Langfuse vs Weights & Biases — Which One Wins?
A detailed, side-by-side comparison of Langfuse 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. Langfuse (4.5) is the better pick if you need ai engineering teams that need observability into their llm application performance and costs.
Side-by-Side Comparison
| Criteria | Langfuse | Weights & Biases |
|---|---|---|
| Rating | ★★★★★ 4.5(54) | ★★★★★ 4.9(12) |
| Pricing Model | open_source | freemium |
| Starter Price | $59/mo | $50/user/mo |
| Free Tier | No | No |
| Platforms | Web | Web |
| Learning Curve | moderate | moderate |
| API Available | Yes | Yes |
| Best For | AI engineering teams that need observability into their LLM application performance and costs | ML teams that need to track, compare, and reproduce experiments |
| Verdict | recommended | recommended |
Feature Checklist
| Feature | Langfuse | Weights & Biases |
|---|---|---|
| LLM tracing and debugging | — | |
| Cost tracking | — | |
| Prompt management | — | |
| Evaluation framework | — | |
| Analytics dashboard | — | |
| Self-hosting option | — | |
| Experiment tracking | — | |
| Model evaluation | — | |
| Dataset versioning | — | |
| Collaborative dashboards | — | |
| Hyperparameter sweeps | — | |
| Artifact management | — |
Langfuse
Pros
- ✓Open-source and self-hostable
- ✓Excellent tracing
- ✓Cost tracking per request
- ✓Good evaluation framework
Cons
- ✕Only useful for LLM apps
- ✕Requires integration effort
- ✕Analytics depth still improving
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 Langfuse and Weights & Biases are solid tools in the Analytics & Data space. Weights & Biases edges ahead with a stronger overall rating (4.9 vs 4.5) and is the better choice for ml teams that need to track, compare, and reproduce experiments. However, if you prioritize ai engineering teams that need observability into their llm application performance and costs, Langfuse is worth serious consideration. We recommend trying the free tier or trial of each before committing.