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Note: This post originally published in October 2023 and was updated in May 2026 to include new methods and options for embedding Grafana dashboards. Grafana dashboards are powerful and flexible tools for observing applications and infrastructure, so it’s no surprise we get a lot of questions from the community about how to embed them into their web applications.

For years, teams have relied on k6 to take a more proactive approach to performance testing, ensuring they can catch issues early and deliver more reliable user experiences. That approach has helped make k6 one of the most widely used performance testing tools in the open source community today, with more than 30k stars on GitHub. Last year, we introduced k6 1.0, a major release that brought TypeScript support, native extensions, revamped test insights, and production-grade stability guarantees.

Most platform and observability teams have logs they know are noise. These could be throwaway health check logs, forgotten DEBUG logs, or verbose INFO logs from little used services that only serve to inflate your bill. Regardless of what they contain and why they're there in the first place, the hard part is getting rid of them. Centralized teams want to easily and quickly prevent these logs from being ingested, without having to work with toilsome infrastructure change management to do so.

So your database is slow. Now what? Grafana Cloud Database Observability already gives you visibility into your SQL queries with RED metrics, individual execution samples, wait event breakdowns, table schemas, and visual explain plans. But visibility is just the starting point. You can see that a query's P99 latency spiked, but what should you do about it? You can see wait events like wait/synch/mutex/innodb firing, but what does that actually mean?

When an unexpected alert fires these days, most engineers' first move is to ask their AI assistant for help.You ask why your checkout service is slow and the assistant gets to work, but it can't get any meaningful insights—at least not quickly—without the proper guidance. So, the next thing you know you're sharing deals about your existing data sources, the services you have running, how they connect, which labels and metrics matter, and on and on.

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.