LangSmith vs Helicone
Detailed side-by-side comparison to help you choose the right tool
LangSmith
🔴DeveloperAI Observability
LangSmith is LangChain's commercial observability, evaluation and prompt management platform for LLM apps and agents in production.
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Starting Price
FreeHelicone
🔴DeveloperLLM Observability
Open-source LLM observability and AI gateway — logs every prompt, response, cost, and latency across 20+ providers with a one-line proxy or async SDK, plus caching, retries, and prompt experiments.
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Starting Price
FreeFeature Comparison
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💡 Our Take
Choose LangSmith if you need deep evaluation tooling, prompt versioning, and hierarchical agent traces tied to the LangChain ecosystem. Choose Helicone if you primarily need a lightweight proxy-based observability layer for OpenAI/Anthropic API calls with caching and rate limiting, and want simpler setup at a lower starting price.
LangSmith - Pros & Cons
Pros
- ✓Best-in-class integration if you already use LangChain or LangGraph.
- ✓Eval suites are practical enough to actually gate releases on, not just dashboards.
- ✓Self-hosted Enterprise tier covers SOC 2 and regulated environments.
Cons
- ✗Per-trace pricing on Plus surprises teams that scale production traffic quickly.
- ✗Non-LangChain stacks work but trade ergonomic polish for SDK overhead.
- ✗Some eval features require additional LLM spend on top of the platform fee.
Helicone - Pros & Cons
Pros
- ✓5-minute proxy integration captures full traces, cost, and latency across 20+ providers
- ✓Real AI gateway features (caching, retries, fallback, key vault) replace a custom proxy
- ✓MIT-licensed and self-hostable on Postgres + ClickHouse — passes regulated procurement
Cons
- ✗Proxy mode adds a network hop unless self-hosted in your own region
- ✗Prompt experiment UX is less mature than dedicated eval platforms like Braintrust
- ✗Self-hosting requires running ClickHouse, which is an extra ops surface
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