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Google Cloud
60 posts about Google Cloud. Every summary links to the original.
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Supercharge your development with the Google Developer Knowledge API ecosystem
The Developer Knowledge API is Google’s official, programmatic source for developer documentation across Google Cloud, Firebase, and Android. It replaces web-scraping with a structured API serving fresh Markdown-formatted documentation, offering semantic and keyword search, intelligent document chunking, and grounded Q&A. The ecosystem includes a gcloud CLI surface that integrates search and grounded Q&A into terminal environments without extra plugins. An agent skill uses the standard MCP integration to connect AI assistants such as Claude Code, Cursor, and GitHub Copilot to up-to-date documentation. Multi-language client libraries enable programmatic access with features like BatchGetDocuments for retrieving up to 20 documents in one call.
Turn your REST APIs into MCP tools with Google Cloud API Gateway
Most enterprise REST APIs are invisible to AI agents, forcing teams to run separate MCP servers that duplicate their existing gateway logic. Google Cloud API Gateway in Public Preview solves this by acting as a remote MCP server — annotate the OpenAPI 3.0.x/3.1.x spec with x-google-api-management.mcp and deploy it with no extra infrastructure. The gateway accepts MCP JSON-RPC requests on a single /mcp endpoint, transcodes tools/call into the corresponding REST call, runs it through existing policies, and returns the response as an MCP result. Authentication via JWT or API key works unchanged for REST operations; tools/list is unauthenticated by default and requires JWT in production for security. With no separate server to operate, teams in Public Preview can expose up to 1,000 tools per gateway that support existing authentication and quota, and the MCP server is automatically discoverable via API hub and Agent Registry.
Google Cloud Use Cases: How Enterprises Run Data & AI on GCP with Databricks
The guide presents Google Cloud as a unified platform for enterprise data engineering, analytics, machine learning, and generative AI, with managed services intended to reduce infrastructure and integration work. It focuses on practical architectures, including medallion ETL pipelines that store Bronze, Silver, and Gold Delta Lake tables in Google Cloud Storage and expose Gold data to BigQuery through an optimized connector. For streaming workloads, Cloud Pub/Sub supplies ingestion while Apache Spark Structured Streaming processes continuous micro-batches; together with Delta Lake ACID transactions, this supports exactly-once results despite duplicates or out-of-order messages. The text also describes Cloud Run and Cloud Functions for containerized or event-driven workloads, and identifies retrieval-augmented generation applications on Vertex AI as a common starting point for generative AI. It concludes that integrated service combinations, rather than isolated tools, are the central strategic advantage for organizations building evolving data and AI platforms on Google Cloud.
Expanding our use of Google Cloud TPUs
Anthropic announces plans to expand its use of Google Cloud technologies, including up to one million TPUs, in an expansion worth tens of billions of dollars. The company expects the deployment to bring well over one gigawatt of capacity online in 2026, increasing resources for customer demand, testing, alignment research, and responsible deployment at scale. Anthropic says it serves more than 300,000 business customers and that large accounts, defined as customers representing more than $100,000 in run-rate revenue, have grown nearly sevenfold in the past year. The company describes a diversified compute strategy using Google TPUs, Amazon Trainium, and NVIDIA GPUs, while remaining committed to Amazon as its primary training partner and cloud provider and continuing Project Rainier.
Anthropic expands Google and Broadcom compute deal
Anthropic announced a new agreement with Google and Broadcom for multiple gigawatts of next-generation TPU capacity expected to come online starting in 2027. The company says the expansion will support its frontier Claude models and meet customer demand, with most of the new compute sited in the United States as an expansion of its November 2025 commitment to invest $50 billion in American computing infrastructure. Anthropic reports that run-rate revenue surpassed $30 billion, up from approximately $9 billion at the end of 2025, while the number of business customers spending more than $1 million annually rose from over 500 to more than 1,000 in less than two months. It will continue using AWS Trainium, Google TPUs, and NVIDIA GPUs, and says Claude remains available across Amazon Web Services, Google Cloud, and Microsoft Azure.
