Braintrust
LLM evaluation and observability platform for testing, scoring, and improving AI application quality in production.
What it does
Braintrust is an LLM evaluation platform - providing the infrastructure for testing AI application quality, comparing model and prompt versions, and monitoring LLM performance in production. AI capabilities include automated LLM evaluation that scores model outputs against custom criteria using AI judges, experiment tracking that compares prompt versions and model configurations side by side, dataset management for organizing evaluation examples and golden test sets, real-time logging that captures LLM inputs, outputs, and metadata from production applications, AI-powered scoring that evaluates outputs for accuracy, relevance, tone, and custom criteria, and regression detection that alerts teams when model changes degrade evaluation scores.
Why AI-NATIVE
Braintrust is AI-native - an evaluation platform purpose-built for measuring and improving LLM application quality is inherently AI-native developer infrastructure.
Best for
Individual AI developers use Braintrust for prompt evaluation - free tier enabling systematic prompt comparison without building custom evaluation infrastructure.
Small AI teams use Braintrust for systematic LLM quality assurance - evaluation datasets and automated scoring preventing regressions in AI product quality.
Mid-market AI engineering teams use Braintrust for production LLM evaluation - experiment tracking informing model upgrade decisions and production monitoring surfacing quality issues.
Large AI organizations use Braintrust for enterprise LLM evaluation - systematic quality measurement across many models and applications with team collaboration.
Limitations
LangSmith offers tighter LangChain integration for tracing and evaluation — teams building on LangChain should compare both platforms for their observability workflow.
Arize AI covers both traditional ML and LLM monitoring — teams with both ML models and LLM applications may prefer a unified observability platform.
Braintrust's automated evaluation is only as good as the scoring rubrics configured — teams must invest in thoughtful evaluation design to get meaningful quality signals.
Alternatives by segment
| If you need… | Consider instead |
|---|---|
| LLM application observability | LangSmith |
| ML and LLM monitoring platform | Arize AI |
| ML experiment tracking | Weights & Biases |
Free tier available. Team at $150/month. Enterprise pricing negotiated. Annual billing discount.
2026-04-09
