Grafana vs Grafana OnCall
| Tagline | Observability and analytics dashboards for metrics, logs, and time series | Open-source on-call scheduling and incident alerting for engineering teams |
| Category | BI & Dashboards | Monitoring & Status Pages |
| Replaces | Tableau, Power BI, Datadog | Datadog, Pingdom |
| GitHub stars | 75k | 4k |
| Language | TypeScript | Python |
| License | AGPL-3.0 | AGPL-3.0 |
| Self-host difficulty | 2/5 Easy | 3/5 Moderate |
| Deploy options | One-Click Docker Docker Compose Kubernetes Manual | Docker Compose Kubernetes |
| Managed hosting | ||
| Last updated | 5 days ago | 1 month 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
Grafana OnCall
- Requires Grafana instance for alerts; not standalone
- Mobile app push notifications require Grafana Cloud relay
- Less mature than PagerDuty for complex multi-team routing
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.
Grafana OnCall
Open-source on-call scheduling and incident alerting for engineering teams