Announcing ClickHouse Managed Postgres on Google Cloud
ClickHouse Managed Postgres is entering Private Preview on Google Cloud, extending the fully managed, NVMe-backed Postgres service previously launched on AWS. The GCP deployment targets teams wanting Postgres for OLTP and ClickHouse for OLAP in one cloud, avoiding cross-cloud replication, egress costs, and separate networking and compliance management. It uses GCP Local SSD storage, native change data capture through ClickPipes, in-region networking, and pg_clickhouse, which pushes filters, joins, and aggregations to ClickHouse behind a single Postgres connection. Preview capabilities include zonal high availability with two standbys and quorum replication, point-in-time recovery, read replicas, PgBouncer, more than 90 Postgres extensions, monitoring, Query Insights, scheduled upgrades, and infrastructure-as-code support through OpenAPI, Terraform, and clickhousectl. Access is invitation-only via a waitlist, with availability in every GCP region where ClickHouse Cloud runs and hands-on migration support from Cloud SQL, AlloyDB, RDS, Aurora, Neon, or self-managed Postgres.
Supabase is now available in Gemini Enterprise
Supabase is now available as a prebuilt connector in Google Cloud Gemini Enterprise, allowing teams to query and take action on Supabase projects through natural-language prompts. After an organization-level connection, Gemini Enterprise calls the connector for live answers, does not store or index Supabase data, and can combine results with connected tools such as GitHub, Linear, Notion, Slack, and Salesforce. The integration supports questions about tables, database metrics, and table structure, while exposed tools are marked read-only or destructive. Business users connect through the connector menu, whereas Standard, Plus, and Frontline administrators use a client ID and secret in the admin console; the connector is available across all editions.
Driving Developer Excellence: Inside the Program Sprints
Google Cloud’s Gemini Enterprise developer experience (DevEx) Program pressure-tests enterprise AI tooling by walking fixed developer workflows without internal credentials or shortcuts, logging friction, and working with engineering on fixes. This sprint focused on governance across five dependency-ordered workflows: provisioning an agent identity, registering it, binding it to an Agent Gateway, applying policies and content safety, and verifying an authorized request with auditable decisions. Changes included documenting the Identity-Aware Proxy API prerequisite, fail-closed Model Armor extension examples, automatic allowance for essential Google-managed APIs, Private Service Connect and private Cloud DNS guidance, corrected IAM CEL syntax, clearer bind-time versus runtime evaluation, and ready-made Logs Explorer queries. The program says fixes are rigorously re-verified through continued user-journey testing to improve reliability, security, and end-to-end usability.
Anthropic partners with Google Cloud
Anthropic, an AI safety and research company, has selected Google Cloud as its cloud provider in a partnership intended to support the next phase of its work. The companies plan to co-develop AI computing systems, while Anthropic will use Google Cloud’s GPU and TPU clusters to train, scale, and deploy its AI systems to a larger set of people. The work centers on Claude, Anthropic’s AI assistant, and on building safe, steerable systems using techniques including Constitutional AI, which is intended to make AI technologies easier to rely on and understand. Anthropic says the infrastructure’s performance and scale will support a more robust platform for reliable, interpretable, and steerable AI systems.
Introducing Governance Hub: Intelligent, account-level governance over your Databricks estate
Databricks introduces Governance Hub, a beta account-level interface for platform and governance teams overseeing estates spanning hundreds of workspaces and multiple regions. It consolidates data health, access, AI usage, and cost information across AWS, Azure, and GCP, replacing scattered system tables, workspace views, and third-party tools with drill-downs for metadata gaps, principal access, token and model activity, and untagged spend. The hub also integrates with Genie, allowing users to ask governance questions in natural language and receive answers and recommendations grounded in governance data; future actions such as configuring policies and alerts are described as planned. Account admins enable it from the Account Console, with access varying by role and existing permissions respected, while upcoming features include performance and security insights and delegated recommendations.
