Langfuse vs Helicone
An honest, context-aware comparison. No affiliate links. No paid placements. Just the data that helps you decide.
Langfuse
Open-source LLM engineering platform — trace, evaluate, and debug your AI application in production.
Helicone
LLM observability proxy — one line of code to monitor costs, latency, and quality across all AI calls.
Side-by-Side Comparison
Objective metrics, no spin.
Every team running LLM applications in production. Langfuse makes debugging, cost tracking, and quality evaluation possible.
Simple prototyping — adds overhead before you have traffic worth monitoring.
Startups and solo developers wanting instant LLM observability without installing an SDK. The fastest path from zero to monitored AI calls.
Teams needing deep tracing of multi-step agent workflows — Langfuse offers more granular observability.
Shared Integrations (2)
Both tools connect to these — you won't lose workflow continuity whichever you pick.
Both suited for: small, medium companies
Since both tools target small and medium companies, your decision should hinge on the specific use case above rather than company fit. Try the AI Advisor to get a recommendation tailored to your exact stack.
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Other AI Observability & MLOps Tools to Consider
If neither is the right fit, these are the next best alternatives in the same category.