Grafana vs VictoriaMetrics
| Tagline | Observability and analytics dashboards for metrics, logs, and time series | Fast, cost-efficient time-series database and monitoring drop-in for Prometheus |
| Category | BI & Dashboards | Monitoring & Status Pages |
| Replaces | Tableau, Power BI, Datadog | Datadog |
| GitHub stars | 77k | 18k |
| Language | TypeScript | Go |
| License | AGPL-3.0 | Apache-2.0 |
| 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 | 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.
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
VictoriaMetrics
- Primarily a metrics backend; needs Grafana for dashboards and vmalert for alerting
- No logs/traces/APM in the core product (separate VictoriaLogs project for logs)
- No public status page or synthetic uptime checks
- Assembling a full observability suite requires multiple components
Bottom line
Choose Grafana if you want the lower-effort setup; choose Grafana for the larger community and ecosystem. Open each guide below for deploy steps and the full feature gap.
VictoriaMetrics
Fast, cost-efficient time-series database and monitoring drop-in for Prometheus