Cloud Computing

Leaders across industries around the world are asking: How do we harness all of this powerful technology effectively and at scale, to solve real problems, and drive value and impact, right now? Google has been building for this moment from the beginning; 25 years ago, Google created the front door to the internet, and today,

In freeskiing and snowboarding, traditional video replay shows you what happened during a complex aerial maneuver, but it fails to explain the physics of how it was possible. At the speed of the sport, it’s incredibly difficult to translate high-speed motion into actionable data—joint angles, rotational velocities, body compression. This requires tracking and analyzing a

More organizations are using natural language to query data instead of writing manual SQL. But moving an AI agent from a prototype to a production-ready tool requires rigorous, repeatable testing. Prism is an open-source evaluation tool for Conversational Analytics in the BigQuery UI and API, as well as the Looker API. It replaces unpredictable testing

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 integration within the Google Cloud ecosystem. Yet, a fundamental question often arises: “What about our current load balancer configurations?” Existing on-premises load balancer configurations often contain years of business-critical logic for

In today’s data-rich environment, organizations possess vast amounts of information. Yet, bridging the gap between that data and the users who need to make daily, informed decisions remains a challenge. Users need a single place to curate and analyze their data from the many different sources that impact their business each day. We are sharing

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 developers rush to deploy these autonomous agents, they are slamming into a wall: the compounding error problem of accuracy. To understand why agentic workflows require near-100% accuracy

In support of our mission to accelerate the developer journey on Google Cloud, we built Dev Signal: a multi-agent system designed to transform raw community signals into reliable technical guidance by automating the path from discovery to expert creation. In part 1 and part 2 of this series, we established the essential groundwork by standardizing the