (putting a metric after the < or >), but if you do so you can’t easily visualize the threshold.

Because these use historical data, granular alert policies that trigger on individual workloads instead of aggregates might be flaky when spinning up new workloads. This issue will resolve itself as you accrue historical data. You can also avoid this by only running dynamic threshold alerts on aggregates.

Moving averages
In the simplest of the algorithms, alerts trigger when the recent trend of the data deviates from a moving average of data over a long period of time. This is good for catching anomalies in relatively stable data.

Here’s some example PromQL, comparing the last 5 minutes to a one-week baseline and alerting if it’s 30% higher or lower than average:

code_block: <ListValue: [StructValue([('code', 'sum(rate(http_requests_total[5m])) /\r\nsum(rate(http_requests_total[1w]))\r\n > 1.3\r\nOR\r\nsum(rate(http_requests_total[5m])) /\r\nsum(rate(http_requests_total[1w]))\r\n < .7'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d0e2820>)])]>

You can also write this as a direct comparison, which might be more understandable. The following says “alert me if the most recent 5 minutes average of data is >1.3x the weekly average.”:

code_block: <ListValue: [StructValue([('code', 'sum(rate(http_requests_total[5m])) > 1.3 * sum(rate(http_requests_total[1w]))'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d0e20d0>)])]>

Z-score (standard deviation)
Use this algorithm to identify anomalies based on the average and standard deviation of your data. A z-score measures the statistical distance between your recent data and historical data, with a common threshold being that a z-score above three or below negative three is considered anomalous. This measures the volatility of your data compared to its usual noisiness, and it works best with data that has a stable average and decent volatility:

Example PromQL, comparing the last 5 minutes to the one-week average and standard deviation:

code_block: <ListValue: [StructValue([('code', 'abs(\r\nsum(rate(http_requests_total[5m]))\r\n-\r\nsum(rate(http_requests_total[1w]))\r\n)\r\n/\r\nstddev_over_time(sum(rate(http_requests_total[5m]))[1w:5m])\r\n> 3'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d0e2100>)])]>

Example z-score signal and the resulting anomaly detection threshold:

Seasonal decomposition (time offset comparison)
This is a simple time-offset algorithm that compares time-series data in a period of time to the same period from the previous day or week. This is ideal for metrics that have timely patterns associated with them, such as visitors to a website that vary by time of day and day of week. Holidays and other factors that might cause a given day to be lower than expected can be smoothed away by averaging more than one historical period (e.g., average one week ago, two weeks ago, and three weeks ago, then compare that average to today).

Example PromQL, comparing the last 5 minutes to the same time period yesterday, alerting if the recent data is more than 50% lower than the one-day offset data:

code_block: <ListValue: [StructValue([('code', 'sum(rate(http_requests_total[5m])) /\r\n sum(rate(http_requests_total[5m] offset 1d))\r\n < .5'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d0e29d0>)])]>

Which can be algebraically rewritten to:

code_block: <ListValue: [StructValue([('code', 'sum(rate(http_requests_total[5m])) < .5 * sum(rate(http_requests_total[5m] offset 1d))'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d09f7c0>)])]>

In production, you might want to compare to the same period one week ago, or compare to an average of the same period one and seven days ago, to avoid triggering on naturally lower days such as weekends and holidays:

code_block: <ListValue: [StructValue([('code', 'sum(rate(http_requests_total[5m])) /\r\n ((\r\n sum(rate(http_requests_total[5m] offset 1d)) + sum(rate(http_requests_total[5m] offset 7d)) \r\n ) / 2)\r\n < .5'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4d09fa00>)])]>

When using time offsets, you can only reliably trigger on either drops or spikes, as triggering on both sudden drops and sudden spikes in a single policy may cause your alerts to fire twice.

Think of it this way: If traffic drops steeply today, your alert will trigger immediately. However, exactly 24 hours later, today's anomalous drop becomes tomorrow's historical baseline. If your policy triggers on any anomalous difference (higher or lower), the sudden "return to normal" tomorrow will look like a massive spike relative to yesterday's dip, and you will get a false alert for a phantom anomaly. You can see this in the above chart — the dip in the signal (blue line) reappears as its reciprocal exactly 24 hours later.

To prevent this, you should only track either drops or spikes when monitoring any given metric.

Control runaway costs using dynamic thresholds

Once you can trigger an alert based on deviations from a historical baseline, many interesting use cases open up. For example, you can use dynamic thresholding to prevent overspend for any Google Cloud service that offers a metric that roughly tracks spend.

