Best LLM Evaluation Tool 2026: Braintrust vs Weave vs Promptfoo

Choose on where the eval runs and who reads the results, not on the scoring feature list — all three cover the same scoring primitives. Read down and stop at the first line that describes you.

An LLM evaluation tool runs a dataset of test cases through your prompt or agent and scores each output, so you can tell whether a change made things better or worse before it reaches users. A dataset is usually a set of input and expected-output pairs, drawn from production logs, written by hand as edge cases, or generated. Scorers range from deterministic checks like string match and JSON schema validation, through LLM-as-judge grading where a second model scores against a rubric, to custom functions you write yourself.

That is a different job from watching what the system does once it is live. Evaluation answers "is this change safe to ship"; production monitoring answers "what is it doing now". This page is about the first question. For the prompt-level view of the same problem, see how to evaluate prompt quality.

All three do LLM-as-judge, custom scorers and dataset regression testing, so the comparison is about hosting model, cost structure and eligibility. Pricing was read from each vendor's own page on 28 August 2026, and repository figures from the GitHub API the same day.

Braintrust is the one to pick when people who do not write code need to look at eval results. Its value is concentrated in the hosted UI, dataset versioning and human-review workflow, which is exactly the part you would otherwise have to build.

Weave is the obvious answer if your team already tracks experiments in Weights & Biases, and a harder sell if it does not. The integration is the argument: evals land next to the runs you are already looking at.

Promptfoo is the one that costs nothing at any scale, because you run it yourself. It is a CLI and library rather than a platform, so evals live as configuration next to your code and run wherever your tests already run.

OpenAI announced it was acquiring Promptfoo on 9 March 2026, and committed publicly to keeping it open source. OpenAI's own statement reads: "Promptfoo will remain open source under the current license, and we will continue to service and support current customers." The acquisition was reported at the time by TechCrunch, CNBC and Forbes, and confirmed on both OpenAI's and Promptfoo's own sites.

The evidence on the ground supports the commitment so far. Nearly six months later the repository is still under the `promptfoo` organisation, still MIT-licensed, and was pushed the day this page was checked. That is what you would want to see.

The reason to think about it anyway is governance rather than licence. An evaluation tool is the thing you use to judge models, and it is now owned by a company that makes models. The MIT licence means you can fork if the project direction ever stops serving you, which is a real protection that Braintrust and Weave do not offer. But if your evaluation strategy depends on neutrality between model vendors, that is worth a deliberate decision rather than an assumption.

Cost scales with data volume and judge-model spend, not seats — which is why unlimited users on the free tiers is more useful than it first sounds. The table below prices a mid-size workload of roughly 50,000 eval scores a month.

Promptfoo is the only one of the three where self-hosting is the default rather than an enterprise upgrade. Because it is a CLI and library, your test cases and model outputs never leave your environment unless you deliberately choose a hosted tier. For a team under data-residency rules, that removes the question entirely rather than answering it.

Braintrust offers on-prem or hosted deployment, but on Enterprise only, which means custom pricing and a sales conversation. It is a real deployment option rather than a marketing line, but you cannot evaluate it self-serve.

Weave's Enterprise tier offers a single-tenant option with customer-managed encryption keys, HIPAA compliance and secure private connectivity. Read that carefully: single-tenant with customer-managed keys is a dedicated instance, which is not the same thing as running inside your own perimeter. If your requirement is strictly on-premise, confirm the specifics with Weights & Biases rather than assuming the single-tenant option satisfies it.

Team shape decides this more than feature lists. Five profiles cover most readers.

Eval datasets are usually built from production traffic, which makes them some of the most sensitive data in an LLM stack. That turns the hosted-versus-local choice into a compliance question in three major markets.

If your test set is a dozen prompts and one engineer runs it, skip all three and write a scored script in CI. You get the same regression signal without an account, a meter or a retention window, and you can graduate to a platform later without having wasted anything, since the dataset is the asset and it moves with you.

The threshold worth waiting for is when managing the dataset becomes the work: when you want to pull failing cases from production into the test set routinely, when someone who does not write code needs to grade outputs, or when you need to compare this month's results against last quarter's. Those are dataset-management problems, and that is what you are actually buying. Scoring itself is the easy part and always was.