What custom AI actually costs — a transparent breakdown
bicaralabs · GUIDE“How much does AI cost?” is the question every business asks and almost no vendor answers straight. Here’s the honest version.
What you’re actually paying for
An AI system has four cost centres, and only one of them is the model:
- Scoping & strategy — figuring out what to build and whether it’s worth it. Small, but skipping it is the most expensive mistake there is.
- Build — the engineering: integrations, the data plumbing, the interface, the guardrails. This is the bulk.
- Inference — what you pay per use (API calls or the servers if self-hosted). Usually smaller than people fear.
- Run & maintain — monitoring, updates, and the person who owns it after launch.
Where money gets wasted
Three places, every time: building a custom version of something you could have bought; over-engineering a pilot that was never going to ship; and paying for a frontier model where a smaller, cheaper one would score just as well on your task.
Right-sizing
We start every engagement from ROI, not capability. Name the number that has to move, size the build to that number, and favour what you already own. A focused, well-scoped project is often a fraction of a vague “AI transformation” — and far more likely to pay for itself.
The right question isn’t “how much does AI cost?” It’s “what’s the smallest thing that moves the number, and what does that cost?” That, we can answer on a call.