Say you are concerned about runaway AI token costs. You could do the following:

- Configure a dynamic threshold alert that triggers if the most recent 10 minutes of accumulated input/output token usage is more than 25x the one-week historical average, which should only catch extreme anomalous scenarios (such as leaked API keys) that will definitely result in overspend:
  - sum(rate({"__name__"="aiplatform.googleapis.com/publisher/online_serving/ token_count"}[10m])) >25 * sum(rate({"__name__"="aiplatform.googleapis.com/publisher/online_serving/ token_count"}[1w]))

Trigger your alert to fire to a Pub/Sub notification channel that pushes notifications to a Cloud Run function.
That Cloud Run function then runs a workflow that uses the Cloud Quotas API to lower your Token Usage quota to 0, which immediately stops the overspend. Note that legitimate use of tokens will be paused until you can fix the problem… but at least you’ll stop the bleeding.

Sign up to be a design partner

We are working on productizing anomaly detection using dynamic thresholds so they’re easier to write. We’re also working on more complex anomaly detection algorithms in Cloud Monitoring alerting that uses AI models specifically trained on time-series data.

If you’re interested in sharing your thoughts and being an early adopter of what we’re building in this space, sign up to be a preview partner. We’d love to have you!

Supercharging the agentic era with Spanner’s multi-model architecture

Mon, 29 Jun 2026 23:00:00 +0000

In the agentic era, the role of the database has fundamentally changed. It is no longer a passive repository; it’s a critical context engine designed to ground generative AI apps, models and power autonomous workflows. To do this effectively, databases must move beyond fragmented architectures and embrace a unified, multi-model foundation, facilitating deep reasoning and transforming static data into a system of action.

Spanner is leading this charge, and as a foundational pillar of Google’s Agentic Data Cloud, the industry is taking notice. In the 2025 Gartner® Critical Capabilities for Operational Cloud Database Management Systems report, Google (Spanner) ranked #1 in the Lightweight Transactions Use Case for the second consecutive year — in our opinion proving it is the most efficient engine for modern microservices and event-driven architectures.

Gartner® Operational Cloud DBMS use cases:

#1 in Lightweight Transactions
4.9 / 5.0 for Transactional Consistency
4.6 / 5.0 for AI/Machine Learning and GenAI

This technical momentum, which also recently earned Spanner the prestigious SIGMOD Systems Award, is matched by undeniable economic value. A recent Forrester Consulting Total Economic Impact™ (TEI) study commissioned by Google Cloud found that an organization (based on composite customer profile from Forrester’s survey) realized a 132% ROI with a fast 9-month payback period, yielding $7.74M in total benefits over three years having deployed Spanner.

The multi-model advantage for the agentic era

True AI autonomy requires deep context. To reason effectively, an AI agent cannot look at data through a single lens; it must simultaneously understand structured history (relational), semantic meaning (vectors), real-world connections (graphs), and textual details (full-text search).

Spanner natively breaks down these multi-model barriers. Instead of forcing you to stitch together disparate engines, Spanner unifies relational, vector, graph, key-value, and full-text search data directly within a single, highly performant database architecture. This architectural integration allows AI models to leverage situational, semantic, and relationship context instantly and concurrently.

Spanner’s fully interoperable multi-model capabilities allow organizations to build intelligent applications without compromise:

Spanner Graph: A unified graph and relational experience built on the ISO-standard GQL. You can model data natively as a graph or as an overlay on top of relational data, which is critical for building knowledge graphs that ground AI agents in real-world facts. Customers like Palo Alto Networks leverage Spanner Graph to power crucial access-control use cases at planet-scale, securing their AI infrastructure without needing a specialized, siloed graph database.
Integrated vector search: A fully integrated semantic search solution offering both K-Nearest Neighbors (KNN) and Approximate Nearest Neighbor (ANN) search, capable of supporting indexes with over 10 billion vectors for fast, low-latency retrieval-augmented generation (RAG).
Relational and key-value: Spanner pioneered the relational scale-out database (Google SQL and PostgreSQL). We've also introduced high-performance key-value capabilities via a Cassandra-native endpoint, allowing for easy lift-and-shift of Cassandra workloads.
Full-text search: Building on Google's decades of search expertise, Spanner provides advanced information retrieval across structured and unstructured data, including an enhance_query option for automatic synonym matching and spell correction. Streaming legal intelligence platform Inspira simplified a 4.5 TB data pipeline into a unified, high-performance single-source of truth. Leveraging Spanner’s native support for FTS and vector search capabilities Inspira achieved high-precision snippets for LLM-based legal analysis with RAG workflow.
Spanner columnar engine: This architectural breakthrough enables analytical queries to run up to 200× faster on live operational data, bridging the gap between OLTP and analytics to provide agents with real-time context without the "ETL tax." AI-powered fraud prevention platform Verisoul uses the columnar engine to run rich analytics on high-velocity transactional writes in one place, eliminating data copies and replication lag to get near-instant answers.

