AI strategy & costsJune 20268 min read
Cut your LLM bill by 30 to 70%
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.
Should I use the same model for every task?
No. Routing each request to the cheapest model capable of handling it is enough in most cases: a simple classification or extraction does not need the most powerful model.
I never cut a bill by degrading quality. I cut it by no longer paying for what adds nothing.
- Cut the dead context: everything the model never reads still costs money.
- Favor short instructions and targeted examples over long directives.
- Group bulk processing into batches when latency allows.
How much can I really save, and how fast?
Taken together, these levers bring a bill down by 30 to 70% on most of the products I audit, at equal quality, and the first gains usually land within two to three weeks.