Operations | Monitoring | ITSM | DevOps | Cloud

Grafana Tempo 2.9 is now available, delivering MCP server support, TraceQL performance improvements, and more. Watch the video below to see the Tempo MCP server in action and learn how to speed up TraceQL metrics queries, or continue reading to get a quick overview of these and other updates. The Grafana Tempo 2.9 release notes and changelog provide more in-depth details and include all of the changes that came with this release.

When you start building an observability stack, it’s natural to reach for logs first. They’re familiar, easy to generate, and often already part of a developer’s workflow. And sending logs to a centralized system feels like a quick win, too. Simply add a log shipper, and voila, your application is observable.

This meeting could’ve been an email. It’s a phrase most of us have said (or at least thought) at some point in our careers. For me, that realization hit years ago while working for a government organization. I’d frequently sit through long, agendaless meetings that seemingly went nowhere. I wasn’t sure why I was there. And because I’m an engineer at heart, I started to wonder: what were these meetings actually costing the organization?

In observability, we’ve traditionally been taught to think in terms of pillars, namely logs, metrics, and traces (and more recently, profiles). But pillars are rigid and disconnected. They don’t reflect how modern systems actually work or how we troubleshoot in real time. So let’s change that.

When you operate a restaurant, the last thing you want to do is shut your doors and turn away guests and staff because of some technology failure. And if you’re the one providing that tech, it’s your job to make sure that doesn’t happen. “For us, observability is about a lot more than just dashboards and alerts.

The benefits of observability are clear: deep visibility into system health, faster troubleshooting, and improved reliability (to name a few). But what’s equally clear is that, as organizations scale and evolve their observability strategies, they need a way to tap into these benefits without runaway costs. According to Grafana Labs’ 2025 Observability Survey, 74% of respondents say cost is a top priority for selecting tools.

In today’s fast-paced development environment, it’s critical that you understand what’s happening in your CI/CD pipeline. And yet, many teams struggle with fragmented tooling that makes it difficult to get a holistic view of their dev lifecycle. For example, if you’re using GitLab for CI/CD and Grafana for observability, you’ve probably faced this challenge: how do you bring your GitLab events into your existing observability and alerting infrastructure?

When it comes to observability, more data doesn’t always mean more clarity. In fact, as telemetry volumes grow, it only becomes more difficult to discern the signals from the noise and to keep overall costs in check. This is exactly why we built Adaptive Telemetry, a suite of features in Grafana Cloud that analyzes how your telemetry is used and then automatically recommends actions like aggregating, sampling, dropping, or reducing low-value data.

When an incident lands in your lap, you’ll often start with a lot of questions: Why is latency so high? What’s causing this outage? How much money are we losing at this very moment? The uncertainty—and the pressure to quickly find answers—has always been one of the more nerve wracking parts of being an on-call engineer, but it doesn’t have to be that way any more.

Today at ObservabilityCON 2025 in London, we unveiled a number of exciting announcements and updates to Grafana Cloud that reimagine SaaS economics, simplify the complexity of running your observability stack at scale, and provide AI tooling that’s actually useful. (Root cause analysis via chatbot? Yes, please!) Check out the keynote to learn more about how we’re helping you do more with the open observability cloud, and read on for a quick recap of all the news from ObservabilityCON 2025.