Keywords AI

LangSmith vs Patronus AI

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

Quick Comparison

	LangSmith	Patronus AI
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Enterprise
Best For	LangChain developers who need integrated tracing, evaluation, and prompt management	AI teams that need rigorous, automated quality evaluation and safety testing
Website	smith.langchain.com	patronus.ai
Key Features	Trace visualization for LLM chains Prompt versioning and management Evaluation and testing suite Dataset management Tight LangChain integration	Automated LLM evaluation platform Hallucination detection RAG-specific evaluation metrics Red-teaming capabilities CI/CD integration
Use Cases	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	Detecting hallucinations in production RAG quality evaluation Adversarial testing of LLM systems Continuous evaluation in CI/CD Model comparison and selection

When to Choose LangSmith vs Patronus AI

Choose LangSmith if you need

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

Pricing: Freemium

Choose Patronus AI if you need

Detecting hallucinations in production
RAG quality evaluation
Adversarial testing of LLM systems

Pricing: Enterprise

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

About Patronus AI

Patronus AI provides automated evaluation and testing for LLM applications. The platform detects hallucinations, toxicity, data leakage, and other failure modes using specialized evaluator models. Patronus offers pre-built evaluators for common use cases and supports custom evaluation criteria, helping enterprises ensure AI safety and quality before and after deployment.

View Patronus AI 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.

Browse all Observability, Prompts & Evals tools →