Overview
Prometheus is a time-series database that scrapes and stores metrics with its own query language and alerting engine, while Grafana is a visualization layer that queries Prometheus (and many other backends) to render dashboards. They’re often deployed together, not as alternatives, because each solves a different half of the observability pipeline.
Comparison Diagram
Comparison Table
| Aspect | Prometheus | Grafana |
|---|---|---|
| Primary purpose | Collect, store, and query time-series metrics | Visualize and correlate data from one or more sources |
| Data collection | Pull-based scraping of HTTP /metrics endpoints on a schedule | None — has no collector, relies entirely on configured data sources |
| Data storage | Built-in on-disk time-series database (TSDB) | Stateless; stores only dashboard/config metadata, not metric data |
| Query language | PromQL, native to its own TSDB | Delegates to whatever query language the connected data source uses |
| Supported data sources | Only its own TSDB (federation aside) | 150+ backends including Prometheus, Loki, InfluxDB, Elasticsearch, SQL |
| Alerting | Native alerting rules evaluated in-server, routed via Alertmanager | Unified alerting engine that layers on top of any connected data source |
| Visualization | Minimal built-in expression browser, no dashboarding | Rich, customizable panels, graphs, and shareable dashboards |
| Typical deployment | Runs per cluster/environment alongside exporters | Central server pointing at multiple backends across teams |
Key Differences
- Prometheus owns the TSDB; Grafana holds no metric data of its own.
- Prometheus scrapes targets directly; Grafana only issues read queries.
- Prometheus alerting runs server-side via Alertmanager; Grafana’s alerting spans any connected source.
- Grafana supports multi-source dashboards, unlike Prometheus’s single-source expression browser.
- Neither replaces the other — most stacks run both together.
When to Use Each
Prometheus
- Kubernetes metric scraping: Prometheus’s pull model and service discovery are purpose-built for scraping dynamic pods and services.
- Threshold-based alerting: Its native alerting rules and Alertmanager routing don’t require any external visualization layer.
- Standalone metrics storage: When you just need a reliable, queryable TSDB for your own microservices without a UI requirement.
Grafana
- Cross-team observability dashboards: Grafana can pull Prometheus metrics, Loki logs, and traces into one shared view.
- Mixed-backend visualization: Ideal when metrics live in multiple systems (Prometheus, InfluxDB, SQL) that need a single pane of glass.
- Executive/team-facing reporting: Its polished panels and sharing features suit stakeholders who never touch PromQL directly.