The first time you deploy an LLM application, cost usually isn't the biggest concern.
You have one model, a few API calls, and a relatively small number of users. Even if a request costs a few cents, it doesn't seem significant.
Then the application grows.
More users start sending requests. Developers introduce additional models. Different teams begin experimenting with their own AI applications. Agents start making multiple model calls within a single workflow. MCP servers add even more tools and context to the process.
Suddenly, the AI bill looks very different.