True interoperability means these aren't just isolated features , they are tightly integrated. Instead of writing complex application logic and brittle ETL pipelines to stitch together a graph database, a vector database, and a search engine, developers can query relationships, semantic meaning, and keywords in a single, ACID-compliant SQL statement.

Here’s an example of how a developer can combine relational, graph traversal, full-text search, and vector similarity search in one cohesive query to power an intelligent product recommendation agent:

Spanner Omni: Multi-model capabilities, everywhere

To truly be the unified data foundation for the Agentic Data Cloud, a database cannot be confined by infrastructure borders. That’s why we expanded our vision with Spanner Omni, bringing these multi-model capabilities to any environment without hardware restrictions, just as we did with AlloyDB Omni.

Spanner Omni is a downloadable version of Spanner in a fully containerized deployment model that requires absolutely zero dedicated hardware. It is designed with maximum flexibility in mind, running natively on Kubernetes using the infrastructure you already own. Whether your workloads are running on-prem, at the edge, or across other major public clouds like AWS and Azure, Spanner Omni gives you control and helps ensure you have a consistent, globally distributed data foundation.

This means organizations can leverage Spanner Graph, vector search, full text search, and our columnar engine anywhere, effectively breaking down cloud silos and making these cutting-edge capabilities available without vendor lock-in.

Industry-defining capabilities for core databases

In the 2025 Gartner® Critical Capabilities for Cloud Database Management Systems for Operational Use Cases, for the second consecutive year, Gartner ranked Google (Spanner) #1 in the Lightweight Transactions Use Case. We believe this a testament to its efficiency and low latency for modern, event-driven microservices.

In our opinion, this industry recognition goes far beyond simple market presence, it is validated by deep foundational technical breakthroughs that separate Spanner from legacy architectures. Unlike platforms that bolt disparate, siloed database engines together and label it as "multi-model," or require users to select the modality at the time of database creation with no interoperability between modalities, Spanner’s capabilities are built on a bedrock of Google’s most advanced computer science:

TrueTime and Paxos for global consistency: Spanner’s distributed transactions are governed by TrueTime — a highly available, globally synchronized clock system utilizing GPS and atomic clocks. This enables lock-free distributed reads and strict external consistency globally. Combined with highly optimized Paxos consensus, Spanner delivers synchronous replication with zero data loss (Recovery Point Objective, i.e. RPO=0) and rapid recovery timelines (Recovery Time Objective, i.e. RTO=0) even during total regional failures.
Integrated columnar engine: To eliminate the ETL tax and bridge the gap between OLTP and OLAP, we integrated a breakthrough columnar engine directly into Spanner's distributed storage layer (Colossus). This allows developers to run complex analytical queries to run up to 200x faster directly on live, operational data without impacting transactional performance. And with full separation of storage and compute, users are able to run large analytical queries without impacting the operational workload using Spanner DataBoost, a serverless technology that directly accesses the database storage.
ScaNN-powered vector search: Our native vector search isn't a bolted-on afterthought. It’s powered by Scalable Nearest Neighbors (ScaNN) — the exact same state-of-the-art indexing algorithm that powers Google Search and YouTube. This allows Spanner to execute sub-millisecond similarity searches across 10-billion-plus vector indexes natively alongside relational and graph data.
Dynamic resharding: Under the hood, Spanner's architecture automatically reshards data based on size and load. This transparent load balancing eliminates the dreaded "hotspotting" that plagues legacy NoSQL and distributed SQL systems.

While some industry evaluations often measure the market through a fragmented lens of disconnected database engines, we believe true innovation requires engineering for this level of deep, architectural integrations. For the agentic era, anything other than a natively unified foundation is simply a bottleneck.

A unified vision for the agentic era

We believe that the future of data is unified, open, and inseparable from AI. Spanner’s momentum reflects a market rapidly shifting away from a patchwork of isolated databases towards a singular, intelligent context hub.

