How to estimate the cost of a custom AI solution
A custom AI solution has no list price: the cost comes from five factors — data readiness, model approach, integration count, the accuracy bar, and maintenance. Data readiness is usually the most underestimated cost. Beyond the build cost there is also a monthly run cost (API, infrastructure, maintenance) that any honest estimate includes.
- There is no list price for custom AI: the cost comes from the complexity of your problem, not from a menu.
- Five factors drive the price: data readiness, model approach, integration count, the accuracy bar, and maintenance.
- Data readiness is usually the most underestimated cost — messy data can double the effort before any model.
- An AI project has both a build cost and a monthly run cost (API, infrastructure, maintenance).
Why is there no fixed price for a custom AI solution?
"Custom" means the solution is built around your problem, your data, and your systems, and those differ from one company to another. Two projects that sound identical on the surface — "a chatbot that answers from our documents" — can differ tenfold in effort, depending on how clean the data is, how many systems have to be integrated, and how high the stakes of a mistake are. That is why a number thrown out before anyone has seen your process is not an estimate, it is a marketing promise. The honest answer to "how much" starts with "what drives the price", and the rest of this article walks through those factors.
Market context helps calibrate expectations. In 2025, 5.2% of Romanian enterprises with 10 or more employees used AI, the lowest share in the EU; the EU average was 20.0% (Eurostat, reference year 2025, published December 2025). Adoption is still early and budgets stay careful, all the more reason to understand exactly what you are paying for.
What factors drive the price of an AI solution?
Five factors explain almost all the price variation between projects. The table below shows what raises and what lowers each factor, so you can gauge for yourself where your project sits before any commercial conversation.
| Cost driver | Lowers the price when… | Raises the price when… |
|---|---|---|
| Data readiness | Data is already structured, clean, accessible | Data is scattered, in PDFs, unlabelled |
| Model approach | You use an existing API + RAG | You need fine-tuning or a custom model |
| Integration count | A single system, with a documented API | Many legacy systems, no APIs |
| Accuracy bar | You tolerate errors, a human checks at the end | Financial/legal stakes, errors cost a lot |
| Maintenance | Stable data, occasional use | Frequently changing data, high volume, strict SLA |
Why is data readiness the most underestimated cost?
Most people assume the money goes into the model. In practice, the most time-consuming part of an AI project is often gathering, cleaning, and structuring the data before the model sees anything. If your data is already organised, the model gets built quickly. If it is spread across spreadsheets, emails, and scanned PDFs, half the project becomes data-preparation work. That is why a company with tidy data can pay a fraction of what a company with the same need but chaotic data pays. Before you ask for a quote, it is worth assessing the state of your data — we wrote separately about how to get your company data ready for AI.
What is the difference between build cost and run cost?
An AI solution is not a one-off expense. There is a build cost (design, development, integration, testing) paid once, and a monthly run cost that continues for as long as you use the solution. The run cost covers calls to the model's API, the infrastructure it runs on, and maintenance — because data changes, systems get updated, and a model left unattended degrades over time. When you compare quotes, always ask about the monthly cost too, not just the build. A quote that looks cheap to build can be expensive to run, and vice versa.
How do I get a real estimate for my project?
The best estimate comes after someone has seen your problem, your data, and your systems — not from an online calculator. Tell us about the process in the contact form; after a short call in which we walk through the five cost drivers on your case, we send you an offer that fits. If the project is complex enough to be worth analysing in detail before building it, the recommendation may be a Tech Audit, our paid starting package: 4 hours on site and a report in 3 working days, with the opportunities ranked and the recommended next step. What we build, and how, is on the custom AI software development page.
In 2025, 5.2% of Romanian enterprises with 10 or more employees used AI, the lowest share in the EU; the EU average was 20.0% (Eurostat, reference year 2025, published December 2025).
Frequently asked questions
Why won't you just tell me a price?
Because any number given before seeing your data and systems would be a guess, not an estimate. "Custom" means the solution is built around your problem, and two projects that sound the same can differ tenfold in effort. An honest answer starts from the factors that drive the price; the real number comes after a conversation about your specific case.
What raises the cost of an AI project the most?
Usually the state of the data. If your data is scattered across spreadsheets, emails, and scanned PDFs, much of the project becomes cleaning and structuring work before the model sees anything. Next come a high accuracy bar (when an error costs a lot) and integration with legacy systems that have no API.
Is custom AI a one-time payment or a recurring cost?
Both. There is a one-time build cost (design, development, integration, testing) and a monthly run cost — calls to the model's API, infrastructure, and maintenance. A model left unattended degrades, so maintenance is not optional. When comparing quotes, always ask about the monthly cost too, not just the build.
Can I cut the cost by using an off-the-shelf model instead of a custom one?
Often, yes. For many needs, an existing API plus a RAG layer over your data is cheaper and faster than fine-tuning or a custom model. A custom model makes sense when you have a narrow domain, strict tone or format requirements, or constraints that rule out an external API. The right approach depends on the case, and we decide it together.
Is the AI audit free?
No. The Tech Audit is our paid starting package: 4 hours on site and a report in 3 working days, with 3–5 ranked opportunities and the recommended next step. A short call beforehand helps us decide whether the audit is justified for your project or whether a direct pilot makes more sense.
Want to discuss a project?
How we do it: Custom AI software development. Tell us about the process in the form; after a short call we send you an offer that fits.