Grafana vs Kibana
| Tagline | Observability and analytics dashboards for metrics, logs, and time series | Visualize and explore Elasticsearch data with powerful BI dashboards |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Power BI, Datadog | Tableau, Looker, Power BI |
| GitHub stars | 75k | 20k |
| Language | TypeScript | TypeScript |
| License | AGPL-3.0 | Elastic-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 | 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
Kibana
- Tightly coupled to Elasticsearch; not useful without an ES cluster
- License changed from Apache-2.0 to Elastic-2.0 in 2021, limiting some redistributions
- Resource-heavy; full ELK stack demands significant RAM and storage
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.