Operations | Monitoring | ITSM | DevOps | Cloud

You're investigating checkout errors when someone asks who's coordinating the incident. Support needs an update, and another responder is looking for the runbook. You stop what you're doing to get everyone pointed in the same direction. They're all reasonable requests, but each one takes your attention away from understanding the problem. Yes, your team has a process for handling incidents, but carrying it out is another job for someone who's already busy.

You've just finished a deployment, and though your service is up, you start to notice some requests are getting slower. To address the lag, someone—or, more likely, some group of people on your team—needs to watch the dashboards, decide whether the change matters, investigate what happened, and turn the findings into a fix. At each step you're gathering context—and often explaining that context again to the next person or tool.

Today at ObservabilityCON 2026 in San Francisco, we unveiled a range of new Grafana Cloud capabilities designed to reshape cloud economics, bring richer context and deeper visibility across your systems and end-user experiences, and help teams move toward autonomous operations. Check out the keynote to learn more about the latest updates to the open agentic observability cloud, and read on for a quick recap of all the news from ObservabilityCON 2026.

When you ask Grafana Assistant to build a dashboard or investigate an alert, you're doing so because there's work to be done. To do that work effectively, you need the right data, changes you can inspect, and conclusions supported by evidence. You also want Assistant to avoid mistakes and act quickly. Over the past six months, our team has worked hard to improve what makes those outcomes possible.

Adaptive Metrics , which is part of the Adaptive Telemetry suite, looks at how your metrics are actually used and recommends how much detail you should keep for each metric. This helps you cut costs while still retaining the data you actually need. Last year we added auto-apply capabilities that let you implement those recommendations automatically, but only with coverage-first behavior, maximizing coverage of whatever you're querying.

When you’re on the go, your phone is often the only observability tool you have. And what you can do with it in the first few minutes matters. With the incredible innovation that has gone into mobile CPUs, GPUs and displays, a phone is a powerful device where you can immediately see what’s happening and start acting on it. And the Grafana mobile app now allows you to both see and act.

You’ve got 10 minutes before your next meeting when your phone starts ringing. You’re being paged because the frontend is erroring with failed API requests. You open one tool to see what fired, then another to figure out what’s going on. You message a teammate in Slack to see if they’re noticing the same thing. That’s a lot of context switching, and it’s still how a lot of teams' incident response works today. Opening another tool and catching someone up takes time.

If you’ve built your telemetry pipelines around OpenTelemetry Collectors, you’ve already invested in a collector distribution, YAML configuration, and a deployment model that fits your infrastructure. As that deployment grows, managing it means keeping shared configuration consistent, accommodating different workloads, and understanding whether your collectors are healthy. Fleet Management in Grafana Cloud brings those tasks together in one place.

As your use of Grafana Cloud Synthetic Monitoring grows, so does the number of checks you need to manage across services, environments, and teams. Eventually, a single flat list of checks becomes difficult to navigate, and even simple questions get harder to answer: Which checks belong to the payments team? Can I disable everything in staging during a maintenance window? Who should be able to edit the checks for this service?

Building on the major release of Tempo 3.0, Tempo 3.1 is here, delivering community-contributed Kafka client improvements, query-based trace redaction, sampling-aware TraceQL metrics, and more. Together, the updates in 3.1 make it easier to operate Tempo, get accurate insights from your trace data, and investigate issues more efficiently. You can continue reading and check out the video below to learn more about the latest features.