Operations | Monitoring | ITSM | DevOps | Cloud

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.

In January, we announced that Grafana Labs had assumed maintenance of the business intelligence (BI) plugins created by Volkov Labs, and committed to a six-month maintenance period. Today, we’re sharing an update: we're extending our maintenance commitment through the end of 2026. As announced earlier this year, that commitment includes maintaining compatibility with recent Grafana releases while handling bug fixes, security updates, and community contributions on a best-effort basis.

One of the primary reasons organizations adopt Grafana Cloud is to create a single pane of glass across the data they collect from self-hosted systems, cloud providers, and third-party platforms. Bringing those signals together enables richer correlations, reduces tool sprawl, and makes it easier for teams to understand what's happening across their environment. But as observability grows and becomes more centralized, access management becomes more important.

Many times, the hardest part of troubleshooting isn’t fixing the actual problem. It’s figuring out where to start. As engineers, it’s easy to lose count of how many times we’ve opened logs, then 10 metrics tabs, and another 10 tabs with trace queries, only to end up back in the logs trying to find a root cause.