On the bills I audit, the problem is almost never the price per token. It is useless context sent on every call and the most expensive model plugged in everywhere by default. Here is what I cut first.
Why is my AI bill exploding when usage isn't going up?
In nearly every engagement, the problem is not the price per token but the way the tokens are spent: useless context sent back on every call, the most expensive model used everywhere by default, answers regenerated when they already existed. So I start by measuring where each euro goes, not by cutting at random.
Can caching really cut my LLM bill?
Yes, and it is almost always the first lever: in production, a large share of calls are near-duplicates. Caching the answers on identical inputs removes that waste without changing anything for your users, often within a few days.
