DeepEval vs Ragas
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as Ragas vs DeepEval — same comparison, one verdict.
DeepEval
Evals as pytest tests, with the research metrics already implemented.
DeepEval brings LLM evaluation into pytest, so an eval is a test function and your existing test runner, CI integration and reporting all work unchanged. It ships implementations of the metrics people actually cite — answer relevancy, faithfulness, contextual precision and recall, hallucination, bias, toxicity — including several LLM-as-judge metrics done carefully. Apache-2.0, from Confident AI, who sell an optional hosted platform.
Ragas
Purpose-built RAG evaluation — measures retrieval and generation separately.
Ragas evaluates RAG systems specifically, and its central insight is that retrieval failure and generation failure need separate measurement. Context precision and recall tell you whether the retriever found the right material; faithfulness and answer relevancy tell you whether the model used it honestly. Without that separation you know the answer was wrong but not which half to fix. Apache-2.0, and it composes with LlamaIndex and Haystack directly.
Side by side
| DeepEval | Ragas | |
|---|---|---|
| Sovereignty Score | 92 | 92 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free and Apache-2.0; optional paid cloud dashboard. | Free, Apache-2.0. |
It's close — DeepEval and Ragas score evenly. Choose on the trade-offs below.
DeepEval
Strengths
- +Pytest-native — your existing CI and reporting just work
- +Large library of implemented, research-backed metrics
- +Synthetic test-case generation for cold-start coverage
- +Apache-2.0 with no seat cost
Trade-offs
- −LLM-as-judge metrics cost tokens on every run
- −Assumes a Python codebase
- −Best dashboard experience is the paid hosted one
Ragas
Strengths
- +Separates retrieval quality from generation quality
- +Can generate a synthetic test set from your own documents
- +Integrates directly with LlamaIndex and Haystack
- +Apache-2.0
Trade-offs
- −RAG-specific — not a general evaluation framework
- −Judge-model calls make large suites cost real money
- −Metric scores need calibration before you trust them
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.