Cut your LLM bill by 30 to 70%

When a client calls us about their AI bill, the problem is almost never the price per token. It is the way the tokens are spent: useless context sent on every call, the most expensive model used everywhere by default, answers regenerated when they already existed. So we start by measuring, not by cutting.

Caching, your first reflex

A huge share of production calls are near-duplicates. Caching the answers on identical inputs, and the stable context on similar inputs, removes that waste without changing anything for the user. It is often the most profitable lever, and the fastest to put in place.

The right model for the right task

Routing each request to the cheapest model capable of handling it changes everything. A simple classification or extraction does not need the most powerful model. We keep the heavy artillery for the cases that deserve it, and shift the rest to lighter models.

You never cut a bill by degrading quality. You cut it by no longer paying for what adds nothing.

Prompts that get straight to the point

• 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.

Taken together, these levers bring a bill down by 30 to 70% on most of the products we audit. And above all, they make the cost legible: you finally know where every euro goes.