Dashy vs Grafana
| Tagline | Feature-rich homelab homepage with easy YAML configuration and a polished UI | Observability and analytics dashboards for metrics, logs, and time series |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Looker, Power BI | Tableau, Power BI, Datadog |
| GitHub stars | 25k | 74k |
| Language | Nodejs | TypeScript |
| 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
Grafana
- Oriented toward time-series and observability, not ad-hoc business analytics or pivot-style exploration
- No business-friendly visual query builder; dashboards assume knowledge of data sources and query languages
- Weak at relational/tabular BI reporting compared to Tableau or Power BI
- No semantic modeling layer; data modeling lives in the underlying sources
Bottom line
Both are a similar lift to self-host; choose Grafana for the larger community and ecosystem. Grafana 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