To meet this future head-on, we are relentlessly expanding what is possible with a single unified database. This includes breakthrough innovations like our integrated columnar engine for real-time analytics, native vector search powered by Google's world-class ScaNN technology, and built-in AI functions that bring model inference directly to your data. Furthermore, by integrating Spanner Graph integrated with Graph Neural Networks (GNNs) for deep predictive reasoning, and Spanner Omni to extend this unified architecture across hybrid and multi-cloud environments, we are delivering a platform designed for what comes next.

Crucially, Spanner does not exist in isolation; it is a foundational pillar of Google’s broader Agentic Data Cloud. Through seamless, zero-ETL integrations across our Data Cloud Including BigQuery for enterprise-wide analytics and Gemini Enterprise Agent Platform for advanced model orchestration, Spanner breaks down the barriers between operational data and enterprise intelligence.

In the agentic era, AI models require more than just isolated data points; they need a cohesive ecosystem. By natively federating real-time operational context from Spanner with petabyte-scale historical insights from BigQuery, we empower agents to act autonomously, reason deeply, and drive unprecedented business value.

By providing a real-time, trustworthy, and multi-faceted view of data, regardless of where it lives, Spanner empowers organizations to build the next wave of transformative, intelligent applications.

We are incredibly excited about the journey ahead and will continue to pioneer the frontiers of what a true multi-model database can achieve.

Try Spanner for free for 90-days or for as little as $65 USD/month for a production-ready instance that grows with your business without downtime or disruptive re-architecture.

^{Critical Capabilities for Cloud Database Management Systems for Operational Use Cases, By Ramke Ramakrishnan, Masud Miraz, Xingyu Gu, Henry Cook, Aaron Rosenbaum, November 19, 2025.}

^{GARTNER and MAGIC QUADRANT are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.}

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

Cloud CISO Perspectives: How Google Cloud Security uses AI internally

Mon, 29 Jun 2026 16:00:00 +0000

Welcome to the second Cloud CISO Perspectives for June 2026. Today, we’re discussing how we use AI to chart a path to autonomous software development lifecycle security.

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.

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Cloud CISO Perspectives: Our path to autonomous SDLC security

By Chris Betz, CISO, and Ruchi Shah, senior director, Security Engineering, Google Cloud

Chris Betz, CISO, Google Cloud

AI has upended the economics of exploiting vulnerabilities, effectively erasing the traditional patching window. To survive this new reality, security requires an autonomous defense.

To counter machine-speed, AI-driven threats, we’ve worked hard to transition Google Cloud’s security posture to an autonomous, proactive model. By embedding specialized AI agents directly into our software development lifecycle (SDLC), we’ve created automated guardrails that protect code at a scale and speed unreachable by human teams — and we’re taking steps to make those same guardrails widely available.

Ruchi Shah, senior director, Security Engineering, Google Cloud

How we designed agentic, secure SDLC architecture

Google Cloud deploys modular, interconnected AI agents across every stage of the software lifecycle to continuously harden products from code ingestion to production.

1. Design, review, and gate

Historically, launch intakes and threat modeling were manual bottlenecks. Today, Google Cloud engineering teams route product launches through an agent-based security review pipeline.

Agents cross-reference designs against a continuous control catalog of more than 200 rigorous security requirements. High-risk indicators are automatically triaged and flagged for human engineering intervention, while a dynamic product dossier updates in real-time to replace static threat models.

Google Cloud has embedded agentic capabilities across the entire SDLC flow to continuously harden products end-to-end.

2. Centralized AI code scanning and the Mantis framework

Naive, decentralized AI code scanning suffers from sloppiness, frequently hallucinating bugs and yielding true-positive rates under 7%. To solve this, we built Mantis, our core multi-agent orchestration framework designed specifically for scalable, context-aware repository analysis.

The core skills at the heart of Mantis are now open source to demonstrate the fundamental concept. We have a more full-fledged version running internally and securing our customers.

Mantis eliminates brute-force code ingestion by constructing a hierarchical security summary tree. By condensing individual files into directory and root-level summaries, Mantis reduces token overhead by over 85% while preserving critical structural context across massive repositories.

