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We have big news for platform and AI infra teams: *Kong AI Gateway 2.0 is available today in private beta*. It runs on its own dedicated runtime, ships on its own release cadence, and carries a completely reimagined user experience designed around the way teams actually build with AI: models, MCP servers, and agents as first-class citizens, not plugins bolted onto an API gateway.

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

Learn how to use BigQuery to analyze Gemini Enterprise usage logs, audit compliance, and build Looker dashboards at scale for your organization.

Looking back on 2025, one idea anchors everything we set out to accomplish: make data accessible and trusted so organizations can generate better decisions for their business, their customers, and the world.

Performance engineering teams are already stretched. AI-accelerated development means more code, more releases, and more pressure on the people responsible for making sure it all holds up. Tricentis NeoLoad 2026.2 is built around a straightforward premise: performance validation must scale to match the pace of delivery, and that means more than just making specialists faster.

An effective Java debugging strategy lets us pause execution, inspect data, and observe real execution rather than relying on vague assumptions. The complexity of the Java Virtual Machine creates unique challenges, but a focused approach will turn this complexity to our advantage. This guide will equip you with the tools to do this, looking at: By the end, you’ll have a practical workflow to debug Java reliably across local and remote environments.

Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.

This is the fifth post in our "Features Sitting Idle" series, where we look at OctoPerf features that are already in your platform but often missed by the teams that would benefit the most. This one is for IT teams running OctoPerf on-premise. IT teams managing an on-premise OctoPerf instance regularly run into the same friction around SSO.

AI is becoming part of every stage of the testing lifecycle. Teams are using it to analyze requirements, design test cases, generate automation scripts, and accelerate execution activities that previously required significant manual effort. Within Xray, AI already helps transform Jira requirements into actionable test cases with AI Test Case Generation.