AI tool comparison
Langfuse vs Helicone
A data-driven comparison of Langfuse (LLM Observability) and Helicone (LLM Observability) — features, GitHub momentum, and which tool fits your workflow.
Langfuse
LLM Observability
Helicone
LLM Observability
Feature comparison
Track your AI tool usage with Rize
LLM observability tools track your AI systems. Rize tracks the humans building them — how much time goes to debugging, prompt engineering, and evaluation workflows.
Frequently asked questions
What is the difference between Langfuse and Helicone?
Langfuse is a LLM Observability while Helicone is a LLM Observability. Key differences include: Self-hosted (Langfuse: Yes (Docker), Helicone: Yes (Docker)); Tracing (Langfuse: Full trace trees, Helicone: Request-level logging); Evaluations (Langfuse: Built-in eval framework, Helicone: Basic scoring).
Which is more popular, Langfuse or Helicone?
Both tools are actively developed open-source projects. Check the live GitHub stats above for the latest popularity data.
Can Rize track time spent in Langfuse and Helicone?
Yes. Rize automatically detects and tracks time spent in both Langfuse and Helicone. It runs in the background and categorizes your AI tool usage by project and client — no manual timers needed.