Side-by-side

Weights & Biases vs Langfuse

Weights & BiasesLangfuse

Bottom line: Weights & Biases for mL and AI teams needing mature experiment tracking; Langfuse for teams wanting open-source LLM observability.

	Weights & Biases The AI developer platform for experiment tracking and LLMOps. Visit	Langfuse Open-source LLM observability and evaluation Visit
Votes	0	0
Pricing	Freemium	Freemium
Category	Coding	Coding
Tags	mlopsllmopsexperiment-trackingllm-observabilityevaluation	llm-observabilityopen-sourcetracingevaluationprompt-management
Best for	ML and AI teams needing mature experiment tracking Teams shipping LLM apps that need tracing and evaluation Research groups and enterprises with existing W&B workflows	Teams wanting open-source LLM observability Data-sensitive teams needing self-hosting Prompt and evaluation workflows
Pros	Industry-standard, mature experiment tracking with a large user base Weave gives low-friction LLM tracing with one decorator plus rich evals Covers both classic ML and modern LLMOps in one ecosystem Pre-built safety and quality scorers and guardrails out of the box Genuinely useful free tier for individuals and a strong academic program	Open source with nearly all features MIT-licensed Self-host the full product free, no seat or usage caps Framework-agnostic (works with or without LangChain) Strong tracing, prompt management, and evaluation Managed cloud with a free tier available
Cons	Non-commercial restriction on the free tier limits business use New 2026 usage-metered billing makes costs harder to predict A team-size threshold pushes growing teams into custom Enterprise quickly Enterprise seat pricing is high per third-party estimates Only the Weave SDK is open source; the backend is proprietary and SaaS-first	Self-hosting still requires running infrastructure Enterprise compliance features are commercial Cloud Pro tier jumps significantly in price Focused on observability, not app building Analytics depth may need tuning for large scale

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