← Compare hub
Weights & Biases vs Langfuse
Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.
| Dimension | Weights & Biases | Langfuse |
|---|---|---|
| Overall | 8.0 | 8.7 |
| Capability fit | 8.8 | 8.8 |
| Price / performance | 6.8 | 9.2 |
| Latency / reliability | 9.0 | 8.4 |
| Developer & agent DX | 8.2 | 8.8 |
| Privacy / data retention | 7.8 | 8.8 |
| Lock-in risk (higher = worse) | 5.5 | 2.5 |
| Maturity / track record | 9.4 | 8.2 |
| Pricing | Experiment tracking + W&B Weave/LLM tools; seat and usage based; free tiers for individuals. | Open-source + cloud; generous self-host; cloud from ~$29–59/mo tiers; EU-friendly. |
| Tagline | Use for training/fine-tune discipline. For pure agent request tracing, Langfuse/Helicone are leaner. | Depot default observability for most agent teams: open, affordable, good enough to serious. |
Weights & Biases
Use for training/fine-tune discipline. For pure agent request tracing, Langfuse/Helicone are leaner. Strengths: Gold standard ML experiment tracking; Expanding LLM/weave tooling; Enterprise adoption.
Strengths
- Gold standard ML experiment tracking
- Expanding LLM/weave tooling
- Enterprise adoption
Weaknesses
- Heavier than needed for simple API traces
- Cost at team scale
- Historically training-centric
Langfuse
Depot default observability for most agent teams: open, affordable, good enough to serious. Strengths: OSS + cloud flexibility; Excellent agent/trace UI for the price; EU posture.
Strengths
- OSS + cloud flexibility
- Excellent agent/trace UI for the price
- EU posture
Weaknesses
- Enterprise features chase LangSmith
- You operate it if self-hosting
- Ecosystem still growing
More pairs: anthropic-vs-openai · openai-vs-google-ai · groq-vs-fireworks · fireworks-vs-together · qdrant-vs-pinecone · pinecone-vs-supabase-pgvector · e2b-vs-modal · browserbase-vs-firecrawl