LangSmith vs Sentry (2026)
Updated March 10, 2026
Overview
LangSmith is LangChain's platform for tracing, evaluating, and deploying AI agents. Compare its features, pricing, pros and cons, and alternatives.
Sentry provides runtime error monitoring and performance observability for AI applications. Its LLM monitoring capabilities track model calls, token usage, and latency alongside traditional error tracking. Sentry helps teams catch and debug issues in production AI pipelines with detailed stack traces and context.
Key features
Core capabilities each platform advertises.
- Native tracing for LangChain and LangGraph
- OpenTelemetry and multi-language SDKs
- Offline and online evaluations
- Annotation queues and datasets
- Prompt Hub with versioning
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Native LangChain and LangGraph tracing with no extra instrumentation
- Evals, datasets, and annotation queues sit in the same workspace as traces
- Tracing, deployment, and sandboxes from one vendor
- Framework-agnostic SDKs and OpenTelemetry support
Cons
- Per-seat pricing on Plus, and the free plan allows one seat
- 14-day base retention, with longer retention billed at a higher rate
- LLM Gateway is still in beta
- Self-hosting and hybrid deployment are Enterprise only
Pros
- Developer-friendly platform
- Production-ready
- Good performance
- Active development
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve
LangSmith or Sentry — which should you choose?
Choose LangSmith if you wantChoose if you want
- Debugging LangChain and LangGraph agents
- Evaluating agent output against datasets
- Human review through annotation queues
- Deploying long-running agents
Choose Sentry if you wantChoose if you want
Compare LangSmith and Sentry 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 1,000+ models through one gateway.
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