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Create full monitoring dashboards from a single prompt. Grafana Assistant analyzes your stack, builds key metrics instantly, and suggests next steps, so you skip complex queries and move straight to actionable insights.
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Ask a question, get a system-wide answer. Grafana Assistant maps your architecture, builds service diagrams, and runs queries behind the scenes, so you can troubleshoot faster without writing complex code or hunting through dashboards.
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Query fair usage in Grafana Cloud: What it is and how it affects your logs observability practice
In Grafana Cloud we use a simple yet generous formula that lets you query up to 100x your monthly ingested log volume in gigabytes for free. This works for the vast majority of our customers, but if you aren’t careful and strategic with your usage, you could find yourself with an overage bill.
Observability in Go: Where to start and what matters most
Sometimes the hardest part of debugging a system isn’t fixing the problem—it’s figuring out what’s actually happening in the first place.
Grafana Cloud Demo in Under 5 minutes | Full Stack Observability and more
Overview & demo of how Cloud provides an end to end Observability Platform that empowers users who have adopted open standards like or to improve their systems reliability using & a shift left approach with performance testing while optimizing their observability costs.
k8s-monitoring-helm Chart Office Hours (March 2026)
In the March edition of the Kubernetes Monitoring Helm chart office hours, we discuss the version 4.0 major release, the upcoming 4.1 release and features, and we discuss the upcoming deprecation of the 1.x and 2.0 versions.
Finding performance bottlenecks with Pyroscope and Alloy: An example using TON blockchain
Performance optimization often feels like searching for a needle in a haystack. You know your code is slow, but where exactly is the bottleneck? This is where continuous profiling comes in. In this blog post, we’ll explore how continuous profiling with Alloy and Pyroscope can transform the way you approach performance optimization.
From raw data to flame graphs: A deep dive into how the OpenTelemetry eBPF profiler symbolizes Go
Imagine you're troubleshooting a production issue: your application is slow, the CPU is spiking, and users are complaining. You turn to your profiler for answers—after all, this is exactly what it's built for. The profiler runs, collecting thousands of stack samples. eBPF profilers, including the OpenTelemetry eBPF profiler, operate at the kernel level, so they capture raw program counters: memory addresses pointing into your binary.