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Migration guide · LLM Evaluation & Testing

The 5 best Braintrust alternatives

Braintrust is an evaluation and observability platform for LLM applications. You define test cases and scoring functions, run them against prompt or model changes, and get a hosted dashboard showing what improved and what regressed — plus logging of production traffic so you can pull real failures into the test set. It exists because the alternative is shipping prompt changes on vibes, which is what most teams are still doing.

94
Bottom line

Promptfoo is our pick for most teams — declarative test cases in YAML, runs in CI on every pull request, MIT licensed, and it treats a prompt regression exactly like a failing unit test, which is the correct mental model. DeepEval is the stronger choice if you want a pytest-native workflow with research-backed metrics already implemented. Ragas is purpose-built for RAG and measures the things RAG actually fails at, Inspect AI is the most rigorous of the group and comes from the UK AI Safety Institute, and OpenAI Evals remains the simplest way to run a standard benchmark suite.

Jump to the full comparison →

The cost

Free tier for individuals with limited monthly spans. Paid plans are per-seat — commonly around $249/month for a small team on the pro tier — plus usage-based charges once trace volume grows. Enterprise pricing is negotiated. The per-seat model is what makes it expensive for teams where many engineers touch prompts occasionally.

Why people consider an alternative

Per-seat pricing sits badly against how evaluation actually gets used. Everyone who edits a prompt needs to see whether they broke something, so the seat count is your engineering team, not a subset of it — and the value per marginal seat drops fast. The second reason is architectural: evaluation belongs in CI, next to your unit tests, running on every pull request. A hosted platform with a dashboard encourages evaluation as a thing you visit rather than a gate that blocks a bad merge. And your prompts, test cases and production traces are among the most commercially sensitive artefacts you have — for some teams sending them to a third party is simply not available.

When Braintrust is still the right call

Stay if the dashboard is doing real work for a team that would not otherwise look at evaluation at all. A hosted UI that a product manager can read is worth more than a CLI nobody outside engineering runs, and Braintrust's trace-to-test-case workflow — pulling a real production failure straight into your regression set — is genuinely well built. Stay while your seat count is small. The tools below assume someone will own evaluation as engineering infrastructure rather than as a product surface.

The verdict — at a glance

AlternativeLicenseSelf-hostPricingSovereignty
Promptfoo★MITYesFree and MIT, unlimited seats. An enterprise tier exists for larger organisations.94
Inspect AIMITYesFree, MIT, publicly funded.93
DeepEvalApache-2.0YesFree and Apache-2.0; optional paid cloud dashboard.92
RagasApache-2.0YesFree, Apache-2.0.92
OpenAI EvalsMITYesFree, MIT. You pay for whatever model API the evals call.85
94
Macrostack's top pick

Promptfoo

Prompt regressions fail the build. Declarative evals that live in CI.

Which one fits your situation?

There is no single best answer — it depends on what you are actually trying to do.

Just tell me the best Braintrust alternative

Promptfoo94

Our top pick — the highest Sovereignty Score at 94, and the one we would choose ourselves.

Promptfoo vs Inspect AI →

I have no budget — is there a genuinely free one?

Inspect AI93

Open source under MIT. Free, MIT, publicly funded.

Inspect AI vs Promptfoo →

I want to self-host it and own the data

DeepEval92

Self-hostable, so your data stays on your own server. Free and Apache-2.0; optional paid cloud dashboard.

DeepEval vs Promptfoo →

Every alternative, compared

#1★ TOP PICK

Promptfoo

Prompt regressions fail the build. Declarative evals that live in CI.

94
OPEN SOURCEMITSELF-HOSTLOCAL-FIRST

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.

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
Free and MIT, unlimited seats. An enterprise tier exists for larger organisations.
WebsiteSourceDocs
#2

Inspect AI

The rigorous one, from the UK AI Safety Institute.

