Keywords AI

Datadog LLM vs LangSmith

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

Quick Comparison

	Datadog LLM	LangSmith
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Enterprise	Freemium
Best For	Enterprise teams already using Datadog who want to add LLM monitoring	LangChain developers who need integrated tracing, evaluation, and prompt management
Website	datadoghq.com	smith.langchain.com
Key Features	LLM monitoring within Datadog platform Unified APM + LLM observability Automatic instrumentation Cost and token tracking Integration with existing Datadog dashboards	Trace visualization for LLM chains Prompt versioning and management Evaluation and testing suite Dataset management Tight LangChain integration
Use Cases	Unified monitoring for AI and traditional services Enterprise LLM monitoring at scale Correlating LLM performance with infrastructure Compliance and audit logging Large-scale production monitoring	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 Datadog LLM vs LangSmith

Choose Datadog LLM if you need

Unified monitoring for AI and traditional services
Enterprise LLM monitoring at scale
Correlating LLM performance with infrastructure

Pricing: Enterprise

Choose LangSmith if you need

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

Pricing: Freemium

About Datadog LLM

Datadog's LLM Observability extends its industry-leading APM platform to AI applications. It provides end-to-end tracing from LLM calls to infrastructure metrics, prompt and completion tracking, cost analysis, and quality evaluation—all integrated with Datadog's existing monitoring, logging, and alerting stack. Ideal for enterprises already using Datadog who want unified observability across traditional and AI workloads.

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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 →