Operations | Monitoring | ITSM | DevOps | Cloud

To simulate real user behavior, performance tests often rely on API keys, tokens, or credentials to interact with real systems. But as your testing suite grows, this sensitive data can start to sprawl across scripts, configs, and environments, increasing the risk of exposure and making tests harder to manage and maintain. To address this challenge, we’re rolling out secrets management for Grafana Cloud k6, the fully managed performance testing platform powered by k6 OSS.

The way you write code is changing, which means the way you observe your systems and respond to issues needs to change, too. Engineers today spend much of their day working via command line, as agentic tools like Cursor and Claude Code have become highly effective at handling many day-to-day engineering tasks. This greatly accelerates code generation, but it doesn't solve for the context switching that comes when you have to jump into another tool that's not part of this new, faster workflow.

Part of what makes Cloud Provider Observability in Grafana Cloud really useful is that it gives you prebuilt dashboards and drill-downs for AWS, Azure, and Google Cloud. Out of the box you get service overviews, instance-level views, and quick links to explore your data. However, you might already have dashboards you trust, want a view tailored to your team’s workflow, or need to change which panels show up when you drill into a single instance.

GrafanaCON 2026 kicked off in Barcelona, which is a fitting city to reveal the latest updates in Grafana 13. In 2013, Grafana Labs Co-founder Torkel Ödegaard made the first commit for what would become Grafana while he was on vacation in the Catalan city. "I was traveling here for the Christmas holiday and I got a cold and spent most of the day in bed coding and working on Grafana," said Torkel during the opening keynote of GrafanaCON, our biggest community event of the year.

The observability industry has developed great tools for using metrics, logs, traces, and profiles to monitor the cloud native applications that have dominated the last decade of software development. But when it comes to understanding what an AI system is actually doing, we’re often left reading raw conversations, guessing at quality, and reacting too late. And that’s a problem.

Evaluating agents is hard. Verifying observability tasks is harder. Yes, AI agents have gotten dramatically and quantifiably better at coding and tool use, but observability presents a different kind of challenge. In a real incident, the hard part is rarely just writing a query. It's deciding which signal matters, figuring out whether a spike is noise or symptom, correlating metrics with logs and traces, and sometimes making a change in Grafana without breaking the dashboard another engineer depends on.

Who says 13 is unlucky? With the release of Grafana 13, we're giving the community the most streamlined, flexible, and intuitive Grafana experience yet. Unveiled during the opening keynote of GrafanaCON 2026, the latest major release is all about helping you get value from your data faster, whether you’re spinning up dashboards, operating Grafana at scale, or extending the platform as your requirements change. Download Grafana 13.

If you're running Databricks workloads, you've probably asked yourself these types of questions: How much is this costing me? Why did that job fail last night? Why are my dashboard queries suddenly slow? We've been there, too. Databricks is fantastic for data engineering, ML, and analytics. But once you start running jobs, pipelines, and SQL queries at scale, you need a way to keep tabs on what's happening. That's why we built the Databricks integration for Grafana Cloud.

Alerts are meant to help teams respond quickly to problems, but too often they arrive without enough context to be immediately useful. An alert that says “CPU usage is high” still leaves the on-call engineer asking critical follow-up questions: Which service? Which environment? Where do I look next? Validating the alert and triaging the situation is the first step for every engineer. It's a manual step that takes time, extending every potential incident.

The Kubernetes Monitoring Helm chart is the easiest way to send metrics, logs, traces, and profiles from your Kubernetes clusters to Grafana Cloud (or a self-hosted Grafana stack). And version 4.0 is the biggest update the chart has ever received. Representing nearly six months of planning and development, it's designed to solve real pain points that users have hit as their monitoring setups have grown.