Apache Superset vs Kibana
| Tagline | Enterprise-ready BI web app for data exploration and dashboards | Visualize and explore Elasticsearch data with powerful BI dashboards |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Looker, Power BI |
| GitHub stars | 73k | 20k |
| Language | TypeScript | TypeScript |
| License | Apache-2.0 | Elastic-2.0 |
| Self-host difficulty | 3/5 Moderate | 4/5 Involved |
| Deploy options | 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.
Apache Superset
- No native desktop authoring app like Tableau Desktop; all work happens in the browser
- Visualization customization is less polished and flexible than Tableau's drag-and-drop canvas
- No built-in semantic/modeling layer comparable to Looker's LookML (relies on external tools)
- Steeper learning curve and heavier infrastructure (Celery, Redis, metadata DB) for production
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 Apache Superset if you want the lower-effort setup; choose Apache Superset for the larger community and ecosystem. Apache Superset has seen more recent development. Open each guide below for deploy steps and the full feature gap.