MongoDB Atlas Joins Google Cloud Foundation Fabric (FAST): Secure Private Data Infrastructure in Minutes
Many organizations lack cloud fundamentals, making foundational setup complex and time-consuming; Google Cloud Foundation Fabric (FAST) is a GitOps-driven landing zone toolkit that accelerates establishing a production-ready Google Cloud organization. A new MongoDB Atlas module within FAST enables provisioning MongoDB Atlas clusters with Private Service Connect (PSC) for secure, private connectivity. PSC avoids VPC peering issues like CIDR conflicts and transitive routing, using a unidirectional consumer-producer model that respects project boundaries and routes traffic over Google’s private fiber network. The module automates cluster creation, PSC endpoint registration with Service Directory, and internal DNS resolution, integrating with Google Cloud KMS for customer-managed encryption keys. This integration reduces foundational setup from weeks to minutes, providing a secure data foundation that connects to BigQuery for real-time analytics and Vertex AI for GenAI workloads.
Build zero-trust AI agents with Google's Agent Development Kit
Connecting an autonomous Agent Development Kit (ADK) agent to databases, internal APIs, and code execution turns model decisions into production-state changes that system prompts cannot securely constrain. The post presents an open-source Customer Support & Returns Agent built with ADK and Gemini, using an example where prompt injection could produce an unauthorized refund or leak environment secrets. Its zero-trust design combines hardware-backed signing for database mutations, gVisor isolation with no network egress and resource limits for generated Python, and deterministic semantic gateways for prompts, tool calls, and database updates. Local equivalents use HMAC keys, a database ingress verifier, a constrained Docker runner, and Python validation tests, while production mapping uses Cloud KMS, HSM, and VPC Service Controls. The conclusion is that infrastructure-enforced boundaries let the model reason dynamically within strict limits.
HeyGen x Google Cloud: Bringing Avatar IV to TPUs
HeyGen and Google Cloud ported Avatar IV, an 18B-plus-parameter diffusion stack for streamed talking-head video, to an eight-chip Trillium (v6e) host. The pipeline uses two diffusion transformers and a VAE decoder, with FSDP weight sharding and Ulysses sequence parallelism across the mesh, and runs through torchax, JAX, XLA, and shape-specific Pallas kernels. Optimization targeted exposed all-to-all collectives, frame-misaligned sparse-attention blocks, serial softmax work, layout copies, scheduler choices, and custom-kernel cost estimates; changes were measured per video chunk against output-quality gates. The resulting pipeline was 1.86× faster than the first working TPU version, comparable in performance to the 8×H100 production setup and up to 25% more cost efficient per generated-video minute, while shipped changes preserved byte-identical output or passed defined similarity review.
Running Cost-Efficient Export Workloads at Uber
Uber’s offline export workloads often retrieve a small set of records from large, date-partitioned Apache Hudi tables, but narrow historical predicates can still trigger full-table scans. Repeatedly touching files, metadata, or Parquet footers keeps Google Cloud Storage objects hot, undermining Auto-Class tiering and increasing storage, retrieval, metadata-operation, egress, and latency costs. The approach combines Hudi column stats with sorting on predicate columns: metadata-table statistics prune files without opening them, while clustered records tighten file-level min/max ranges and make pruning more selective. A Hudi secondary index was evaluated but did not reduce file scans enough because records for a subject were spread across most files. The conclusion is that a one-time rewrite and ongoing Hudi table services can turn broad historical scans into lower-touch lookups, while future work targets multi-column layouts and automated ROI-based optimization.
Run Ray on TPU, Part 1: The foundations
Ray 2.55 makes Google Cloud TPUs a first-class accelerator in Ray, with official APIs, pre-built images, and core-library support rather than the earlier experimental path. The central constraint is TPU slicing: a multi-host workload must land on one intact slice because its hosts communicate through the slice’s ICI (Inter-Chip Interconnect). On GKE, the Ray Operator installs KubeRay and a TPU webhook; the webhook labels hosts, while Ray Core uses those labels to reserve a whole slice atomically through the public slice_placement_group() API, with topology and accelerator version as inputs. The guide shows node selection by TPU generation and topology, a multi-host numOfHosts field, and a RayCluster manifest that brings up one worker per host. Ray AI libraries such as Data, Train, and Serve generally invoke this placement primitive themselves, while direct use targets custom distributed workloads; the API is public but alpha.
Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search
Gemini Enterprise Agent Platform now natively integrates Parallel Web Systems as a web-grounding provider, expanding developers’ choice of sources for real-time, verifiable information. Grounding with Parallel Web Search connects Gemini models to Parallel’s structured, LLM-optimized search results, combines the provider’s web index with Gemini’s prompt decomposition and synthesis, and adds precise citation annotations. The capability is available through the Gemini API and Agent Studio, and can be subscribed to through Google Cloud Marketplace with usage charged on an existing Google Cloud invoice; zero data retention is available for sensitive workloads. Developers can make programmatic calls at scale, extract and cache web data, post-process results with other LLMs, and build catalog enrichment, autonomous-agent, and multi-agent workflows. Setup requires subscribing, accepting the terms, reviewing pricing, and selecting Parallel Web Search under Agent Studio’s partner-grounding settings.
Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning
The NVIDIA Nemotron Model Reasoning Challenge on Kaggle tasked over 5,000 participants with improving reasoning accuracy on Nemotron-3-Nano-30B using restricted LoRA adapters on Google Cloud G4 VMs. Top-performing competitors approached reasoning as an end-to-end engineering workflow by auditing synthetic chain-of-thought traces with programmatic solvers before training. Successful teams also compressed reasoning representations into compact signatures and bit-manipulation formats to prevent long chains from exceeding runtime token generation limits. Additionally, leading approaches separated precomputed reusable structures from live computation and conducted granular per-category validation to monitor regression and non-determinism. These results show that reasoning improvements stem from verifiable training data, compact context encoding, and structured validation rather than unconstrained data scaling.
Introducing Atlas Gen2 on AWS and GCP for M30+ Dedicated Clusters
MongoDB Atlas introduced Gen2 for M30+ Dedicated Clusters on AWS and GCP, addressing the common inefficiency of overprovisioning storage to meet IOPS requirements. Gen2 runs on newer ARM-based processors, delivering a statistically significant 15-20% reduction in CPU utilization and approximately 45% lower read and write latency in internal pilots. It introduces extended standard IOPS that can be provisioned independently from storage capacity, allowing teams to scale performance without adding unused storage and gain more precise control over infrastructure costs. No application changes or migration are required; teams retain the same Atlas API, connection strings, and operational model while benefiting from newer infrastructure underneath. Gen2 is available in supported AWS and GCP regions across APAC and Europe, with limited U.S. availability on GCP, and existing customers can evaluate upgrade opportunities for eligible M30+ clusters.
We terminated a TPU mid-training and it recovered in seconds: Introduction to elastic training with MaxText
Distributed training on multiple TPU machines normally fails as a whole when one worker disappears: collective communication times out, processes exit, and the job restarts from a checkpoint. The article demonstrates elastic training with JAX, MaxText, PathwayS, Orbax, and Cloud TPUs on GKE, using a single CPU-side controller that remains alive while TPU workers execute compiled programs. A Pathways elastic_retry wrapper catches the failure, cleans up partial state, restores the last viable checkpoint, and re-enters the training function without relaunching the workload; the demo returned to training in under two minutes, with no JobSet restarts, after losing 88 steps. It also contrasts pause-and-resume with replica resize, explains direct checkpoint persistence through ENABLE_PATHWAYS_PERSISTENCE=1, and describes planned snapshot-based recovery from host memory.
ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available
Google Cloud has launched the Google Cloud Workbench Notebooks extension for VS Code, allowing developers to use managed cloud notebook environments from a local IDE. Workbench, described as a managed Jupyter platform optimized for data science, can provide access to Google Cloud's AI-optimized infrastructure and high-performance compute when local machines need more capacity. The workflow connects through VS Code's Extensions view, the Jupyter extension, and a notebook's Select Kernel menu, where users choose Google Cloud, Workbench, authenticate, select a project, and choose an active instance. The extension runs notebooks on Workbench instances, preserves local settings and extensions, is fully open-sourced, and is available through the VS Code Marketplace, with GitHub contributions invited.
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