What an AI project costs in the Netherlands

What actually drives the price of an AI project

The headline numbers above are real, but a price only makes sense once you understand what moves it. In practice, six drivers explain almost every euro of difference between a €2,500 engagement and a €50,000 one.

• Scope and number of use cases. One clearly defined process — quoting, invoice matching, a support inbox — is cheap and predictable. Three loosely defined "AI ambitions" is a research programme. Cost scales with the number of distinct problems, not the ambition behind them.
• Data readiness and cleanup. This is the single most underestimated line item. If your data lives in clean, accessible systems, you build fast. If it sits in scattered spreadsheets, scanned PDFs and inconsistent labels, someone has to clean and structure it first — often a third of the effort before a model is ever trained.
• Integration with existing and legacy systems. A standalone proof of concept is straightforward. Wiring AI into your ERP, CRM or a decade-old line-of-business system — with authentication, error handling and real workflows — is where production budgets are spent.
• Model choice and running costs. The model you pick sets an ongoing bill, not a one-off cost. A hosted API charges per request; a self-hosted open model shifts the cost to infrastructure. High-volume use cases make inference a monthly line you must budget for from day one.
• Compliance work. The EU AI Act and AVG (GDPR) mean documentation, risk classification, data-handling controls and human oversight. For higher-risk use cases this is genuine engineering and legal effort, not a checkbox.
• Change management and adoption. A working model that nobody uses returns nothing. Training, redesigned workflows and buy-in from the people doing the work are part of the cost — and the part that decides whether the ROI ever arrives.

Price ranges by project type

Rather than an open-ended "it depends," here is how the stages line up against our own fixed prices.

• A bounded audit — €2,500. A fixed-scope assessment of where AI genuinely fits, which processes are viable, what your data needs, and what the realistic return looks like. It ends with a decision, not an open tab.
• A proof of concept — €20,000. One prioritised use case, built and tested against real data, so you can see it work before committing to production. This is where you buy down risk cheaply.
• A production build — from €50,000. The PoC hardened into something integrated, secure, compliant and maintainable. The "from" matters: integration depth, data work and compliance scope move this number.
• Ongoing running and maintenance. Budget for hosting and inference, monitoring, and periodic iteration or retraining. This is a recurring cost, not an afterthought — see how long AI implementation takes for how the timeline maps to spend.

Fixed price vs. day rate — why open-ended budgeting fails

Much of the market sells AI on day rates. The problem is simple: an open-ended day rate makes budgeting impossible. You cannot tell your board what a project will cost, because the number depends on how long someone chooses to work. Discovery drifts, scope creeps, and the meter runs regardless of outcome.

We work on fixed prices for exactly this reason. A fixed price forces the hard scoping conversation up front, transfers the overrun risk to us instead of you, and gives you a number you can actually approve. You also own the code — no lock-in, no rental. See our pricing for the full breakdown.

Total cost of ownership and the honest ROI picture

The build is not the whole cost. Total cost of ownership includes hosting and inference, monitoring for drift, iteration as your processes change, and occasional retraining. A model is a living system, and pretending otherwise is how projects quietly go over budget in year two.

The returns are real but rarely instant. Time savings show up in weeks — knowledge workers recover a median of around 6.4 hours per week according to recent workplace research — but full ROI on a production build often takes two to four years. Some well-chosen projects do far better: a 2025 study found high-return AI projects delivering roughly 150% first-year returns through savings. The biggest productivity gains cluster in specific functions — software/IT and customer service around 32%, procurement around 27% — which is why picking the right process matters more than picking the fanciest model.

Scale shapes the odds, too. In one survey 55% of SMEs reported AI productivity gains versus 72% of large enterprises — smaller firms win less often, usually because scope and data discipline slip. And in the Netherlands specifically, CBS reports that 29.8% of SMEs now use AI, while 74.6% of non-adopters cite a lack of experience as the reason they haven't started.

Hidden costs, and how to keep a first project cheap

The costs SMEs underestimate are consistent: data cleanup before any model runs, the monthly inference bill on high-volume use cases, integration into legacy systems, compliance documentation, and the change-management effort to get people actually using the tool. None of these appear in a demo; all of them appear in production.

The way to de-risk is not to spend more — it's to start smaller. Pick one process, put a fixed price on it, and prove the return before you scale. A €2,500 audit followed by a €20,000 proof of concept tells you almost everything a €50,000 build would, at a fraction of the exposure. If a grant can offset part of it, check whether you qualify for AI subsidies for SMEs. Start narrow, measure honestly, and let one proven win pay for the next.