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Confident AI vs LangSmith

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

Quick Comparison

	Confident AI	LangSmith
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Open Source	Freemium
Best For	Developers who want to add automated LLM evaluation testing to their CI/CD pipeline	LangChain developers who need integrated tracing, evaluation, and prompt management
Website	confident-ai.com	smith.langchain.com
Key Features	DeepEval open-source evaluation framework 14+ evaluation metrics Benchmarking suite Pytest integration Conversational evaluation support	Trace visualization for LLM chains Prompt versioning and management Evaluation and testing suite Dataset management Tight LangChain integration
Use Cases	Unit testing LLM applications Automated evaluation in CI/CD pipelines Benchmarking across model versions RAG evaluation with custom metrics Regression testing for prompts	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 Confident AI vs LangSmith

Choose Confident AI if you need

Unit testing LLM applications
Automated evaluation in CI/CD pipelines
Benchmarking across model versions

Pricing: Open Source

Choose LangSmith if you need

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

Pricing: Freemium

About Confident AI

Confident AI develops DeepEval, the most popular open-source LLM evaluation framework. DeepEval provides 14+ evaluation metrics including faithfulness, answer relevancy, contextual recall, and hallucination detection. The Confident AI platform adds collaboration features, regression testing, and continuous evaluation in CI/CD pipelines.

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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.

Browse all Observability, Prompts & Evals tools →