Keywords AI

Arize AI vs LangSmith

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

Quick Comparison

	Arize AI	LangSmith
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Freemium	Freemium
Best For	ML teams who need comprehensive observability spanning traditional ML models and LLM applications	LangChain developers who need integrated tracing, evaluation, and prompt management
Website	arize.com	smith.langchain.com
Key Features	ML observability with LLM support Embedding drift detection Performance dashboards Automatic monitors and alerts Open-source Phoenix companion	Trace visualization for LLM chains Prompt versioning and management Evaluation and testing suite Dataset management Tight LangChain integration
Use Cases	Production ML and LLM monitoring Embedding quality monitoring Model performance tracking Drift detection for AI systems Root cause analysis for AI failures	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 Arize AI vs LangSmith

Choose Arize AI if you need

Production ML and LLM monitoring
Embedding quality monitoring
Model performance tracking

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 Arize AI

Arize AI provides an ML and LLM observability platform for monitoring model performance in production. For LLM applications, Arize offers trace visualization, prompt analysis, embedding drift detection, and retrieval evaluation. Their open-source Phoenix library provides local tracing and evaluation. Arize helps teams identify quality issues, debug failures, and continuously improve AI system performance.

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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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