The architecture relies on a highly-coordinated workflow across new agents and existing technologies:

Strategist agent: Evaluates the high-level code structure, threat models, and dependency graphs to isolate risky architectural patterns, establishing a prioritized global plan of targeted investigation tasks.
Research agents: Acting as specialized domain investigators, these agents use internal code searches to drill into raw source files, examining data tracking, control flows, and sanitization logic.
Deduplicator, reviewer, and critic agents: Sanitize findings to filter out noise and eliminate false positives.
Reproduction sandbox: Automatically runs AI-generated proof-of-concept exploits in an isolated, emulated environment to verify real-world exploitability before alerting developers.

3. Self-healing fuzz testing

While code scanning provides breadth, dynamic fuzz testing uncovers deep runtime vulnerabilities. However, writing and maintaining fuzz harnesses are often a significant engineering bottleneck.

Our autonomous, multi-agent engine eliminates manual intervention:

Context and Drafting agents synthesize product logic and existing unit tests to author initial fuzzing harnesses.
Building and Testing agents execute the code and feed real-time compiler and linker errors into a Hallucination Cleaner agent, which acts as an automated mechanic to repair broken dependencies and build configurations.
Quality Analyzer agents monitor runtime execution, actively adjusting inputs to bypass code blockers and penetrate deeper into complex, stateful APIs.

4. The unified AI patching pipeline

Finding thousands of vulnerabilities at scale can create a dangerous remediation backlog without proper planning. To close the exposure window, our discovery tools route findings directly into an autonomous remediation pipeline:

The Reproduce agent replicates the crash in the sandbox.
The Bug Context agent maps the failure execution path.
The Patch agent generates a targeted code fix.
The Evaluation agent runs a rigorous regression loop (that re-compiles code and executes tests) to ensure the patch is safe. Only fully-validated fixes are submitted to a human reviewer.

5. Autonomous and secure posture management

Post-launch, we maintain security integrity with an autonomous security posture management (ASPM) system. By converting our security standard catalog into programmable skills files, the ASPM system continuously checks production systems for configuration drift, automatically triggering agentic remediation when a violation occurs.

Continuous augmentation via self-reflection

Stateless AI systems repeatedly fall into the same logical traps, such as attempting to fix bugs inefficiently and hallucinating about non-existent code. Our framework solves this by introducing a post-hoc self-reflection loop. After a workflow concludes, a dedicated reflection agent analyzes execution logs, tool histories, and human feedback.

Successful trajectories and design patterns are permanented into a global knowledge store. When future agents spin up, this intelligence is injected directly into their context window, creating a compounding-interest effect on our security engineering. This approach has helped us to improve both the vulnerability fix success rate and efficiency.

Moving toward immune software

Google Cloud's internal journey demonstrates that protecting software at AI-scale requires a fundamental paradigm shift from human-dependent checklists to proactive multi-agent orchestration. By pairing open-source tooling like Mantis with autonomous, self-healing execution loops, we are pioneering a future of "immune" software development — where applications continuously discover, validate, and patch their own weaknesses in real-time.

You can learn more about how we use Mantis and other tools to find and fix vulnerabilities at machine-speed here.

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In case you missed it

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

Verifiable trust in the AI era: What’s new in Confidential Computing: To help further strengthen verifiable privacy in cloud AI deployments, here’s our latest Confidential Computing innovations. Read more.
Choice, compliance, and collaboration: Europe’s path to open digital sovereignty: Our Sovereign Cloud solutions are designed to meet Europe's tiered compliance requirements at every level. Read more.
How AI Is rewriting the SecOps playbook: With adversaries operating at machine speed, defenders must prioritize speed, automation, and continuous decision-making. Read more.
Google named a Leader in IDC MarketScape SIEM 2026 Vendor Assessment: We are proud to announce that Google has been named a Leader in the 2026 IDC MarketScape for worldwide SIEM platforms. Read more.
Announcing the Wiz Runtime Sensor for Windows: Wiz pairs real-time threat detection with a memory-safe architecture that scales efficiently to protect your essential cloud infrastructure. Read more.
New VPC Service Controls updates can help secure agents: Designed for agentic workloads, new capabilities in VPC Service Controls can help establish a network-level, destination-based perimeter. Read more.
Bug hunting on Gemini Spark: Gemini Spark brings a persistent agent to the Gemini App. Learn how to approach security testing for this new paradigm and focus on high-impact bugs. Read more.

