DeepEval vs OpenAI Evals
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as OpenAI Evals 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.
OpenAI Evals
The original benchmark harness. Simple, standard, still useful.
OpenAI Evals is the framework and registry that popularised systematic LLM evaluation. It provides a standard harness for running benchmark-style evaluations and a public registry of existing ones, so comparing against a known suite is a command rather than a project. It is less actively developed than the others here and less suited to application-level regression testing, but for running or extending a standard benchmark it remains the shortest path. MIT licensed.
Side by side
| DeepEval | OpenAI Evals | |
|---|---|---|
| Sovereignty Score | 92 | 85 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | Apache-2.0 | MIT |
| Pricing | Free and Apache-2.0; optional paid cloud dashboard. | Free, MIT. You pay for whatever model API the evals call. |
DeepEval edges it on the Sovereignty Score, but the right pick depends 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
OpenAI Evals
Strengths
- +Large registry of existing benchmark evaluations
- +Simple, well-understood format that many teams already know
- +Straightforward to extend with your own cases
- +MIT licensed
Trade-offs
- −Development pace has slowed relative to the alternatives
- −Oriented to benchmarks, not application regression testing
- −Weaker RAG and agent evaluation support than the others
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.