Braintrust

What is Braintrust?

Braintrust is a platform for measuring whether an AI feature actually works. When software is deterministic you write a test that passes or fails; when a feature is powered by a language model the same input can produce a different answer every time, and “worse” is a matter of degree rather than a crash. An eval is the answer to that problem: you assemble a dataset of representative inputs with known-good expectations, run your prompt or agent against all of them, and have each output scored — by another model acting as a judge, by ordinary code, or by a human reviewer. Braintrust stores those datasets, runs the experiments, records the scores, and shows you the diff between two runs, so you can tell whether swapping a model or editing a prompt made things better or quietly broke a case that used to work.

The other half of the product is production observability. Braintrust ingests traces from live traffic — the prompt, the response, every tool call, retrieval step, token count, latency, and cost — and stores them in Brainstore, a database the company built specifically for the large nested documents that AI traces produce. Topics clusters those logs automatically into recurring patterns of task, issue, and sentiment so failure modes surface without anyone reading logs by hand, and any interesting trace can be promoted into an eval dataset in one click. Loop, the built-in agent, works on the evals themselves: it drafts test cases, suggests better scorers, and iterates on prompts. Instrumentation is framework-agnostic through SDKs for Python, TypeScript, Go, Ruby, Java, C#, and Rust, and an MCP server lets a coding agent query logs and update prompts from inside an IDE. The company raised an $80M Series B in February 2026 and ships continuously — the changelog and public SDK repositories were both updated in July 2026.

Who is it for?

Braintrust is developer infrastructure, not an end-user tool: the value only appears once you are shipping a real LLM or agent feature and have instrumented your code with an SDK. Billing is unusual for this category in that it charges nothing per seat — unlimited users on every plan, including the free one — but meters processed data, scores, and Topics tokens on top of the plan fee, so the invoice depends on trace volume rather than headcount. That makes the free Starter tier viable for a small production app and Pro reasonable for a team, but it also means anyone with high-volume traffic should estimate GB ingested and scores recorded before committing. Teams that want an open-source core they can self-host for free should look at Langfuse instead, since Braintrust’s platform is proprietary and its self-hosted option is Enterprise-only.

• AI application engineers who need to debug a multi-step agent or RAG pipeline by reading the actual trace rather than guessing from application logs.
• Teams putting quality gates in CI who want a prompt or model change blocked automatically when scores regress against a golden dataset.
• Product managers and domain experts doing structured human review on sampled production traces, using custom annotation views built for their specific task.
• Early-stage startups that qualify for the 6-12 month free Pro program and want production-grade eval infrastructure without paying for it during the seed stage.
• Regulated or privacy-sensitive organisations needing BYOC or a self-hosted data plane so traces containing customer data stay in their own cloud account.