Operations | Monitoring | ITSM | DevOps | Cloud

Setting up website monitoring has always meant a small but annoying detour. You spot a page worth watching, copy the URL, switch tabs, log into your monitoring tool, paste, configure, save. By the time you’re done, you’ve lost whatever train of thought sent you there in the first place. We’re fixing that. Today we’re excited to announce the ChangeTower Chrome Extension — now open for waitlist signups.

UK organizations are increasingly required to design systems that account for data residency requirements, ensuring that operational data remains within national boundaries. Many teams already run their applications on AWS infrastructure in the UK, but telemetry data can still be processed outside the region, creating gaps in visibility. Datadog’s upcoming UK availability zone solves this by keeping telemetry data in the same region as the workloads that generate it.

Developers and SREs who rely on Microsoft Azure DevOps often face fragmented workflows when investigating issues or reviewing code quality. Troubleshooting an error can require jumping between observability tools and source code repositories as you manually connect traces, stack frames, and commits. At the same time, security vulnerabilities, misconfigurations, and flaky tests may go undetected until later stages of the software delivery life cycle (SDLC), where they are more costly to fix.

For years, infrastructure stability could be approximated through static limits. If CPU utilization exceeded a defined percentage or response time crossed a fixed boundary, risk was assumed to increase in a predictable way. Monitoring systems were designed around that assumption, and for contained environments, it largely held true.

Who says 13 is unlucky? With the release of Grafana 13, we're giving the community the most streamlined, flexible, and intuitive Grafana experience yet. Unveiled during the opening keynote of GrafanaCON 2026, the latest major release is all about helping you get value from your data faster, whether you’re spinning up dashboards, operating Grafana at scale, or extending the platform as your requirements change. Download Grafana 13.

Evaluating agents is hard. Verifying observability tasks is harder. Yes, AI agents have gotten dramatically and quantifiably better at coding and tool use, but observability presents a different kind of challenge. In a real incident, the hard part is rarely just writing a query. It's deciding which signal matters, figuring out whether a spike is noise or symptom, correlating metrics with logs and traces, and sometimes making a change in Grafana without breaking the dashboard another engineer depends on.

The observability industry has developed great tools for using metrics, logs, traces, and profiles to monitor the cloud native applications that have dominated the last decade of software development. But when it comes to understanding what an AI system is actually doing, we’re often left reading raw conversations, guessing at quality, and reacting too late. And that’s a problem.

GrafanaCON 2026 kicked off in Barcelona, which is a fitting city to reveal the latest updates in Grafana 13. In 2013, Grafana Labs Co-founder Torkel Ödegaard made the first commit for what would become Grafana while he was on vacation in the Catalan city. "I was traveling here for the Christmas holiday and I got a cold and spent most of the day in bed coding and working on Grafana," said Torkel during the opening keynote of GrafanaCON, our biggest community event of the year.

Almost every production application uses a number of different tools and libraries,whether that’s a library to communicate with a database, a cache, or frameworks like Nest.js or Nitro. To be able to observe what’s going on in production, application developers reach out for Application Performance Monitoring (APM) tools like Sentry. But there’s an inherent problem: the performance data that APM tools need is most often not coming natively from the libraries themselves.

GPU observability isn't one thing - it's eight connected layers from silicon to cost. See why correlation across layers is what cuts debugging from 2 hours to 2 minutes, and why most teams instrument only one or two.