grafana

Grafana is an open-source observability platform for querying, visualizing, and alerting on metrics and logs regardless of where the underlying data lives. Instead of locking you into one storage backend, it connects to a wide range of data sources and lets you build dashboards that pull from several of them at once. It's aimed at teams who need a shared, browsable view of system health: SREs and platform engineers building operational dashboards, developers debugging with logs and metrics side by side, and anyone who needs to turn raw time series data into something a team can read at a glance.

Key features

• Visualizations: client-side graphs and panels with a large set of display options, extendable through panel plugins for different ways of rendering metrics and logs.
• Dynamic dashboards: template variables appear as dropdowns at the top of a dashboard, so one dashboard definition can be reused across services, environments, or hosts without duplicating panels.
• Explore for metrics: ad-hoc querying and drilldown, with split view to compare different time ranges, queries, and data sources side by side.
• Explore for logs: switch between metrics and logs while preserving label filters, search across all logs, or stream them live.
• Alerting: define alert rules visually against your most important metrics, with notifications routed to systems like Slack, PagerDuty, VictorOps, or OpsGenie.
• Mixed data sources: specify a different data source per query within the same graph, including custom data sources, so a single panel can combine metrics from separate backends.
• Extensible plugin system: panel and data source plugins extend what Grafana can visualize and where it can pull data from.
• Documented contribution path: a contributing guide, developer guide, and style guide with Storybook cover how to set up a local environment, plus a list of beginner-friendly issues for newcomers who want to start contributing code.

Ideal use cases

Grafana fits teams that already have metrics or logs scattered across multiple systems (Prometheus, Loki, Elasticsearch, cloud provider metrics, SQL databases, and dozens of others) and need one place to look at all of it. It's a natural choice for infrastructure monitoring dashboards, on-call alerting, capacity planning views, and post-incident log investigation. Because dashboards are shareable and dynamic, it also works well as the visualization layer a whole team looks at daily rather than a one-off report.

Teams evaluating whether Grafana fits their stack before committing to a deployment can try play.grafana.org, a live instance with sample dashboards, without installing anything. For ongoing support once running, the project maintains discussion forums for specific questions and an official Slack community for broader conversation, alongside a blog for release and feature updates.

It is not a metrics storage engine or a log aggregator on its own. Grafana visualizes and alerts on data; it needs a data source such as Prometheus, Loki, or a database behind it to actually collect and store that data. If you're looking for a single all-in-one collector-plus-dashboard product with no separate storage layer to run, you'll still need to stand up and maintain that backend yourself, which is worth factoring into the setup effort.

Installation

The README points to Grafana's own installation guides for platform-specific instructions rather than listing every command inline. Standard installation paths include:

Docker (quickest way to try it locally):

docker run -d -p 3000:3000 grafana/grafana

Then open http://localhost:3000 in a browser. Default admin credentials are set on first login.

For Linux package managers, official APT and YUM repositories are documented in Grafana's installation guides, and binaries for other platforms are available from the releases page. For anyone who wants a hosted option instead of running it themselves, Grafana Cloud offers a managed version, and play.grafana.org lets you try the interface without installing anything first.

Because Grafana is a data visualization and alerting layer, a working install typically also means connecting at least one data source (Prometheus, a SQL database, Loki, or similar) through the admin UI after the server is running, before dashboards will show real data.