Datadog LLM vs LangSmith
Updated March 9, 2026
Overview
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.
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.
Key features
Core capabilities each platform advertises.
- 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
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- 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
Cons
- 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
Pros
- 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
Cons
- 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
Datadog LLM or LangSmith — which should you choose?
Choose Datadog LLM if you wantChoose if you want
- 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
Choose LangSmith if you wantChoose if you want
- 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
Compare Datadog LLM and LangSmith on your own traffic
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 500+ models through one gateway.