Apache Superset vs Redash
| Tagline | Enterprise-ready BI web app for data exploration and dashboards | Connect, query, visualize, and share data from any SQL or NoSQL source |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Looker, Power BI |
| GitHub stars | 75k | 29k |
| Language | TypeScript | Python |
| License | Apache-2.0 | BSD-2-Clause |
| Self-host difficulty | 3/5 Moderate | 3/5 Moderate |
| Deploy options | 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.
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
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
Both are a similar lift to self-host; 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.