DataLens vs Grafana
| Tagline | Yandex's open-source BI and data visualization system | Observability and analytics dashboards for metrics, logs, and time series |
| Category | BI & Dashboards | BI & Dashboards |
| Replaces | Tableau, Power BI, Looker | Tableau, Power BI, Datadog |
| GitHub stars | 1.7k | 77k |
| Language | TypeScript | TypeScript |
| License | Apache-2.0 | AGPL-3.0 |
| Self-host difficulty | 3/5 Moderate | 2/5 Easy |
| Deploy options | Docker Compose Kubernetes Manual | One-Click Docker Docker Compose Kubernetes Manual |
| Managed hosting | ||
| Last updated | 22 days ago | 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.
DataLens
- Ships with a limited set of connectors (ClickHouse, PostgreSQL) compared to commercial BI
- Documentation and community are smaller and partly Russian-language oriented
- Multi-service architecture makes self-hosting more involved than lightweight alternatives
- Fewer advanced governance, modeling, and enterprise integrations than Tableau/Power BI
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
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.