Braintrust
4.4
Braintrust is a platform for evaluating AI output and improving it systematically: production traces and evaluation datasets live in the same place, so a change can be scored against real cases rather than judged by impression.
- Commercial use OK
- Free tier
- Enterprise contract
- Pricing
- $249/mo
- Free tier
- Free tier
- Commercial use
- Permitted
- Japanese UI
- Not confirmed
- Enterprise contract
- Available
What it is good for
- Teams shipping LLM features frequently enough that regressions matter
- Turning production failures into regression cases
- Comparing prompts or models on evidence rather than vibes
- Organisations that need quality to be auditable
Strengths and trade-offs
Strengths
- Production traces and evaluation sets in one system
- Makes prompt and model changes measurable
- Supports both automated and human scoring
- Enterprise contracting available
Trade-offs
- Expensive relative to tracing-only tools
- Value depends entirely on the quality of your evaluation set
- Overkill for a single LLM feature
Editors' review
Makes 'is this prompt better?' a question with an answer
Every team shipping LLM features eventually hits the same wall: a prompt change improves the cases you looked at and quietly breaks others. Without a scored evaluation set, you discover that from users.
Braintrust's design point is that the production traces and the evaluation set are the same material. A failure reported today becomes a regression case tomorrow, which is the only mechanism that compounds.
It is not cheap and it is not automatic. The evaluation set is yours to build, and a thin one gives confident but meaningless scores. For one small feature, tracing alone is enough.
Verdict
Worth it once LLM output quality is something you have to defend. Build the evaluation set first.
Compiled from official documentation and public sources, with our own editorial judgement. We do not publish figures we cannot source.