93
OPEN SOURCEMITSELF-HOSTLOCAL-FIRST

Inspect is the evaluation framework built by the UK AI Safety Institute for evaluating frontier models, released MIT. It is the most methodologically serious option here: first-class support for multi-turn agent evaluations, tool use, sandboxed execution and human grading, with a design that takes statistical validity seriously rather than producing a number that feels reassuring. If your evaluations need to withstand scrutiny — regulatory, academic or internal — this is the one.

Strengths

  • +Built for evaluations that have to survive real scrutiny
  • +Strong agent, tool-use and sandboxed-execution support
  • +Excellent log viewer for inspecting individual samples
  • +MIT, from a public institute with no commercial upsell

Trade-offs

  • −Aimed at model evaluation more than application regression testing
  • −Steeper learning curve than Promptfoo for simple cases
  • −Less oriented toward CI gating out of the box
Free, MIT, publicly funded.
WebsiteSourceDocs
#3

DeepEval

Evals as pytest tests, with the research metrics already implemented.

92
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST

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.

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
Free and Apache-2.0; optional paid cloud dashboard.
WebsiteSourceDocs
#4

Ragas

Purpose-built RAG evaluation — measures retrieval and generation separately.

92
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST

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.

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
Free, Apache-2.0.
WebsiteSourceDocs
#5

OpenAI Evals

The original benchmark harness. Simple, standard, still useful.

85
OPEN SOURCEMITSELF-HOSTLOCAL-FIRST

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.

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
Free, MIT. You pay for whatever model API the evals call.
WebsiteSourceDocs

Questions people ask

▸ What is the best open-source alternative to Braintrust?

Promptfoo for most teams — test cases are declarative YAML that live in your repository, run in CI, and fail the build on a regression. That is the right shape for evaluation, and it is MIT with no seat limits, which matters because everyone who edits a prompt needs to run it. For a pytest-native workflow with research metrics already implemented, DeepEval is the better fit.

▸ Why does per-seat pricing hurt so much for evaluation tools?

Because the correct seat count is your whole engineering team. Anyone who changes a prompt, a model version or a retrieval parameter needs to know whether they broke something — that is not a specialist function. Per-seat pricing pushes teams to buy fewer seats than they need, which quietly reintroduces the problem the tool was bought to solve.

▸ Do I need evaluation if I have manual QA?

Manual QA catches the failure you thought to check. The failures that matter in LLM applications are the ones a prompt tweak introduced three tests away from where you were looking, and nobody re-reads two hundred outputs by hand on every merge. Even thirty automated cases running in CI will catch regressions manual review reliably misses.

▸ How much do these evaluations cost to run?

The framework is free; the model calls are not. LLM-as-judge metrics — faithfulness, answer relevancy — make an extra model call per test case, so a large suite on every commit adds up. The usual pattern is a small fast suite on every pull request and the full judged suite nightly or before release. Running the judge against a cheap local model keeps that cost near zero.

▸ Is Braintrust bad?

No. Its production-trace-to-test-case workflow is genuinely good, and a dashboard that non-engineers will actually open has value a CLI does not. It is a reasonable buy for a small team that wants evaluation without owning it. The concerns are per-seat pricing against a team-wide need, the pull toward evaluation-as-dashboard rather than evaluation-as-CI-gate, and sending prompts and traces to a third party.

▸ Can I evaluate without sending data anywhere?

Yes. Promptfoo, DeepEval, Ragas and Inspect all run entirely on your own infrastructure, and if you point the judge model at a locally served one, no prompt, test case or output leaves your network at any point. For teams whose prompts encode commercial logic, that is often the deciding factor rather than the price.

Compare them head-to-head

Promptfoo vs Inspect AI→Promptfoo vs DeepEval→Promptfoo vs Ragas→Promptfoo vs OpenAI Evals→Inspect AI vs DeepEval→Inspect AI vs Ragas→Inspect AI vs OpenAI Evals→DeepEval vs Ragas→DeepEval vs OpenAI Evals→Ragas vs OpenAI Evals→

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Entry last verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.

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