Grafana Campfire - Assistant powered Dynamic dashboards - (Grafana Community Call - August 2026)

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.

How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons

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.

When to Use Grafana Assistant vs. MCP vs. gcx: Part 3

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.

Grafana 13.2 release: easier ways to query and explore your data

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.

Knowledge Graph as context for LLMs: demonstrating decisive RCA and faster production performance

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.

From failed check to real user impact: Pairing Synthetic Monitoring and Frontend Observability in Grafana Cloud

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.

When to Use Grafana Assistant vs. MCP vs. GCX: Part 2

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.

Introducing the AI toolkit - build a SquaredUp plugin from a single prompt

When we introduced the Low Code Plugin (LCP) framework in February, the premise was simple: if a system has an API, you should be able to build a plugin for it — quickly, with minimal code, and in a way you can share with the community. The "AI-ready" part was deliberate. The framework was designed to work naturally with AI assistants, so the path from idea to working integration would be as short as possible. That design decision is now paying off.

Getting started with Microsoft Purview dashboards

Microsoft Purview is an enterprise-scale platform for managing data governance across your whole cloud estate. It is not just about ensuring the integrity of data stored in SQL databases — it spans the whole spectrum of data storage including blob storage, document databases, email and AI frameworks. It has an extensive list of features for organising and monitoring your enterprise data. This includes.

The finance dashboard I actually use, built from CloudZero and Campfire in an afternoon

Every finance person I know lives in the same loop approaching the end of the month, quarter, or fiscal year. Leadership wants to know where the financials will land (most times before the close has occurred). CS wants customer margins. Someone on the People team needs each department’s AI spend for an OKR review, and they need it quickly to make business decisions. Each answer sits in a different tool or a different spreadsheet, and I bounce across all of them several times a day.