Dashy vs Metabase
| Tagline | Feature-rich homelab homepage with easy YAML configuration and a polished UI | 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 | 25k | 48k |
| Language | Nodejs | Clojure |
| License | MIT | AGPL-3.0 |
| Self-host difficulty | 2/5 Easy | 2/5 Easy |
| Deploy options | Docker Docker Compose Manual | One-Click Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 2 days ago | today |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Dashy
- No analytical data visualization, BI queries, or database connectivity
- Multi-user support is basic; no proper RBAC or team workspaces
- Service auto-discovery requires manual YAML entries; no Docker auto-detection like Homepage
- Not suitable for business reporting or data-driven dashboards
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
Both are a similar lift to self-host; 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.
Dashy
Feature-rich homelab homepage with easy YAML configuration and a polished UI