Operations | Monitoring | ITSM | DevOps | Cloud

You’re about to click "Merge" on a PR, but you feel more anxious about it than you used to. Why?

It's Monday afternoon and that feature you've been working on is mostly done. There's just one item still sitting untouched at the bottom of the ticket: "Add monitoring." You know you should. You also know the sprint ends tomorrow, nobody on the team is an observability expert, and figuring out what to measure—let alone how to write the PromQL for it—feels like a project all on its own. So it gets the same treatment it always does: "We'll add it when it breaks.".

Observability has traditionally been tacked on after your code hits production, but with agentic operations on the rise, that's no longer sustainable. Agents have dramatically increased the rate of change as they write more code, ship more changes, and operate more systems—all at a speed that compounds scale and complexity.

As AI agents accelerate software development and spin up applications at scale, visibility into what's happening behind the scenes, including query performance and database health, has never been more important. Gaining that level of insight requires observability that can keep pace.

If your network restricts inbound or outbound traffic, you likely maintain an allowlist of Grafana Cloud IP addresses so your systems and Grafana Cloud can talk to each other. Today we're introducing a new allowlists design: a single, structured API that replaces the collection of per-product lists we've published until now. If you don't use IP allowlisting—or you connect to Grafana Cloud over private connectivity such as AWS PrivateLink—nothing changes for you, and no action is needed.

Knowing what you're spending on observability is useful. Knowing which team, service, or project is driving that spend is what actually lets you act on that information. Cost attribution is a core part of how Grafana Cloud approaches cost management and optimization.

You've optimized your Largest Contentful Paint. Your Time to First Byte is under 200ms. Your Lighthouse scores are green. And yet, your checkout conversion rate is quietly dropping. A segment of users in Southeast Asia is churning. Your support team is fielding tickets about a form that "just doesn't work" and you have no idea which one. Traditional frontend performance monitoring tells you whether your application is fast. It doesn't tell you whether people are actually succeeding when using it.

We’re delighted to share that Grafana Labs has been named a Leader in the Gartner Magic Quadrant for Observability Platforms for the third consecutive year. Notably, we’re also positioned furthest in “Completeness of Vision” for the second year in a row.

When you're the on-call engineer and something breaks, you can quickly find yourself deep in a series of tools you don't regularly use—switching tabs, copying query results, and manually stitching together a picture of what's happening and why. People are increasingly turning to AI to get around this, but the results can be a mixed bag.

Modern applications can fail in many different ways, from performance regressions and frontend errors to systems that break under heavy load. Because no single testing or monitoring approach can catch every type of failure, effective reliability testing requires multiple layers that validate your application before, during, and after their release.