Keywords AI

Galileo AI vs LangSmith

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

Quick Comparison

	Galileo AI	LangSmith
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Freemium
Best For	AI teams who need to measure and improve the quality of their LLM outputs	LangChain developers who need integrated tracing, evaluation, and prompt management
Website	rungalileo.io	smith.langchain.com
Key Features	LLM output quality evaluation Hallucination guardrails RAG evaluation metrics Data-centric AI debugging Automated error detection	Trace visualization for LLM chains Prompt versioning and management Evaluation and testing suite Dataset management Tight LangChain integration
Use Cases	Monitoring LLM output quality Detecting and preventing hallucinations Evaluating RAG pipeline accuracy Debugging data quality issues Continuous quality assurance	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 Galileo AI vs LangSmith

Choose Galileo AI if you need

Monitoring LLM output quality
Detecting and preventing hallucinations
Evaluating RAG pipeline accuracy

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 Galileo AI

Galileo is a data intelligence platform for AI that helps teams evaluate, debug, and improve LLM applications. It provides metrics for hallucination detection, context adherence, chunk quality, and response completeness. Galileo's guardrails can be deployed in production to catch quality issues in real-time.

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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.

View LangSmith profile →Visit website

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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