Why it matters
- Used by Stripe, Zapier, and Vercel — teams with rigorous engineering standards who need reliable AI evaluation.
- Structured experiment management enables systematic A/B testing of prompts and models at scale.
- Dataset versioning ensures evaluation is reproducible — same inputs across experiments for fair comparison.
- Production monitoring closes the loop between offline evaluation and real-world performance.
Key capabilities
- Experiments: Run structured A/B tests comparing prompts, models, or pipeline configurations.
- Dataset management: Create, version, and share curated evaluation datasets with expected outputs.
- Automated scoring: LLM-as-a-judge, custom Python evaluators, and semantic similarity metrics.
- Human review: Queue outputs for human annotation and quality scoring.
- Production logging: Log every production LLM call for ongoing quality monitoring.
- Playground: Interactive prompt testing and comparison tool.
- SDK: Python and TypeScript SDKs for logging and experiment management.
- Regression detection: Alert when new prompt versions perform worse than baseline.
- Multi-model comparison: Run the same dataset against GPT-4, Claude, Gemini, and others simultaneously.
Technical notes
- SDK: Python (
pip install braintrust), TypeScript (npm install braintrust) - Framework: Framework-agnostic; works with any LLM or pipeline
- LLMs: OpenAI, Anthropic, Google, and custom models
- Hosting: Braintrust Cloud; self-hosted option (Enterprise)
- Data: SOC 2 compliant; Enterprise data isolation available
- Pricing: Free tier; Pro ~$150/mo; Enterprise custom
- Company: Braintrust; San Francisco; YC W22; backed by Elad Gil, Nat Friedman
Ideal for
- AI product teams who need rigorous, reproducible evaluation before shipping prompt or model changes.
- Organizations running continuous evaluation as part of their CI/CD pipeline for AI features.
- Teams comparing multiple model providers and needing clear performance data across configurations.
Not ideal for
- Small teams who just need basic logging — Helicone or PromptLayer are simpler for pure observability.
- LangChain-heavy pipelines where LangSmith's native integration is more seamless.
- Individual developers doing occasional prompt testing — the free tier may be sufficient but alternatives are simpler.