Langfuse vs Ragas
Overview
Langfuse is an open-source LLM observability platform that provides tracing, analytics, prompt management, and evaluation for AI applications. It captures detailed traces of LLM calls, supports custom scoring, and integrates with LangChain, LlamaIndex, Vercel AI SDK, and raw API calls. Langfuse can be self-hosted for data privacy or used as a managed cloud service. Its open-source model and generous free tier make it popular with startups and developers.
Ragas is an open-source evaluation framework specifically designed for RAG (Retrieval-Augmented Generation) pipelines. It provides metrics for context precision, context recall, faithfulness, and answer relevancy, helping teams measure and improve the quality of their RAG systems. Ragas has become the standard evaluation toolkit for teams building production RAG applications.
Key features
Core capabilities each platform advertises.
- Open-source LLM observability
- Detailed trace and span tracking
- Prompt management
- Evaluation scoring
- Self-hosted and cloud options
- RAG-specific evaluation framework
- Component-wise metrics for RAG
- Synthetic test data generation
- LLM-as-judge evaluators
- Open-source Python library
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Fully open-source with MIT license and free for commercial use with no usage limits
- Acquired by ClickHouse in January 2026, providing strong backing and future investment
- Active development with strong community support and comprehensive documentation
- Transparent pricing model based on units rather than complex metric calculations
- Deep integrations with major frameworks like LangChain, OpenAI, and LlamaIndex
Cons
- Recent acquisition by ClickHouse may lead to platform changes and strategic shifts
- Smaller team of 7 employees may impact support capacity
- Enterprise pricing at $3,450-$5,700/month can be expensive for mid-sized teams
Pros
- Specialized focus on RAG evaluation with metrics specifically designed for retrieval systems
- Open-source framework providing transparency and community-driven development
- Strong industry endorsement from OpenAI, LangChain, and LlamaIndex
- Y Combinator backing providing credibility and potential for growth
- Easy integration with popular frameworks like LangChain and LlamaIndex
Cons
- Very small team of 3 employees may limit support and feature development
- Limited to RAG evaluation, not a comprehensive observability solution
- Minimal funding ($500K) compared to competitors may impact long-term sustainability
- Lack of clear commercial pricing model for enterprise features
Langfuse or Ragas — which should you choose?
Choose Langfuse if you wantChoose if you want
- Self-hosted LLM monitoring
- Open-source tracing for AI applications
- Prompt versioning and management
- Cost tracking across providers
- Community-driven observability
Choose Ragas if you wantChoose if you want
- Evaluating RAG pipeline quality end-to-end
- Measuring retrieval precision and recall
- Testing faithfulness and answer relevance
- Generating synthetic evaluation datasets
- Benchmarking RAG across configurations
Compare Langfuse and Ragas on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.