- ·Per-token pricing scales with usage. Owned hardware scales with capacity.
- ·At steady high volume, on-prem inference often costs less per unit.
- ·Cloud hides cost in egress, rate limits, and price changes you do not control.
- ·Cost is a reason to consider on-prem, but control is usually the bigger one.
Cloud AI pricing is designed to feel free at the start. A few cents per call, no hardware, no commitment. That math is honest for a prototype and misleading for a production workload that runs the same step ten thousand times a day, every day. At scale, the question is not the price of one call. It is the shape of the bill.
The demo price and the production bill
In a demo you make a handful of calls and the cost rounds to zero. In production the same workflow runs continuously, and the cost is a straight line that climbs with every document, every retry, and every user. Worse, it is a line you do not fully control: the provider sets the rate, can change it, and can add charges for moving your own data back out.
Fixed cost vs variable cost
Owning your inference flips the cost from variable to fixed. You buy capacity once and run as much work through it as it can hold. Below a certain volume, the cloud's pay-per-use is cheaper and simpler. Above it, a fixed cost you have already paid beats a variable one that never stops climbing. The crossover point arrives faster than most teams expect once a workflow is genuinely in production.
When owning your inference pays off
If your volume is steady and high, your data is sensitive, and you intend to run for years rather than weeks, owning the inference usually wins on total cost as well as on control. For regulated teams the cost case and the residency case point the same way, which is why on-prem rarely gets justified on price alone. It is the workload that cannot use the cloud anyway, made cheaper to run at the same time.
