Grafana vs Redash
| Tagline | Observability and analytics dashboards for metrics, logs, and time series | Connect, query, visualize, and share data from any SQL or NoSQL source |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Power BI, Datadog | Tableau, Looker, Power BI |
| GitHub stars | 77k | 29k |
| Language | TypeScript | 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.
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
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 Grafana if you want the lower-effort setup; 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.