Braintrust
LLM evaluation and prompt management for production
Overview
Braintrust is an LLM evaluation and prompt-management platform — write evals as code, run them on every model or prompt change, manage prompts as versioned artifacts. Used by Stripe, Notion, Airtable, Coda and others to ship LLM features without regressing quality. Free tier; paid plans for teams and enterprises.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 19 May 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
What you can produce with Braintrust
- Write an eval suite as code that scores your LLM outputs against a versioned dataset, and run it automatically on every prompt or model change.
- Compare two prompt versions or two models side by side in an experiment view and see exactly which test cases regressed before anything ships.
- Capture production LLM traces, inspect a bad response step by step, and promote it into your eval dataset as a permanent regression test.
- Manage prompts as versioned artifacts so a prompt change is reviewed and testable rather than a silent edit in application code.
- Score outputs with built-in autoevals or custom LLM-as-judge and code-based scorers tailored to your product's quality bar.
- Try models from multiple providers against the same dataset in the playground to pick the best cost-quality trade-off for a feature.
- Start free on the Starter tier and run a real eval workflow end to end before committing to the $249/month Pro plan.
ASEAN Perspective
Braintrust in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Braintrust is an evaluation, observability and prompt-iteration platform for teams building LLM-powered products, offering systematic evals, logging, datasets, scoring and a playground that bring engineering rigour to otherwise ad-hoc prompt work. It is well regarded for developer experience and is increasingly part of serious LLM-app stacks.
It is squarely a technical product: value depends on having LLM features in production and the discipline to build eval suites, and meaningful use sits behind paid tiers. It suits ML and product-engineering teams shipping AI features who need to measure quality; non-technical users have no use for it. Globally available in English, so ASEAN engineering teams can adopt it readily.
What people say
Braintrust has consolidated its position as the reference platform for eval-first LLM development, and the market has noticed: in February 2026 it raised an $80M Series B led by ICONIQ at a reported $800M valuation. Its publicly named customers — Notion, Stripe, Vercel, Dropbox, Replit, Coursera — read like a roll call of teams shipping serious LLM features, and 2026 reviews are mostly positive, with one May 2026 assessment scoring it 86 overall and highlighting developer experience.
What practitioners consistently praise is the core loop: write evals as code against versioned datasets, run experiments on every prompt or model change, and trace production behaviour back into new test cases. Multiple independent reviews call the trace-to-dataset-to-experiment workflow the best-integrated in the category, and the free Starter tier is repeatedly described as genuinely usable rather than a demo. Prompt management as versioned artifacts and the playground for side-by-side model comparison round out the strengths.
The criticisms cluster around pricing mechanics and scope. Pro jumps to $249/month, Enterprise pricing sits behind a sales call with annual invoicing only, and overage charges on processed data and scores mean costs scale with trace volume. Data retention is the most-cited squeeze: 14 days on Starter and 30 on Pro is fine for this sprint's experiment but useless for asking when a regression actually started. Reviewers also note it is weaker at automatic issue discovery from production — failure-pattern clustering and auto-generated evals are not native — and that teams who only want a trace viewer are overpaying for eval machinery they will never use.
Braintrust genuinely fits engineering teams who treat evaluation as a first-class workflow and will actually write scorers and run experiments before shipping. Solo developers on side projects, or teams that just need lightweight LLM observability, will find cheaper tools that cover their real needs.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Braintrust's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Braintrust unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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