Metabase vs Redash
| Tagline | Easy-to-use open-source BI and embedded analytics for everyone | Connect, query, visualize, and share data from any SQL or NoSQL source |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Power BI, Looker | Tableau, Looker, Power BI |
| GitHub stars | 49k | 29k |
| Language | Clojure | Python |
| License | AGPL-3.0 | BSD-2-Clause |
| Self-host difficulty | 2/5 Easy | 3/5 Moderate |
| Deploy options | One-Click Docker Docker Compose Kubernetes Manual | Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | yesterday | 12 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.
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
Redash
- SQL-centric: limited value for non-technical users versus Tableau/Power BI drag-and-drop
- Visualization variety and interactivity are basic compared to leading commercial BI
- No semantic modeling layer and limited governance/RBAC features
- Development pace slowed for a period after the Databricks acquisition; community-driven releases
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.