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

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Threat Intelligence news

China-nexus threat actor targets medical community for cross-sector research: Google Threat Intelligence Group (GTIG) has identified a sophisticated campaign attributed to UNC6508, a People's Republic of China (PRC)-nexus threat actor, targeting the North American academic, medical, and military research community, that went undetected for more than a year. Read more.
ShinyHunters targets education sector with Oracle PeopleSoft exploit: Mandiant and GTIG have identified an active compromise and extortion campaign attributed to UNC6240 (ShinyHunters) targeting Oracle PeopleSoft application infrastructure. Read more.
Zero-day exploitation in Cisco Catalyst SD-WAN Manager: Mandiant has identified a threat actor targeting a vulnerability in Cisco Catalyst SD-WAN to escalate privileges from a compromised administrative account to root-level access. 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: How Google Cloud uses LLMs to defend billions of users: Google Cloud CISO Chris Betz discusses AI Threat Defense, and emphasizes shifting security practices earlier in the development lifecycle through human-AI collaboration. Listen here.
Cloud Security Podcast: To couple or decouple SIEM: Alex Hurtado, director, Detection Engineering, Scanner, and Christopher Witter, DNR lead, Dropbox, debate the merits of centralized versus decentralized SIEM architectures. 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.

Synthesize the big picture and analyze trends with BigQuery's AI.AGG function

Mon, 29 Jun 2026 16:00:00 +0000

We recently announced the preview of the BigQuery AI.AGG() function. With AI.AGG(), you can use natural-language instructions within a single line of SQL to summarize or synthesize information over millions of rows of unstructured or even multimodal data.

While BigQuery already offers powerful AI functions that help you analyze individual rows of data, analyzing unstructured data at scale requires a different approach. AI.AGG() lets you ask questions from unstructured data such as logs and documents, for example:

What are the top three feature requests among the negative product reviews?
What kind of errors are users seeing most frequently, and how should I start investigating them?
In which specific scenarios is our automated agent consistently failing to resolve customer issues?

In this post, we'll dive deeper into the AI.AGG() function and look at a few of the use cases that it unlocks, including how it can be used in combination with BigQuery’s other managed AI functions for complex, intelligent data analysis.

Analyzing system logs with `AI.AGG()`

A great example of the power of AI.AGG() is analyzing system logging. Log messages, warnings, errors, and stack traces can contain extremely useful information for improving your service, but it can be time- and labor-intensive to investigate them manually — especially if you operate at scale and have thousands of them to review.

With AI.AGG(), you can easily analyze many logs at once, grouping and prioritizing them to decide which ones to dig deeper into first. In fact, our BigQuery engineering team used this exact approach while developing AI.AGG() — using the function to help identify edge cases related to input handling for the feature itself!

To demonstrate this, let’s analyze a public dataset of Apache Spark standard INFO logs available from Loghub. Often, clusters can run into issues like memory thrashing, clock drift, or broadcast bottlenecks without ever throwing a FATAL error. You can use AI.AGG() to analyze these seemingly normal logs for hidden inefficiencies. You can load the sample data file into BigQuery using any of the supported methods, such as the UI, CLI, or client libraries. The following example assumes you’ve loaded the log file into a dataset called bq_logs_demo and table named spark_logs_unstructured.

Notice how we construct the prompt here. We explicitly give the model permission to say "everything is fine," which prevents it from hallucinating errors, while instructing it to hunt for specific anomalies:

code_block: <ListValue: [StructValue([('code', "SELECT\r\n Component AS spark_component,\r\n COUNT(*) AS log_count,\r\n AI.AGG(\r\n Content,\r\n 'Analyze these Spark system INFO logs. Provide a 2-sentence summary: First, describe the normal operation of this component. Second, explicitly identify any hidden inefficiencies, latency spikes, repeated retries, or unusual patterns.'\r\n ) AS performance_analysis\r\nFROM\r\n `bq_logs_demo.spark_logs_structured`\r\nGROUP BY\r\n Component\r\nORDER BY\r\n log_count DESC;"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c9f4d00>)])]>

You can see in these results that AI.AGG() successfully acknowledges the "operating normally" messages while surfacing the critical diagnostic insights:

The query results pane showing the insights generated by AI.AGG() over the logs dataset.

Extracting categories from unstructured text and image data

Now, let’s look at some more use cases that demonstrate the flexibility of AI.AGG(), using one of BigQuery’s public datasets, cymbal_pets, a fictional pet supply shop. It includes a catalog of products carried by the store, with unstructured data like product names, descriptions, and images, making it a great example of the power of AI functions for handling unstructured data.

