Braintrust
An end-to-end platform for evaluating, iterating on, and observing LLM apps, with a prompt playground.
Braintrust is a hosted platform that ties together LLM evaluation, a prompt playground, datasets, and production logging in one loop — write evals, iterate on prompts side by side, and watch real traffic, so the dev-and-monitor cycle lives in one place.
Braintrust is a commercial platform that unifies the LLM development loop: evaluation, a prompt playground, datasets, and production logging in one place. Rather than stitching an eval library to a separate observability tool, you build datasets, run and compare evals across prompt and model versions, and then monitor the same metrics on live traffic.
It is aimed at teams who want a polished, hosted workflow for iterating on LLM features — comparing prompt variants side by side, catching regressions in CI, and closing the loop from production logs back into evaluation datasets.
Highlights
- Evals + scoring — define scorers (including LLM-as-judge), run them over datasets, and compare experiments.
- Prompt playground — iterate on prompts and models interactively, then promote what works into evals.
- Datasets from production — turn real logged traffic into evaluation cases.
- Experiment comparison — diff results across versions to see exactly what a change moved.
- Observability — log and monitor production runs alongside the same metrics you evaluate on.
In an AI-assisted workflow
A typical loop: log production traffic, curate the interesting and failing cases into a dataset, iterate on the prompt in the playground, then run an experiment to confirm the change improves your scorers before shipping — with CI failing on regressions.
NOTE
Braintrust's value is the closed loop — eval, iterate, observe, and feed production back into eval — rather than any single feature in isolation.
Good to know
Braintrust is a hosted commercial product with a free tier and usage-based paid plans. If you prefer open-source, compare Langfuse and Arize Phoenix; for a code-first eval library you self-run, see DeepEval.
Related
- DeepEvalAn open-source evaluation framework for LLM apps — 'Pytest for LLMs' with ready-made metrics and CI integration.
- Best LLM & RAG Evaluation Tools in 2026: DeepEval vs RAGAS vs LangSmith vs Phoenix vs promptfooA decision guide to the LLM eval landscape — code-first frameworks vs. eval-and-observability platforms, open-source vs. hosted, and which fits your stack.
- LangSmithLangChain's platform for tracing, evaluating, and monitoring LLM apps — framework-agnostic.