Try Braintrust
Overview
Braintrust starts from a different question than the tracing platforms. Instead of 'what happened in production', it asks 'is this change actually better', and builds everything around answering that reliably. The core object is the experiment: a prompt or model variant, run against a dataset, scored by evaluators, and compared side by side against what you shipped last week.
That framing matters because LLM development has a specific failure mode — a change that fixes the example someone complained about and quietly breaks four others nobody looks at. Vibes-based iteration cannot catch that. Braintrust's comparison views make regressions visible before they reach users, which is the difference between engineering and hoping.
It also captures production traces and attaches evaluation scores to live traffic, so the same quality measures apply before and after release. The prompt playground closes the loop for the fast part of the work: try variants, see scores across the dataset immediately, promote what wins. It is a commercial platform rather than an open-source one, which is the main structural difference from Langfuse.
Key Features
Experiment-Centred Evaluation
Run prompt and model variants against datasets with scoring, and compare results side by side rather than judging by impression.
Regression Detection
Surfaces which specific cases got worse when a change improved the average — the failure mode that eyeballing outputs always misses.
Prompt Playground
Iterate on prompts with immediate scoring across a whole dataset instead of one hand-picked example.
Production Traces with Scores
The same evaluators run against live traffic, so pre-release and post-release quality are measured the same way.
Custom and Model-Graded Scorers
Write deterministic scorers or use model-graded evaluation, depending on whether the quality criterion is checkable or judgemental.
Human Review Workflows
Route outputs for human scoring where automated evaluation cannot capture the criterion.
Pros & Cons
Advantages
- The most rigorous evaluation workflow of the major platforms
- Catches regressions that average-score comparisons hide
- Prompt playground with dataset-wide scoring speeds iteration substantially
- Same evaluators run offline and on production traffic
- Good developer experience and clear documentation
Disadvantages
- Commercial platform, not open source or freely self-hostable
- Requires investment in building datasets and scorers to pay off
- Tracing is less of a focus than in Langfuse or LangSmith
- Costs scale with evaluation volume, which grows fast when it works
Pricing Plans
| Plan | Price | Key Features |
|---|---|---|
| Free | $0 | Limited monthly scores and traces for evaluation |
| Pro | From $249 / month | Team features, higher volumes, longer retention |
| Enterprise | Custom | Dedicated deployment options, SSO, compliance, support |
Best Use Cases
Braintrust Excels At:
- Teams shipping frequent prompt and model changes
- Preventing quality regressions before release
- Comparing models honestly on your own data rather than public benchmarks
- Organisations treating LLM quality as an engineering discipline
May Not Be Ideal For:
- Teams that have not yet built any evaluation datasets
- Organisations requiring open-source, self-hosted tooling
- Projects where the primary need is production debugging
How It Compares
Braintrust vs Langfuse
Langfuse answers 'what happened'; Braintrust answers 'is this better'. Langfuse is open source and self-hostable; Braintrust is commercial with deeper evaluation tooling. Mature teams often want both, and pick based on which question hurts more today.
Braintrust vs manual spreadsheet evaluation
Most teams start with a spreadsheet of test prompts and manual review. That works up to about thirty cases, then collapses. Braintrust is what replaces it, and the migration is easier before the spreadsheet has become load-bearing.
Final Verdict
Our Recommendation
Braintrust is for teams that have accepted LLM quality is an engineering problem rather than a matter of taste. The experiment-centred workflow and regression detection catch the specific failure that ruins LLM products — a change that improves the average while quietly breaking cases nobody is watching. It demands real investment in datasets and scorers before it pays off, and it is commercial rather than open source. If you ship prompt changes weekly and cannot currently prove they help, this is the tool that fixes that.