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Ragas vs Braintrust
Agent-native side-by-side. Scores are curated estimates on a 0–10 scale. Lock-in: higher = worse. Prefer JTBD picks via /api/decide when choosing for a job.
| Dimension | Ragas | Braintrust |
|---|---|---|
| Overall | 8.3 | 8.2 |
| Capability fit | 8.0 | 9.2 |
| Price / performance | 9.5 | 7.5 |
| Latency / reliability | 7.5 | 8.6 |
| Developer & agent DX | 8.2 | 8.8 |
| Privacy / data retention | 9.2 | 8.0 |
| Lock-in risk (higher = worse) | 2.0 | 5.0 |
| Maturity / track record | 7.8 | 8.2 |
| Pricing | As of 2026-09-14: Free open-source RAG evaluation library; cost is judge-LLM tokens only (~$0.50–$2 per 100 queries with cheap judges). No hosted SaaS required. Deep LangChain/LlamaIndex integration. | Eval-first platform; data-based pricing with unlimited seats on many plans; strong CI eval story. |
| Tagline | Default RAG metric library. Pair with Promptfoo/Braintrust for broader agent evals. | Depot pick when evals are the product quality gate. Pair with Helicone/Langfuse for cheap request logs if… |
Ragas
Default RAG metric library. Pair with Promptfoo/Braintrust for broader agent evals. Strengths: RAG-specific metrics; Free/OSS; Python-native.
Strengths
- RAG-specific metrics
- Free/OSS
- Python-native
Weaknesses
- Not a full agent-trace platform
- You operate harness
- Less red-team than Promptfoo
Braintrust
Depot pick when evals are the product quality gate. Pair with Helicone/Langfuse for cheap request logs if needed. Strengths: Best-in-class eval workflows; Great for prompt/agent regression gates; Generous seat model.
Strengths
- Best-in-class eval workflows
- Great for prompt/agent regression gates
- Generous seat model
Weaknesses
- Broader tracing competitors exist
- Price entry higher than hobby tools
- Learning curve for eval design
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