What a running AI really costs
“Beyond the setup, what does it cost me each month?”
The observation
People often talk about the project cost, rarely about the operating cost. Yet an AI runs every day: it consumes compute, it produces logs, it needs monitoring. The most underestimated line item is neither hardware nor licenses: it's the human time spent supervising and adjusting.
How we handle this topic
Compute cost, proportional to usage
It depends on the volume processed and the model chosen. That's why there's no single price: a chatbot on 500 conversations a month and an analysis of 20,000 documents have nothing in common.
Infrastructure cost
Hosting, storage, backups. In private cloud it's shared; when self-hosted, it depends on the hardware chosen.
Supervision cost — the forgotten line item
Checking results, adjusting rules, handling edge cases. This is what keeps an AI reliable over time, and it's what you never see in an initial quote.
How we assess it
We measure the cost on your real volumes before we commit. No theoretical estimate: we take your numbers and project them.
What it changes for you
- You know the operating cost before you commit
- Model optimization is part of the work, not after-sales service
- The supervision cost is built in, not discovered afterward
The limit, stated plainly: We don't publish a price list, deliberately: a figure taken out of context is wrong. The cost depends on volume, scope and the level of confidentiality — three parameters determined by your case, not in a table.
Is your situation specific?
This topic is handled case by case. The assessment frames it around your numbers, not in the abstract.