For example, let’s say you want to categorize the products in the dataset. The first hurdle in this case isn't applying labels to your products, but discovering what categories exist across the product catalog. With AI.AGG(), you can ask the model to analyze the raw product names and descriptions to identify the overarching categories for you.

code_block: <ListValue: [StructValue([('code', "-- Identify categories of products from product name and description\r\nSELECT\r\n AI.AGG(\r\n ('Product: ', product_name, ' - Description: ', description),\r\n 'What are the major categories of these products?' \r\n ) AS category_description\r\nFROM\r\n `bigquery-public-data.cymbal_pets.products`;"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c9f4280>)])]>

This query returns a simple plaintext list of categories:

The plaintext result of categories determined by AI.AGG() over our products dataset.

This initial query is great for discovery, but a simple plaintext string isn't enough to build a reliable, automated data pipeline. To actually tag your data, you need to instruct AI.AGG() to return a structured format, like a JSON array. Then, you can use the structured categories as a parameter within another AI function, AI.CLASSIFY(), to actually label each product with its category.

The following SQL statement completes each of these steps in one script:

code_block: <ListValue: [StructValue([('code', "-- 1. Declare a variable to hold the array of categories\r\nDECLARE generated_labels ARRAY<STRING>;\r\n\r\n-- 2. Create a dataset to store the results\r\nCREATE SCHEMA IF NOT EXISTS categorized_cymbal_pets;\r\n\r\n-- 3. Generate the JSON string with AI.AGG and extract it into the variable\r\nSET generated_labels = (\r\n SELECT \r\n JSON_VALUE_ARRAY(\r\n AI.AGG(\r\n ('Product: ', product_name, ' - Description: ', description), \r\n 'Identify the major product categories. Return exactly one valid JSON array of strings. Do not include markdown code blocks, backticks, or conversational text.'\r\n )\r\n )\r\n FROM `bigquery-public-data.cymbal_pets.products`\r\n);\r\n\r\n-- 4. Feed the variable directly into AI.CLASSIFY\r\nCREATE OR REPLACE TABLE `categorized_cymbal_pets.categorized_products` AS (\r\nSELECT \r\n product_name,\r\n description,\r\n AI.CLASSIFY(\r\n ('Product: ', product_name, ' - Description: ', description),\r\n generated_labels\r\n ) AS assigned_category\r\nFROM \r\n `bigquery-public-data.cymbal_pets.products`\r\n);"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c9f4790>)])]>

You can now view the resulting table, which includes an assigned_category:

A preview of the categorized_products table which includes the new assigned_category column created by AI.AGG() and AI.CLASSIFY().

If you look closely at the intermediate table, you'll notice the structured categories changed slightly from the initial plaintext results. This happens for two reasons: First, LLMs are nondeterministic, meaning that they don't always give the exact same response to the same prompt. Second, the prompt was adjusted to accommodate the new output structure.

The returned product categories are structured as JSON by AI.AGG() as requested as part of the prompt.

With the table now labeled by category, you can group by the categories to do traditional SQL aggregation, or use AI.AGG() to consider each category separately.

For example, the following query fetches traditional metrics (like row counts) right alongside a synthesized AI summary of what those specific grouped products have in common:

code_block: <ListValue: [StructValue([('code', "-- Synthesize insights grouped by our newly assigned categories\r\nSELECT \r\n assigned_category,\r\n COUNT(*) AS item_count,\r\n AI.AGG(\r\n ('Product: ', product_name, ' - Description: ', description),\r\n 'Write a concise, one-sentence summary describing the common characteristics or purpose of the products in this category.'\r\n ) AS category_summary\r\nFROM \r\n `categorized_cymbal_pets.categorized_products`\r\nGROUP BY \r\n assigned_category\r\nORDER BY \r\n item_count DESC;"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c9f45b0>)])]>

Query results showing analyzing with AI.AGG() alongside more traditional SQL methods.

Unstructured data isn't limited to text. Because AI.AGG() natively supports multimodal inputs, you can return aggregated insights directly from image files.

The cymbal_pets Google Cloud project also contains a Cloud Storage bucket full of product photos. By creating an external object table, you can securely pass the image URIs directly into AI.AGG() and ask the model to summarize the visual content of the entire collection.

code_block: <ListValue: [StructValue([('code', "-- Summarize content of images in the object table\r\nSELECT\r\n AI.AGG(\r\n STRUCT(OBJ.GET_ACCESS_URL(ref, 'r')),\r\n 'What are the major categories of these images?'\r\n ) AS category_description\r\nFROM\r\n `bigquery-public-data.cymbal_pets.product_images`;"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c9f4550>)])]>

Query results showing AI.AGG() surface product categories by analyzing the product images located in Google Cloud Storage.

