Operations | Monitoring | ITSM | DevOps | Cloud

Most teams have telemetry from across their stack, but connecting those signals to understand what's happening and how it affects users isn't always easy. Critical context about service dependencies, ownership, and past incidents often lives across tools, runbooks, and conversations between team members. That’s why, today at ObservabilityCON 2026, we’re announcing new capabilities that advance full-stack observability across Grafana Cloud.

Earlier this year, we launched the Grafana Marketplace, a platform that gives independent software vendors, systems integrators, and developers in the Grafana community a new way to sell and distribute plugins built for Grafana. The Marketplace builds on our existing plugin catalog by enabling third-party partners and developers to earn revenue from their plugins and invest further in their development.

I remember the good old days, when troubleshooting application traffic often meant I could examine much of the conversation between a client and server in clear text. Not anymore. Today, nearly everything is encrypted. That is obviously a good thing for security and privacy, but it has also created a common misconception that packet analysis is no longer useful because engineers cannot see the application headers and payload.

Service mesh observability guide: the metrics Istio, Linkerd and Envoy proxies emit, what ambient mode changes, and why tracing still needs app changes. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

The first minutes of an incident are spent asking questions a monitoring tool can answer but rarely in the form you asked them: which servers are in trouble right now, is it the same thing as last night, what did memory do on the web servers before it started? Each answer is a few clicks away, on a different page.

AI dashboards use artificial intelligence to surface relevant metrics, generate visualizations, and add context to complex data. Learn how AI dashboards work, how they compare with traditional dashboards, and what they can mean for IT operations.

Yesterday, my partner told me about his new automated pipeline from a conversation recording to publication of a whole podcast, with social media blurbs and graphics and everything. It did more than he'd predicted the agent could do. "We are limited by what we can imagine," he said. Cheesy, but it kinda gave me goosebumps because it's (almost) true. Most of the experienced developers I know are concerned.