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Migrating your existing application load balancer infrastructure from an on-premises hardware solution to Cloud Load Balancing offers substantial advantages in scalability, cost-efficiency, and tight

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Model Releases

Near-100% Accurate Data for your Agent with Comprehensive Context Engineering

DGX agent

Agentic workflows are already used for initiating action. To be successful, agents typically need to combine multiple steps and execute business logic reflective of real-life decisions. But, as develo

model-releasesgoogle-cloud-ai

10 Apr 2026

QueryData helps agents turn natural language into queries for AlloyDB, Cloud SQL and Spanner

QueryData launches in preview today. It is a tool for translating natural language into database queries with near-100% accuracy. With QueryData, you can build agentic experiences across AlloyDB, Clou

Raising the security baseline: Essential AI and cloud security now on by default

The rapid evolution of AI is redefining industries, while also exposing organizations to new risks. At Google Cloud, we believe that modern cloud defense should have AI protection built in and accessi

What’s new with Google Cloud

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

A developer’s guide to architecting reliable GPU infrastructure at scale

Editor’s note: This blog post outlines Google Cloud’s GPU AI/ML infrastructure reliability strategy, and will be updated with links to new community articles as they appear. As we enter the era of mul

9 Apr 2026

Safety

Guardrails at the gateway: Securing AI inference on GKE with Model Armor

Enterprises are rapidly moving AI workloads from experimentation to production on Google Kubernetes Engine (GKE), using its scalability to serve powerful inference endpoints. However, as these models

safetygoogle-cloud-ai

9 Apr 2026

Hardware

How Estée Lauder Companies uses Cloud Run worker pools for its pull-based agentic workloads

Cloud Run has long provided developers with a straightforward, opinionated platform for running code. You can easily deploy request-driven web applications using Cloud Run services, or execute run-to-

hardwaregoogle-cloud-ai

9 Apr 2026

Experimenting with GPUs: GKE managed DRANET and Inference Gateway AI Deployment

Building and serving models on infrastructure is a strong use case for businesses. In Google Cloud, you have the ability to design your AI infrastructure to suit your workloads. Recently, I experiment

Applications

Google Cloud named a Leader in The Forrester Wave™: Sovereign Cloud Platforms, Q2 2026

applicationsgoogle-cloud-ai

In today’s global economy, data is a strategic asset. For many organizations — particularly those in highly regulated industries and the public sector — the ability to innovate with AI is often balanc

Tutorials

New GKE Cloud Storage FUSE Profiles take the guesswork out of configuring AI storage

In the world of AI/ML, data is the fuel that drives training and inference workloads. For Google Kubernetes Engine (GKE) users, Cloud Storage FUSE provides high-performance, scalable access to data st

tutorialsgoogle-cloud-ai

Openness without compromises for your Apache Iceberg lakehouse

Today, at the Apache Iceberg Summit in San Francisco, we are announcing the preview of read and write interoperability between BigQuery and Iceberg-compatible engines, including Trino, Spark, and othe