Braintrust vs Ragas (2026)
Overview
Braintrust is an LLM evaluation and observability platform. Compare its evals, tracing, pricing, pros and cons, and alternatives.
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.
- Experiments comparing prompts and models
- Code-based and LLM-as-judge scorers
- Datasets built from production traces
- Production logging and tracing
- 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
- Unlimited users on every plan, including free
- Production traces, datasets, and experiments connect in one eval workflow
- Framework-agnostic SDKs across multiple languages
- Loop agent drafts scorers and datasets from your own data
Cons
- Processed data and scores bill separately, and both grow with eval volume
- 14-day retention on Starter and 30 days on Pro
- Gateway is in public preview with no SLA
- Self-hosting and custom data export are Enterprise only
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
Braintrust or Ragas — which should you choose?
Choose Braintrust if you wantChoose if you want
- Iterating on prompts with production data
- Running evaluations across model versions
- Building golden datasets from production traffic
- Human review of LLM outputs
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 Braintrust and Ragas on your own traffic
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