Compare Langfuse and Helicone for open-source LLM observability, traces, prompts, evals, AI Gateway, request logs, cost tracking, self-hosting, and pricing units.
Choose Langfuse when evaluation loops and prompt governance are central. Choose Helicone when the immediate pain is routing, logging, and controlling LLM request cost through an AI Gateway.
LangfuseTeams that need trace graphs, prompt management, datasets, evals, experiments, and self-hostable LLM engineering workflows.
HeliconeTeams that need gateway routing, request-level logs, cost visibility, sessions, caching, and quick proxy-style integration.
| Criterion | Langfuse | Helicone |
|---|---|---|
| Unit model | Langfuse units combine traces, observations, and scores. | Helicone pricing is shaped by requests, storage, retention, and gateway usage. |
| Primary job | Trace and evaluate AI agents, prompts, datasets, and production quality. | Route model traffic, log requests, monitor cost, cache responses, and add fallbacks. |
| Open-source posture | Open-source and self-hostable with cloud plans for production. | Open-source and self-hostable with AI Gateway integration paths. |
| Eval depth | Stronger for prompt versions, datasets, experiments, and online/offline evals. | Good for scores and monitoring, but often paired with deeper eval workflows. |
| Best pilot | Instrument one agent flow and create evals from real trace failures. | Proxy one model endpoint and measure cost, latency, cache rate, and failure routing. |
Choose Langfuse when evaluation loops and prompt governance are central. Choose Helicone when the immediate pain is routing, logging, and controlling LLM request cost through an AI Gateway.
Langfuse should be evaluated when the team wants traces to become eval datasets, prompt versions, experiments, and quality feedback loops. Its unit model matches that broader engineering surface.
Helicone is strongest when the first problem is getting all model traffic through a visible, controllable layer. Gateway routing, cost analytics, caching, fallbacks, and request/session views make it practical for teams that need operational visibility quickly.
A common split is Helicone for gateway and request observability, Langfuse for prompt/eval governance. That can work, but duplicate logging, retention, PII handling, and cost reporting need a single owner.
Choose Langfuse when evaluation loops and prompt governance are central. Choose Helicone when the immediate pain is routing, logging, and controlling LLM request cost through an AI Gateway.
Teams that need gateway routing, request-level logs, cost visibility, sessions, caching, and quick proxy-style integration.
Teams that need trace graphs, prompt management, datasets, evals, experiments, and self-hostable LLM engineering workflows.
Langfuse is better for trace-to-eval workflows and prompt governance. Helicone is better for AI Gateway, request logging, routing, and cost monitoring.
Yes. Both official sources position them as open-source LLM observability tools with self-hosting options.
Yes, but define ownership: Helicone can own gateway/request telemetry while Langfuse owns evals, prompts, and trace governance.