Braintrust — Observability, Prompts & Evals Platform
Observability, Prompts & EvalsLayer 4FreemiumWhat is Braintrust?
Braintrust is an AI observability and evaluation platform that helps teams build, monitor, and improve AI applications in production. The platform enables users to turn production traces into evaluations, compare prompts and models, and improve quality with every release. Built on a custom database called Brainstore designed specifically for AI data complexity, Braintrust provides real-time trace inspection, performance monitoring for latency, cost, and quality, along with automated alerts. The platform features Loop Agent for AI-assisted optimization of prompts, scorers, and datasets, and offers framework-agnostic native SDKs for Python, TypeScript, Go, Ruby, and C# with no vendor lock-in. Braintrust is SOC 2 Type II, GDPR, and HIPAA compliant with SSO/SAML integration and granular role-based access control.
Key Features
- ✓Real-time LLM logging and tracing
- ✓Built-in evaluation framework
- ✓Prompt playground
- ✓Dataset management
- ✓Human review workflows
Pros & Cons
Pros
- +Custom-built Brainstore database optimized for AI data with fast full-text search and low latency
- +Framework agnostic with native SDKs for multiple languages and no vendor lock-in
- +Comprehensive security and compliance certifications including SOC 2 Type II, GDPR, and HIPAA
- +AI-assisted Loop Agent for automated optimization of prompts and evaluations
- +Strong backing with $80M Series B funding and customers including Vercel, Notion, Coursera, and Dropbox
Cons
- -Relatively new platform founded in 2022, less mature than some competitors
- -Pro plan at $249/month may be expensive for smaller teams or individual developers
- -Limited public information about advanced features and customization options
Braintrust Pricing
Common Use Cases
AI teams who need a unified platform for logging, evaluating, and improving LLM applications
- •Iterating on prompts with real production data
- •Running evaluations across model versions
- •Building golden datasets from production traffic
- •Human-in-the-loop review of LLM outputs
- •Cost and latency optimization
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