Braintrust
Trace, score, and compare LLM outputs to catch quality regressions before they reach production. Closed an $80M Series B at ~$800M valuation in early 2026.
The verdict on Braintrust: Teams shipping LLM applications who need systematic proof that a prompt or model change actually improved quality Braintrust is priced and built for teams that have moved past 'does this prompt work' and need systematic proof that a change to a model, prompt, or retrieval step actually improved quality before it ships. Pricing: Free (1GB data) / $249/mo Pro / Enterprise custom.
Best For
Teams shipping LLM applications who need systematic proof that a prompt or model change actually improved quality
Standout Feature
Integrates evaluation directly into CI/CD so evals run automatically on every change, not as a manual afterthought
TL;DR
Best-in-class for rigorous LLM evaluation workflows, real overkill for a simple single-prompt application.
Alternatives
Fit Score: use-case reference
How this is scoredBraintrust scored 1 to 5 on five dimensions for the named use cases below. Part of our catalog-wide use-case reference, not a product-quality review rating.
Model Comparison
3.8/5- Capability
- 4/5
- Reliability
- 4/5
- Value
- 4/5
- Ease
- 3/5
- Compatibility
- 4/5
Dataset management, custom scoring, and experiment comparison support repeatable comparisons before a model change ships. A usable free quota makes initial comparisons accessible, but the dataset and scoring criteria still take work. Treat the result as evidence for the cases you evaluate, not a guarantee across all production inputs.
CI/CD for AI
3.8/5- Capability
- 5/5
- Reliability
- 4/5
- Value
- 4/5
- Ease
- 2/5
- Compatibility
- 4/5
Automatic evaluations on each change turn quality checks into part of the release workflow instead of a manual afterthought. Dataset management, custom scoring, and production monitoring support that loop, with a usable free tier to start. Building meaningful datasets and the CI pipeline is substantial setup work, especially for a small single-prompt project.
Editorial judgment grounded in this tool's own published pricing, features, and our existing pros and cons, not a certification or a measured benchmark. Product quality is assessed separately under our review methodology.
Overview
Not yet reviewedProduct quality is assessed from original evidence. How reviews work.
Braintrust is an AI evaluation and experimentation platform for teams shipping LLM applications who need systematic evidence that changes to models, prompts, or retrieval configurations actually improve quality before deploying them to users. The core workflow: define evaluation datasets from production logs, logged edge cases, or manually curated examples; write or configure scoring functions that assess output quality (correctness, faithfulness, relevance, custom criteria); run experiments comparing different configurations against the same dataset; and compare results in a structured dashboard that shows exactly which cases improved, which degraded, and by how much. This experiment-and-compare approach brings the rigor of software testing to AI system development, where qualitative impressions of 'it seems better' frequently lead to regressions that only appear in production. CI/CD integration runs evaluations automatically on every pull request, catching quality regressions before they merge, the same safety net that unit tests provide for functional correctness, applied to LLM output quality.
The logging layer captures production inference with linked evaluation scores, creating a feedback loop from production behavior to evaluation dataset improvement. Human annotation tools enable quality reviewers to label outputs inline without leaving the platform. Used by engineering teams at companies including Stripe, Vercel, and Anthropic for systematic LLM quality assurance. Braintrust's pricing has a genuinely free Starter tier (1GB processed data, 10k scores, unlimited users/projects/datasets/experiments); the Pro tier for AI-native teams starts at $249/month, and Enterprise is custom-priced.
The source of the legacy catalog rating and review count has not been substantiated. Neither is presented here as a verified review rating or included in review structured data.
Workflow Fit Scores are separate, use-case-specific editorial judgments, not user-review averages. Read the Fit Score methodology.
- • Best-in-class for systematic LLM evaluation workflows
- • Integrates into CI/CD so evals run on every change
- • Strong support for complex multi-step agent evaluation
- • Overkill for simple single-prompt applications
- • Takes time to set up meaningful eval datasets
Key Features
- Eval dataset management
- Custom scoring functions
- Experiment comparison
- CI/CD integration
- Prompt playground
- Production monitoring
Trust & Data
Verified 2026-09
- Training on your data
- NoBraintrust's own blog states it does not train on customer data, and prompts, traces, and customer conversations are never used to improve models, reinforced contractually in its self-serve agreement, which commits that Braintrust will not use customer data to train, develop, or improve AI or machine learning models. An optional AI-analysis feature sends user-selected evaluation artifacts to third-party subprocessor-hosted models for summarization, which is subprocessor data processing rather than training, but is a real flow of customer data to external LLMs worth disclosing. The general privacy notice document itself lives on a separate legal-entity domain and explicitly excludes information customers upload or process via the services, so the no-training commitment actually comes from the product docs, blog, and agreement, not that privacy notice.
- Compliance
- SOC 2 Type II
- Retention
- Retention is customer-configurable, with the vendor's own examples being roughly 7 days for development, 90 days for production, and 180 days for experiments; self-hosted deployments soft-delete data with a 24-hour grace period before permanent removal.
- Export
- Full export
Sources: braintrust.dev/privacy policy, braintrust.dev/security data control
As published by the vendor. Verify independently before purchase decisions.
Our Take
The CI/CD integration is its sharpest edge: evals run on every change automatically, not as a manual step someone skips under deadline. Braintrust's Starter tier is free with a real, usable quota, so cost alone doesn't rule out a single-prompt side project; what does is the setup investment, building a meaningful eval dataset and CI pipeline is work most tiny projects don't need yet. But for a production LLM application where a quality regression costs real users or revenue, that investment in a reproducible eval dataset and pipeline pays back fast.
Workflows Using This Tool
Step-by-step playbooks whose entries name Braintrust. The step shown is where the workflow uses it.
- BeginnerEstimate: 1 hour. Planning estimate from the workflow entry, not measured time saved.
Evaluate and Benchmark AI Models Before You Commit
Step 3 of 4: Score Outputs Against Your Rubric
- AdvancedOne-time setup: 1 day, one-time setup. Planning estimate from the workflow entry, not measured time saved.
Deploy a Custom Fine-Tuned Model Like an Enterprise ML Team
Step 3 of 4: Test Against Your Baseline Model
- IntermediateEstimate: 4-6 hours. Planning estimate from the workflow entry, not measured time saved.
Migrate From a Closed API Model to an Open-Weight Model Without Breaking Your App
Step 3 of 4: Score the Replacement Against the Baseline
- AdvancedOne-time setup: 3-5 hours, one-time setup. Planning estimate from the workflow entry, not measured time saved.
Evaluate an AI Agent Before Trusting It
Step 3 of 5: Score the Agent Against the Full Set in Braintrust