LangSmith vs Lunary (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.
Lunary is an open-source LLM observability platform for monitoring AI applications in production. It provides request tracing, cost tracking, user analytics, and prompt management with a clean dashboard. Lunary can be self-hosted for data privacy and offers a managed cloud option.
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
- Production-ready platform
- Good performance
- Active development
- Strong features
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve required
LangSmith or Lunary — 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 Lunary if you wantChoose if you want
Compare LangSmith and Lunary on your own traffic
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