Many providers only reveal the price once you're already half-convinced — that's not an accident, it's a tactic. Here you get the numbers up front. "What does AI cost?" has no flat answer, but it does have clear cost drivers. Know them, and no one can squeeze a vague budget out of you — including the line items quotes tend to "forget".
Why there is no flat rate
AI software isn't an off-the-shelf product. A simple chat assistant on your documents is something entirely different from an AI agent that acts inside your systems. Price hangs on three levers: scope (one feature vs. a whole application), integration depth (standalone vs. wired into CRM/ERP) and your data situation (clean data vs. prep needed). Know those three and you roughly know the budget.
The common pricing models
1. Fixed price per project
One set price for a clearly defined deliverable. Great when scope is well-defined — you get planning certainty, and the estimation risk sits with the provider.
2. Time & material
Billed by actual effort. Sensible for work that evolves during delivery — flexible, but with a less fixed cost ceiling.
3. Sprint or prototype packages
A bounded package (e.g. a prototype in about two weeks) at a predictable price. The cheapest way to test feasibility and value before you invest big.
A rough sense of the numbers
As rough orientation (not a binding quote — it always depends on the actual project):
- Prototype / proof of concept: the smallest investment — ideal to validate an idea in days instead of months.
- Bounded application (e.g. an assistant on your data, one automation): mid-range investment, often shippable in weeks.
- Larger, integrated solution (multiple systems, agents, roles): higher investment, with a correspondingly bigger lever.
What matters more than the absolute number is the ratio: a use-case that saves many work hours per month usually pays for itself within the first quarter.
The hidden costs most people forget
The build price is only part of it. These running items belong in any honest calculation:
- Model / API costs: LLM usage is usually billed per request — predictable, but not zero.
- Hosting & operations: server, database, monitoring — ideally EU-hosted and GDPR-ready.
- Maintenance & iteration: models and requirements change; a small ongoing budget keeps the solution current.
- Data preparation: often the underestimated item — good results need clean, accessible data.
- Change & training: the best solution is worthless if the team doesn't use it.
How to save budget — without cutting the wrong corner
The biggest saving lever is the right order. Start with one clearly bounded use-case as a prototype instead of building everything at once. You invest little first, learn fast, and only scale what proves itself. Insist on EU hosting (it avoids data-protection rework) and a solution that fits your existing tools instead of creating a parallel world.
Conclusion
AI software costs as much as the problem it solves — and good providers make exactly that transparent: scope, model and running costs named clearly, not blanket promises. Our free AI use-case check shows which use-case pays off first for you. To spot the right implementation partner, read Choosing an AI agency: 7 criteria.