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By Fatjon Nebiu
Running Alloy as a single-instance sidecar is simple. Running it as a centralized gateway that absorbs the full telemetry stream of an enterprise platform—tens of millions of active series, terabytes of logs per day, and tens of thousands of trace spans per second—is a different challenge altogether. To get it right, you need deliberate capacity planning, honest load testing, and a monitoring setup that doesn't rely on the very thing you're testing.
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By Grafana Labs Team
Grafana 13.2 is here, bringing more improvements to help you and your team explore your data and get to insights faster. Download Grafana 13.2 In this post, we’ll highlight the latest updates to saved queries, a feature that lets teams share, discover, and reuse queries to get to trusted answers faster and help new teammates get up to speed. We’ll also explore how the new View panel sidebar makes exploring busy panels a breeze.
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By Sarah Constant
On the product team here at Grafana Labs, we consider AI agents our users, too. That’s why we set out to test how well agents can debug incidents across the full stack, and how much better they perform with Grafana Cloud’s Knowledge Graph vs. using raw telemetry alone. Our early results are promising. In one real incident we replayed 16 times each way, an agent with Knowledge Graph context found the correct root cause 15 times, compared with just once using raw telemetry alone.
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By Mark Meier
Say you get a support escalation about a page in the app that won’t load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you’ve run Grafana Cloud Synthetic Monitoring, you’ve been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it’s the inverse.
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By Lukasz Gut
Grafana Cloud Frontend Observability helps engineering teams quantify the end user experience by bringing metrics, logs, traces, and user session context to client-side web applications. Teams can monitor application health and performance over time, triage errors, and correlate frontend signals with backend telemetry to investigate issues across the stack.
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By İnanç Gümüş
As testing workflows become more AI-assisted, fast access to accurate documentation matters more than ever. Whether you're writing a new load test, troubleshooting an issue, or having an AI agent generate a script for you, you need reliable guidance that keeps pace with the way you work. But most documentation still lives in a browser. Every time you or your agent needs to verify an API or look up a best practice, you're forced to leave your terminal or editor and interrupt your workflow.
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By Yuna Verheyden
Tracing is one of the richest observability signals, but it's also noisy and susceptible to data bloat. In a busy system, the vast majority of traces describe the same healthy, fast, successful request over and over, so most organizations downsample their traces to cut costs. But that approach has consequences, since the sampling strategy you choose determines whether you get a faithful picture of your whole system, or just a smaller, blurrier copy of your busiest endpoints.
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By Thanos Karachalios
Here's a scenario that will likely sound familiar: You’re building an executive overview dashboard that you would put on a wall-mounted screen so the whole room can see how the business is doing at a glance. It’s for a Shopify online store, and displays a mix of business and application signals, including latency panels, error-rate panels, and a big stat panel for revenue-per-week. It looked great. But something is missing.
When a Terraform run feels slow, most teams are flying blind. The run log in HCP Terraform tells you what happened, but not what took so long—and it certainly doesn't roll up across hundreds of runs so you can spot a trend.
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By Jack Gordley
Observing fast-growing agentic workloads is no small feat, especially if you try to build your own monitoring stack or rely solely on tools built for a time before LLMs. At Grafana Labs, we know this all too well.
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By Grafana
Observability looks perfect in a slide deck – in practice, it's messier. In this panel, engineering leaders from Cyara, PlayHQ, and National Australia Bank share what really happened when they scaled observability: unexpected cloud bills, alert fatigue, a weekend database outage caught by an AI-assisted triage agent, and a vendor dispute settled by a single chart. They also cover moving beyond legacy tooling, using AI to close the PromQL skills gap, and what's next – from agentic SDLC integration to continuous profiling. Real stories, real numbers, real lessons.
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By Grafana
Many times, it feels like you're maintaining multiple versions of the same dashboard (with a slight modification), *OR* simply spending more time writing queries rather than actually looking at the actual data? In this Campfire community call, we're taking a deep dive into two things that are reshaping how Grafana dashboards get built: Dynamic Dashboards and the Grafana AI Assistant and showing you how to combine them to go from a blank canvas to a reusable, production-ready dashboard in minutes.
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By Grafana
When should you use gcx? If Grafana Assistant is the brain and Grafana MCP is the easy hand, gcx is the power hand. Built for AI agents working in the terminal, gcx gives them deep access across Grafana Cloud—so they can pull telemetry, verify code, automate workflows, and access places MCP doesn’t. Coding agents? gcx. Need the full Grafana Cloud surface? gcx. Automating in CI/CD? gcx. Here’s where it fits, and when to use it — explained by Nicole van der Hoeven.
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By Grafana
When should you reach for Grafana MCP? It’s one of the two “hands” in Grafana’s AI toolkit — and the easy one at that. MCP lets you bring Grafana into the tools you already use, like ChatGPT, Claude, or Cursor, without changing your workflow. No terminal? MCP. Want to stick with your favorite AI tool? MCP. Want easy tool discovery out of the box? MCP. Here’s where it fits, and when to use it — explained by Nicole van der Hoeven.
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By Grafana
Grafana Digital Experience Monitoring (DEM) brings Frontend Observability and Synthetic Monitoring together, so teams can go from symptom to root cause without bouncing between tools. In this video, Bukola, Senior Developer Advocate at Grafana Labs, demos two of the newest DEM features. Session Replay and the integration between Synthetic Monitoring and Frontend Observability.
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By Grafana
Within Grafana's AI offerings, there are three areas that can be a bit confusing. Assistant, MCP, and gcx. In this series, Nicole from the DevRel team talks about which one to use for which use case. This first video starts with Assistant.
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By Grafana
We will look at some new features: Call Tree, Heat Map, & Adaptive Profiles Can't comment in the chat? You may need to create a channel. Join us live for an introduction to flame graphs. We’ll cover what they are, how to read them, and how to use them to find performance bottlenecks in your applications. Bring your questions! Grafana Cloud is the easiest way to get started with Grafana dashboards, metrics, logs, traces, and profiles. Our forever-free tier includes access to 10k metrics, 50GB logs, 50GB traces and more.
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By Grafana
Understand the basic building blocks of the Grafana Stacks as how all the elements (tools) combine together to give you a complete visual to your platform Thanks for watching!
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By Grafana
We will look at some new features: trace diff and span pruning Can't comment in the chat? You may need to create a channel. Join us live for an introduction to flame graphs. We’ll cover what they are, how to read them, and how to use them to find performance bottlenecks in your applications. Bring your questions! Grafana Cloud is the easiest way to get started with Grafana dashboards, metrics, logs, traces, and profiles. Our forever-free tier includes access to 10k metrics, 50GB logs, 50GB traces and more.
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By Grafana
Friday, the last day of AI Week, is all about collaboration. Here's what we're announcing today! Check out grafana.ai for more details on everything we announce this week.
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Grafana provides a powerful and elegant way to create, explore, and share dashboards and data with your team and the world. Grafana is most commonly used for visualizing time series data for Internet infrastructure and application analytics but many use it in other domains including industrial sensors, home automation, weather, and process control.
Grafana has a robust plugin architecture built for extensibility. Visualize data from more than 40 data sources, including commercial databases and web vendors, and add new graph panels with rich data visualization options. There is built in support for many of the most popular time series data sources. It works with Graphite, Elasticsearch, Cloudwatch, Prometheus, InfluxDB and more.
Grafana Labs is the company behind Grafana, the leading open source software for visualizing time series data. Grafana Labs helps users get the most out of Grafana, enabling them to take control of their unified monitoring and avoid vendor lock in and the spiraling costs of closed solutions.