Keywords AI

Braintrust vs LangSmith

Compare Braintrust and LangSmith side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

	Braintrust	LangSmith
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Freemium
Best For	AI teams who need a unified platform for logging, evaluating, and improving LLM applications	LangChain developers who need integrated tracing, evaluation, and prompt management
Website	braintrust.dev	smith.langchain.com
Key Features	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
Use Cases	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	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

When to Choose Braintrust vs LangSmith

Choose Braintrust if you need

Iterating on prompts with real production data
Running evaluations across model versions
Building golden datasets from production traffic

Pricing: Freemium

Choose LangSmith if you need

Debugging LangChain and LangGraph applications
Prompt iteration and A/B testing
LLM output evaluation and scoring

Pricing: Freemium

About Braintrust

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.

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About LangSmith

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.

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What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

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