Yes, exactly - they are different layers.
I am not comparing Langfuse and LiteLLM as if they are the same category of tool.
The point of the article is to compare where each tool sits in the LLM workflow:
Langfuse helps you observe, trace, debug, and evaluate what happened.
LiteLLM sits closer to the request path as a gateway/proxy for routing, provider abstraction, spend tracking, and budgets.
Runcap is narrower again: it focuses on AI coding agent runs - capping routed spend before it compounds, and using a Proof Gate to decide whether an AI-generated PR earned merge eligibility.
So the comparison is not “which one replaces the others?”
It is:
What problem are you trying to solve?
Observability after the run?
Routing and budget control across providers?
Or mission-level control and proof before trusting an AI-generated code change?
Talha
Senior AI / Full-Stack Engineer | Building & running production AI systems
are not langfuse an observability plaform where as litellm a gateway , how you compare both