Braintrust vs LangSmith
Updated March 9, 2026
Overview
Braintrust is an end-to-end AI product platform trusted by companies like Notion, Stripe, and Vercel. It combines logging, evaluation datasets, prompt management, and an AI proxy with automatic caching and fallback. Braintrust's evaluation framework helps teams measure quality across prompt iterations with customizable scoring functions.
LangSmith is LangChain's observability and evaluation platform for LLM applications. It provides detailed tracing of every LLM call, chain execution, and agent step—showing inputs, outputs, latency, token usage, and cost. LangSmith includes annotation queues for human feedback, dataset management for evaluation, and regression testing for prompt changes. It's the most comprehensive debugging tool for LangChain-based applications.
Key features
Core capabilities each platform advertises.
- Real-time LLM logging and tracing
- Built-in evaluation framework
- Prompt playground
- Dataset management
- Human review workflows
- Trace visualization for LLM chains
- Prompt versioning and management
- Evaluation and testing suite
- Dataset management
- Tight LangChain integration
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
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
Pros
- Deep integration with LangChain framework provides unmatched observability for LangChain applications
- Comprehensive evaluation capabilities with custom metrics and automated regression testing
- Excellent tracing UI with detailed step-by-step execution visualization
- Strong dataset management and annotation workflows for human feedback
Cons
- Tight coupling to LangChain ecosystem limits flexibility for non-LangChain applications
- UI scalability issues reported when working with very large datasets
- Production testing limitations - relies primarily on production logs for test datasets
- SaaS-only deployment with no self-hosting option may not suit all enterprise requirements
Braintrust or LangSmith — which should you choose?
Choose Braintrust if you wantChoose if you want
- 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
Choose LangSmith if you wantChoose if you want
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
- Team collaboration on prompt engineering
- Regression testing for LLM apps
Compare Braintrust and LangSmith on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.