Cutting LLM costs in Spring AI starts with two choices: which model answers a request, and what defaults your ChatClient adds to every one it sends.
Neither is worth changing until you can see where the tokens go. That is why this article starts with measurement.
This is Part 1 of four, and it covers the first three of ten cost drivers. Driver #0 tells you where the money actually goes; #1 and #2 are the two decisions that shape every request your application sends. The remaining seven attach to what you build here.
A note on the numbers: where a price ratio matters for the argument (input vs. output, cache read vs. write), this series quotes real July 2026 list prices with a link. All other examples use a flat rate of $1 per million input tokens, so you can redo the calculation with your own provider's price sheet. You should do that, because these prices change every few months.
Driver #0 — Spring AI observability measurement: you cannot cut what you cannot see
