Datadog LLM vs LangSmith: Observability, Prompts & Evals Comparison
Compare Datadog LLM and LangSmith side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 9, 2026
The short answer
Choose Datadog LLM if seamless integration with Datadog's full observability suite for unified application monitoring.
Choose LangSmith if deep integration with LangChain framework provides unmatched observability for LangChain applications.
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Quick Comparison
| 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 |
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Pros & Cons: Datadog LLM vs LangSmith
- +Seamless integration with Datadog's full observability suite for unified application monitoring
- +Built-in sensitive data scanning and prompt injection detection for enhanced security
- +Established enterprise platform with proven reliability and 9,700+ employees globally
- +Automatic cost estimation for LLM requests using public pricing models
- +Quick setup in seconds using their SDK
- −Premium automatically activated at $120/day when LLM spans detected with no opt-out
- −Can be expensive at scale compared to specialized LLM observability tools
- −Requires familiarity with Datadog ecosystem for optimal usage
- −Per-span pricing model may become costly for high-volume applications
- +Deep integration with LangChain framework provides unmatched observability for LangChain applications
- +Comprehensive evaluation capabilities with custom metrics and automated regression testing
- +Excellent tracing UI with detailed step-by-step execution visualization
- +Strong dataset management and annotation workflows for human feedback
- −Tight coupling to LangChain ecosystem limits flexibility for non-LangChain applications
- −UI scalability issues reported when working with very large datasets
- −Production testing limitations - relies primarily on production logs for test datasets
- −SaaS-only deployment with no self-hosting option may not suit all enterprise requirements
Pricing: Datadog LLM vs LangSmith
- · 5,000 traces per month
- · 14-day retention
- · One seat
- · Basic evaluations
- · 100,000 traces (overage ~$0.50 per 1,000)
- · 400-day retention
- · Full evaluations
- · Custom dashboards
- · Custom trace volumes
- · Custom retention policies
- · SSO
- · Dedicated support
When to Choose Datadog LLM vs LangSmith
- Unified monitoring for AI and traditional services
- Enterprise LLM monitoring at scale
- Correlating LLM performance with infrastructure
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
About Datadog LLM
Datadog LLM Observability is a comprehensive monitoring platform designed to help teams deliver LLM applications to production faster with end-to-end tracing across AI agents, structured experiments, and robust quality and security evaluations. The platform provides complete visibility into inputs, outputs, latency, token usage, and errors across AI agent workflows. It features structured experiment management for testing prompt changes, model swaps, and parameter tuning, along with quality evaluations including hallucination detection and output clustering for drift identification. Security features include sensitive data scanning and prompt injection detection. As part of the broader Datadog platform, LLM Observability integrates seamlessly with APM and Real User Monitoring for unified full-stack visibility, allowing teams to correlate LLM workloads with backend services, infrastructure, and user sessions.
About LangSmith
LangSmith is LangChain's observability and evaluation platform for building production-grade LLM applications. Founded in July 2023 by Harrison Chase and Ankush Gola as part of the LangChain ecosystem, LangSmith provides comprehensive tracing of every LLM call, chain execution, and agent step with detailed visibility into inputs, outputs, latency, token usage, and cost. The platform includes annotation queues for human feedback, dataset management for systematic evaluation, and regression testing capabilities for prompt changes. With over 1 million developers using LangChain products globally, LangSmith has become the go-to debugging and monitoring tool for teams building with the LangChain framework, serving major enterprises including Klarna, LinkedIn, Replit, GitLab, Elastic, and Cisco.
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 & Evalstools →Other Observability, Prompts & Evals Tools
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