langfusevshelicone

what each one actually is

Langfuse is an open-source llm observability platform that traces every call, tool use, and token across your application, with prompt management and evaluation tooling built in. it's framework-agnostic and integrates with most popular agent frameworks and model providers through an sdk you add to your code.

Helicone is an open-source llm observability proxy. instead of instrumenting your code, you route your api calls through helicone's endpoint and it logs cost, latency, and request/response data automatically — minimal setup, works with any openai-compatible client out of the box.

pricing, honestly

both are free to self-host with no feature gate on the open-source version. for hosted/cloud usage, both offer generous free tiers that cover early-stage and small production apps, with paid tiers scaling by trace or request volume once you outgrow them.

neither is meaningfully cheaper than the other at comparable usage — the real cost difference is engineering time. helicone's setup takes minutes; langfuse's setup takes longer but returns more structured data for that time investment.

what it's actually like to use them

helicone's appeal is the zero-friction setup — swap one url, add a header, and you have logging across every api call your app makes, including ones you forgot existed. it's the fastest path from "no observability" to "some observability."

langfuse asks you to instrument traces and spans explicitly, which means more upfront work but a payoff in visibility once you have multi-step agents, tool calls, or retrieval pipelines — you can see the actual shape of what happened, not just a list of raw api requests. for anything beyond a single-call chatbot, that structure matters.

who langfuse is for

• production llm apps with multi-step agents, tool use, or retrieval pipelines that need structured tracing
• teams that want prompt versioning and evaluation pipelines alongside observability
• self-hosters who want full control over where prompts and outputs are stored

who helicone is for

• teams that want logging running in under five minutes with no code changes beyond a url swap
• simpler llm apps where individual api call cost and latency are the main thing you're tracking
• anyone evaluating observability tools who wants to try one with the lowest possible setup cost first

when to avoid each

don't choose helicone if you need to see the structure of multi-step agent execution — it logs calls, not application logic, so complex workflows will look like a flat list of requests rather than a connected trace.

don't choose langfuse if you just want quick visibility into api cost and latency with zero integration work — the proxy approach will get you there faster.

stuff their landing pages won't tell you

• helicone's proxy approach means an outage on their end can affect your app's request path — check their status history before routing production traffic through it
• langfuse's tracing sdk needs to be added at every relevant point in your code — it's easy to under-instrument and end up with gaps in visibility, especially as your codebase grows
• both tools' free tiers reset trace/request retention after a period — check the retention window if you need historical data for compliance or debugging older incidents
• self-hosting either tool means you're responsible for the underlying database (postgres for langfuse) staying healthy and backed up
• neither tool replaces actual evaluation of output quality — they tell you what happened, not whether the response was good

the call

langfuse for production llm apps, especially anything with agents or multi-step workflows where understanding the structure of execution matters. the upfront instrumentation cost pays for itself the first time you need to debug a weird agent loop.

helicone if you want observability today with the least possible setup, and your app is closer to "call the model, get a response" than a complex multi-step pipeline. you can always add langfuse later once the proxy approach stops giving you enough detail.