Apache Superset vs ryot
| Tagline | Enterprise-ready BI web app for data exploration and dashboards | Track your media, fitness, and life facets in one self-hosted application |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Looker, Power BI |
| GitHub stars | 75k | 3.6k |
| Language | TypeScript | Docker |
| License | Apache-2.0 | GPL-3.0 |
| Self-host difficulty | 3/5 Moderate | 3/5 Moderate |
| Deploy options | Docker Docker Compose Kubernetes Manual | Docker Docker Compose |
| Managed hosting | ||
| Last updated | yesterday | yesterday |
| 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
ryot
- No business analytics or arbitrary data source connectivity
- No mobile native app; relies on Progressive Web App
- Social/sharing features are limited compared to Goodreads or Letterboxd
- No collaborative or multi-household tracking support
Bottom line
Both are a similar lift to self-host; choose Apache Superset for the larger community and ecosystem. Open each guide below for deploy steps and the full feature gap.