Umami vs Weights & Biases — Which One Wins?
A detailed, side-by-side comparison of Umami and Weights & Biases to help you pick the right tool for your workflow.
Quick Verdict
Weights & Biases takes the lead with a 4.9 rating and is best for ml teams that need to track, compare, and reproduce experiments. Umami (4.4) is the better pick if you need developers who want free self-hosted analytics.
Side-by-Side Comparison
| Criteria | Umami | Weights & Biases |
|---|---|---|
| Rating | ★★★★ 4.4(57) | ★★★★★ 4.9(12) |
| Pricing Model | open-source | freemium |
| Starter Price | $9/mo (cloud) | $50/user/mo |
| Free Tier | Yes | No |
| Platforms | web, docker | Web |
| Learning Curve | medium | moderate |
| API Available | Yes | Yes |
| Best For | Developers who want free self-hosted analytics | ML teams that need to track, compare, and reproduce experiments |
| Verdict | recommended | recommended |
Feature Checklist
| Feature | Umami | Weights & Biases |
|---|---|---|
| Self-hostable | — | |
| Privacy-focused | — | |
| Custom events | — | |
| Multiple websites | — | |
| Realtime dashboard | — | |
| Experiment tracking | — | |
| Model evaluation | — | |
| Dataset versioning | — | |
| Collaborative dashboards | — | |
| Hyperparameter sweeps | — | |
| Artifact management | — |
Umami
Pros
- ✓Free and open source
- ✓Beautiful clean UI
- ✓Easy to self-host
Cons
- ✕Self-hosting requires technical skill
- ✕Fewer features than GA
- ✕Cloud tier is limited
Weights & Biases
Pros
- ✓Industry standard
- ✓Excellent visualizations
- ✓Good free tier
- ✓Comprehensive artifacts
Cons
- ✕ML-specific only
- ✕Enterprise pricing steep
- ✕Learning curve for full features
The Bottom Line
Both Umami and Weights & Biases are solid tools in the Analytics & Data space. Weights & Biases edges ahead with a stronger overall rating (4.9 vs 4.4) and is the better choice for ml teams that need to track, compare, and reproduce experiments. However, if you prioritize developers who want free self-hosted analytics, Umami is worth serious consideration. We recommend trying the free tier or trial of each before committing.