Helicone vs LangSmith (2026)
Updated March 9, 2026
Overview
Helicone is an open-source LLM observability and proxy platform. By adding a single line of code, developers get request logging, cost tracking, caching, rate limiting, and analytics for their LLM applications. Helicone supports all major LLM providers and offers both proxy and async logging modes. Popular with startups for its generous free tier and simple integration.
LangSmith is LangChain's platform for tracing, evaluating, and deploying AI agents. Compare its features, pricing, pros and cons, and alternatives.
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
- Open-source with 5.2K GitHub stars and strong community support
- Minimal latency overhead (under 1ms for self-hosted)
- Flexible deployment options including cloud-hosted, self-hosted, or SDK-only
- Y Combinator-backed with SOC 2 Type II and HIPAA compliance
- No code changes required for AI Gateway features like caching and rate limiting
Cons
- Adds computational cost and one network hop for cloud deployment
- Limited pricing transparency on website
- May require infrastructure expertise for self-hosted setup
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
Helicone or LangSmith — which should you choose?
Choose Helicone if you wantChoose if you want
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
Compare Helicone and LangSmith on your own traffic
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