Kibana vs Metabase
| Tagline | Visualize and explore Elasticsearch data with powerful BI dashboards | Easy-to-use open-source BI and embedded analytics for everyone |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Power BI, Looker |
| GitHub stars | 20k | 48k |
| Language | TypeScript | Clojure |
| License | Elastic-2.0 | AGPL-3.0 |
| Self-host difficulty | 4/5 Involved | 2/5 Easy |
| Deploy options | Docker Docker Compose Kubernetes Manual | One-Click Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 1 month ago | 5 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.
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
Metabase
- Advanced data modeling, row-level security, and SSO are gated behind the paid Pro/Enterprise editions
- Charting and visualization depth is more limited than Tableau or Power BI
- No deep semantic modeling layer like Looker's LookML
- Performance can degrade on very large datasets without careful tuning or caching
Bottom line
Choose Metabase if you want the lower-effort setup; choose Metabase for the larger community and ecosystem. Metabase has seen more recent development. Open each guide below for deploy steps and the full feature gap.