How to make AI coding spend predictable without capping your engineers
Usage-based AI coding spend swings with how hard your team works. Cap it and you slow them down; leave it and the budget burns. There is a third option.
The dilemma
Token-based spend tracks usage, daily. FinOps dashboards and budget alerts help you see it, and they are worth setting up, but they treat the symptom. The number still moves with every busy week.
The structural fix
Move the heavy, repeatable work off the variable meter. Most AI coding is translation, not invention: turning intent into correct code using the right APIs. A compiler can do that part deterministically, at a flat price, with no model call.
Pauhu Fusion is a flat 279 EUR per seat, falling the more you scale, for the composition. Your engineers keep their own assistant for intent and judgment; the compiler handles the rest, off the meter. The predictable line comes back, and no one gets capped.
And you can prove it
Every call returns a receipt: the tokens and energy that request did not spend. So the saving is measured per call, not asserted in a slide.