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?