Comparison

W&B (Weights & Biases) vs Langfuse: Open Source Agent Evals & Observability

W&B (Weights & Biases) and Langfuse: Open Source Agent Evals & Observability both land on Depends.

W&B (Weights & Biases)

Depends

Confidence: Medium

Buy if you run serious ML training or LLM app workloads . it's the category default with deep tracking and eval features.

Depends

Confidence: Low

Buy if you ship LLM apps to production and need tracing, prompt management, and evals in one place; skip if you don't build AI features.

W&B (Weights & Biases) versus Langfuse: Open Source Agent Evals & Observability
Compare	W&B (Weights & Biases)	Langfuse: Open Source Agent Evals & Observability
Verdict	Depends	Depends
Best for	ML teams tracking training runs	Teams shipping LLM apps to production
Who it's not for	Casual users logging a handful of runs	Teams not building LLM-powered products
Privacy	One high-severity 2024 CVE affected the self-hosted Weave server; the managed cloud service was not implicated.^⁸	No known public vulnerabilities found in the sources reviewed.¹²
Support quality	No direct evidence in sources reviewed	No support-quality evidence found
Public sentiment	Users love the tracking UX but increasingly gripe about rigid pricing, run limits, and enterprise growing pains.^⁵	No independent user reviews surfaced in reviewed sources; all claims are vendor-published adoption and feature statements.¹¹
Biggest gotcha	Tracked-run caps (~500) on lower tiers push teams into paid plans quickly^⁵	Free tier caps at 50k observations/month; high-volume agents outgrow it fast, costs scale with volume¹¹