Promptfoo vs DeepEval
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as DeepEval vs Promptfoo — same comparison, one verdict.
Promptfoo
TOP PICKPrompt regressions fail the build. Declarative evals that live in CI.
Promptfoo defines evaluations as YAML: your prompts, your test cases, your assertions, versioned in the repository next to the code they test. It runs from the CLI or in CI, compares outputs across models and prompt variants side by side, and fails the build when a change regresses. It also includes red-teaming for prompt injection and jailbreak testing. MIT licensed with no seat limits, which matters because everyone who edits a prompt should be running it.
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.
Side by side
| Promptfoo | DeepEval | |
|---|---|---|
| Sovereignty Score | 94 | 92 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free and MIT, unlimited seats. An enterprise tier exists for larger organisations. | Free and Apache-2.0; optional paid cloud dashboard. |
Promptfoo is Macrostack's recommended Braintrust alternative, so it's our pick here.
Promptfoo
Strengths
- +Evals live in your repo and run in CI — a regression blocks the merge
- +Side-by-side model and prompt comparison out of the box
- +Includes red-teaming for injection and jailbreak testing
- +MIT, no seat limits, nothing leaves your infrastructure by default
Trade-offs
- −YAML configuration gets long on large test suites
- −Reporting UI is lighter than a hosted platform's
- −Trace history is yours to store and manage
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
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.