Grafana vs Prometheus
| Tagline | Observability and analytics dashboards for metrics, logs, and time series | Industry-standard metrics monitoring and alerting toolkit with PromQL |
| Category | BI & Dashboards | Monitoring & Status Pages |
| Replaces | Tableau, Power BI, Datadog | Datadog |
| GitHub stars | 76k | 65k |
| Language | TypeScript | Go |
| License | AGPL-3.0 | Apache-2.0 |
| Self-host difficulty | 2/5 Easy | 4/5 Involved |
| Deploy options | One-Click Docker Docker Compose Kubernetes Manual | Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 3 days ago | 4 days ago |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Grafana
- Oriented toward time-series and observability, not ad-hoc business analytics or pivot-style exploration
- No business-friendly visual query builder; dashboards assume knowledge of data sources and query languages
- Weak at relational/tabular BI reporting compared to Tableau or Power BI
- No semantic modeling layer; data modeling lives in the underlying sources
Prometheus
- No built-in dashboards UI; you must pair it with Grafana
- Long-term storage and horizontal scale need add-ons (Thanos, Cortex, Mimir)
- No logs, traces, or APM out of the box (metrics only)
- Steeper operational learning curve than turnkey Datadog
Bottom line
Choose Grafana if you want the lower-effort setup; choose Grafana for the larger community and ecosystem. Grafana has seen more recent development. Open each guide below for deploy steps and the full feature gap.