How AI.AGG() works and best practices

To use AI.AGG() effectively in your own environment, it helps to understand how it processes data behind the scenes. Here’s what you need to know about context windows, error handling, and optimizing your pipelines.

1. Context windows and multi-level aggregation
LLMs have a specific context window and can have a hard time handling massive amounts of input. AI.AGG() solves this problem by automatically dividing your input rows into batches, aggregating those batches, and then aggregating the results of those batches into a final answer. This means you don’t have to worry about manually managing the context window when passing in large numbers of rows. Note that AI.AGG() won’t split up a row of data across batches, so make sure that each individual row is smaller than the context window, to avoid the row being skipped. Many smaller rows will give AI.AGG() more flexibility with how to batch each row.

2. Token usage with multi-level aggregation
Because AI.AGG() uses a multi-level aggregation structure, the total input tokens sent to the model may be higher than the raw tokens in your starting table (depending on how many rounds of aggregation are required). As a best practice, always reduce the number of input tokens by using LIMIT or pre-filtering your data upstream before passing it to AI.AGG().

3. Specifying your model endpoint
If you don’t specify a model endpoint, AI.AGG() will default to a recent model. However, for production pipelines, you often want explicit control:

Short-form names: You can use a short-form endpoint (e.g., gemini-2.5-flash), in which case AI.AGG() will use that model in the query execution region:

code_block: <ListValue: [StructValue([('code', "AI.AGG(\r\n input_data,\r\n instructions => 'Your instructions here.',\r\n endpoint => 'gemini-2.5-flash' \r\n)"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c9f4e20>)])]>

Fully-qualified names: If the query execution region doesn’t support your desired model, or you prefer to use a global or multiregional endpoint, provide the fully qualified model name:

code_block: <ListValue: [StructValue([('code', "AI.AGG(\r\n input_data,\r\n instructions => 'Your instructions here.',\r\n endpoint => 'https://aiplatform.googleapis.com/v1/projects/[YOUR_PROJECT]/locations/global/publishers/google/models/gemini-3.5-flash'\r\n)"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c704b50>)])]>

4. Input and output modalities

Inputs: AI.AGG() supports text (via strings or references to text files) and image data. It also supports arrays of these types, though you should refer to the known issues documentation for edge cases regarding arrays of images.
Outputs: The function will always return a string. While you can prompt the model in your instructions to format the output as JSON or Markdown, keep in mind that the database engine does not strictly enforce this. Multimodal output (e.g., generating an image) is not currently supported.

5. Treatment of NULLs
AI.AGG() automatically skips NULL input rows without processing them. However, you must be careful when passing structured data. Like other BigQuery AI functions, AI.AGG() concatenates STRUCT fields similarly to the standard CONCAT() function. This means if even one field within your STRUCT is NULL, the entire row is treated as NULL and will be skipped.

Let's revisit our first categorization query. What if several rows of our products table are missing their description? Because of the NULL concatenation rule, those rows would be silently dropped from the analysis entirely. Here is how we can use IFNULL() to provide a fallback string, guaranteeing that every product is taken into account even if its description is blank:

code_block: <ListValue: [StructValue([('code', "-- Identify categories of products from product name and (optional) description\r\nSELECT\r\n AI.AGG(\r\n ('Product: ', product_name, ' - Description: ', IFNULL(description, 'No description provided')),\r\n 'What are the major categories of these products?' \r\n ) AS category_description\r\nFROM\r\n `bigquery-public-data.cymbal_pets.products`;"), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7fee4c704820>)])]>

6. Error handling
If AI.AGG() receives invalid input, or encounters an error during LLM processing, it will attempt to provide partial results. Rows containing invalid input or which were rejected by the LLM model will not be considered in the final results.

You can review exactly how many rows failed to process by checking your BigQuery job statistics, exactly as you would for scalar managed AI functions like AI.IF().

information showing an example of Gen AI function error details.

Give it a try!

These are just a few examples of the ways AI.AGG() can help analyze unstructured data. The AI.AGG() function is in preview in BigQuery now, so it’s available to all BigQuery users. Try it out on your own use cases!

You may also be interested in checking out BigQuery's other managed AI functions, AI.CLASSIFY(), AI.IF(), and AI.SCORE(), as well as general-purpose functions like AI.GENERATE(). We look forward to seeing what you build with them.