Langfuse vs Ragas: Observability, Prompts & Evals Comparison
Compare Langfuse and Ragas side by side. Both are tools in the Observability, Prompts & Evals category.
The short answer
Choose Langfuse if fully open-source with MIT license and free for commercial use with no usage limits.
Choose Ragas if specialized focus on RAG evaluation with metrics specifically designed for retrieval systems.
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Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Open Source | Open Source |
| Best For | Teams who want open-source LLM observability they can self-host and customize | Developers building RAG applications who need specialized evaluation metrics |
| Website | langfuse.com | ragas.io |
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Pros & Cons: Langfuse vs Ragas
- +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
- −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
- +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
- −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
When to Choose Langfuse vs Ragas
- Self-hosted LLM monitoring
- Open-source tracing for AI applications
- Prompt versioning and management
- Evaluating RAG pipeline quality end-to-end
- Measuring retrieval precision and recall
- Testing faithfulness and answer relevance
About Langfuse
Langfuse is an open-source LLM engineering platform that provides comprehensive tools for traces, evaluations, prompt management, and metrics to debug and improve LLM applications. Founded in Berlin, Germany in 2022, Langfuse quickly became a leading platform in the LLM observability space. The platform features MIT-licensed open-source core with no usage limits for commercial use, making it highly accessible to teams of all sizes. Langfuse offers deep integration with popular frameworks including LangChain, OpenAI, LlamaIndex, and LiteLLM. The platform provides detailed tracing capabilities, evaluation tools, comprehensive prompt management, and rich metrics tracking. In January 2026, Langfuse was acquired by ClickHouse, Inc., marking a significant transatlantic venture exit and validating the platform's technology and market position. The acquisition demonstrates the value of Langfuse's approach to LLM observability, evaluations, and prompt management.
About Ragas
Ragas is an open-source framework specifically designed for evaluating Retrieval-Augmented Generation (RAG) applications. The platform provides automatic metrics that help teams understand the performance and robustness of their LLM applications, with the ability to synthetically generate high-quality and diverse evaluation data customized for specific requirements. Ragas offers component-wise and end-to-end evaluation of RAG systems through key metrics including context relevance, context recall, context precision, faithfulness, and answer relevancy. The framework is built by a small, focused team including Shahul (Applied AI researcher and Kaggle Grandmaster) and Jithin James (Chief maintainer, previously at BentoML), with strong backing from Y Combinator and Pioneer Fund. Ragas has gained significant industry recognition, being endorsed by major frameworks including LlamaIndex and LangChain, and directly recommended by OpenAI at DevDay. The platform integrates easily with popular frameworks and provides production monitoring capabilities to evaluate and ensure quality in production environments.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
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