Promptfoo vs OpenAI Evals
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as OpenAI Evals 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.
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
| Promptfoo | OpenAI Evals | |
|---|---|---|
| Sovereignty Score | 94 | 85 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | MIT |
| Pricing | Free and MIT, unlimited seats. An enterprise tier exists for larger organisations. | Free, MIT. You pay for whatever model API the evals call. |
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
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.