Grafana vs Jaeger
| Tagline | Observability and analytics dashboards for metrics, logs, and time series | Distributed tracing system for monitoring microservice latency and dependencies |
| Category | BI & Dashboards | Monitoring & Status Pages |
| Replaces | Tableau, Power BI, Datadog | Datadog, Pingdom |
| GitHub stars | 75k | 20k |
| Language | TypeScript | Go |
| License | AGPL-3.0 | Apache-2.0 |
| Self-host difficulty | 2/5 Easy | 3/5 Moderate |
| Deploy options | One-Click Docker Docker Compose Kubernetes Manual | Docker 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
Jaeger
- Tracing only; no metrics or log aggregation built in
- Production deployments require Cassandra or Elasticsearch for storage at scale
- UI is functional but less polished than commercial APM products
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.
Jaeger
Distributed tracing system for monitoring microservice latency and dependencies