Braintrust

What it does

Braintrust combines offline experiments, production observability, prompt comparison, datasets, scorers, and online evaluation. It supports managed cloud and a hybrid self-hosted data plane for teams with stricter data requirements.

• Dataset experiments with immutable, comparable snapshots
• Code scorers, classifiers, LLM judges, prompt and workflow comparisons
• CI regression gates and asynchronous scoring of production traces or sessions

Where it fits

Teams that want a feedback loop from production traces and failures into datasets, regression experiments, CI gates, and continuous scoring.

Use it after defining the behavior that matters, collecting representative cases, and deciding how each case will be graded. A tool can execute and organize an eval, but it cannot decide which failures are costly for your users.

Limitations and cautions

• Self-hosting is hybrid: Braintrust retains the managed UI, authentication, updates, and control plane.
• Online evaluation often lacks reference answers and depends heavily on calibrated judge quality and sampling policy.

How to evaluate the evaluator

Before using any score as a release gate, create a small evaluator-validation set that humans have labeled. Compare false passes, false failures, disagreement by failure type, run-to-run variance, latency, and cost. Keep deterministic checks for verifiable facts, calibrate model judges against domain experts, and review raw traces when aggregate scores move.