What an AI proof of concept actually costs (2026)

What an AI proof of concept actually costs in 2026

Ask three vendors what an AI pilot costs and you can get three numbers an order of magnitude apart. That spread reflects what each step really buys, how much risk sits in your data, and whether you are paying for a throwaway demo or the first version of a system you will keep. This guide breaks the cost down honestly, using a real fixed-price ladder rather than a vague day rate, so you can judge any quote before you commit.

Crux Digits publishes fixed prices for exactly this reason: an AI Audit & Strategy at €2,500, a Proof of Concept at €20,000, and a production launch from €50,000 (all excl. VAT), with work outside the ladder at roughly €150 per hour. Those figures are a transparent benchmark, not a quote for your situation — but they make the underlying logic of AI project cost visible, which most pricing pages hide.

The three steps, and what each one actually buys

A sensible AI project moves in stages, and each stage de-risks the next. Spending money out of order — jumping to a build before you have proven the use case — is the most common way Dutch companies waste a pilot budget.

The €2,500 audit: deciding what is worth building

The audit is deliberately cheap relative to everything downstream, because its job is to stop you spending €50,000 on the wrong thing. A real audit produces a ranked shortlist of use cases scored on business value against build difficulty, a realistic ROI estimate per case, a data-readiness check, and an EU AI Act risk class for each idea. You walk away knowing which single use case to pilot first and why — or, just as valuable, that none is ready yet. Audit prices vary widely across the market, and the spread is purely depth: a cheap "audit" is often a generic slide deck, while a serious one inspects your real systems and produces evidence you can act on.

The €20,000 proof of concept: turning a claim into evidence

This is the pilot itself, and where "what does an AI pilot cost" stops being abstract. A proof of concept takes the top use case from the audit and builds a working version — enough to produce a measurable result on your own data. What a pilot costs maps to one variable above all: whether it runs on a clean public dataset (cheaper, far less convincing) or on your own messy production data (more work, genuinely credible). A €20,000 PoC that touches your real data, returns a hard accuracy or time-saving figure, and is built so the code can graduate into production is worth more than a flashier demo that has to be rebuilt from scratch later.

Production launch from €50,000: making it reliable

Production is where the pilot becomes something your team depends on daily. The jump from €20,000 to €50,000-plus is not markup — it pays for integration into your CRM, ERP or document systems, guardrails and access controls, the monitoring that catches a drifting model, and the documentation that lets you own and maintain the result. A proof of concept proves the idea works once, on a good day; production makes it work every day, under load, for people who are not data scientists. Scope drives the number from here, which is why a fixed audit comes first.

One point worth being clear about: a pilot does not buy scale or a guarantee. A €20,000 PoC answers one question — does this work well enough on our data to justify a build? Answering "no" cheaply is a successful pilot, not a failed one. The expensive version of that "no" is discovering it after a production project, which is exactly what the audit-then-pilot sequence prevents.

What actually drives the cost up or down

Two companies asking the same question can get very different answers, because the variables below move the price far more than any hourly rate does. Understanding them lets you read a quote properly.

• Data readiness. Clean, labelled, accessible data is the cheapest possible starting point. If yours is scattered across spreadsheets, legacy databases and PDFs, a real share of the project becomes data engineering before a single model is trained. Honest consultants quote that separately rather than bury it in a vague "AI build" line.
• Use case and integration depth. A forecasting model reading one clean sales table is far cheaper than a computer-vision system running on a factory line in real time, or a generative-AI assistant wired into several internal systems at once. The harder the integration, the higher the bill — and the more a pilot is worth doing first.
• Compliance. For Dutch and EU buyers, the EU AI Act and the GDPR (AVG) are line items now, not afterthoughts. A high-risk application — anything touching health, recruitment, credit or biometrics — carries documentation, transparency and human-oversight obligations. Designing for this from day one is far cheaper than bolting it on after launch, which sometimes forces a rebuild.
• Reusability. The biggest hidden driver of total cost is whether the pilot's code survives into production. A PoC built as a dead-end demo means paying twice; one built to graduate keeps the €20,000 working.

Build versus buy: the question to ask before you pilot anything

Not every problem deserves a custom AI build. Before spending €20,000 proving a bespoke solution, ask whether an off-the-shelf tool already does the job well enough. The honest split looks like this:

• Buy when the problem is generic — transcription, standard document OCR, a basic support chatbot, common analytics. A mature SaaS product is almost always cheaper and faster than building your own, and a consultant who tells you so is worth keeping.
• Build when the value lives in something only you have: proprietary data, a workflow specific to your sector, or an integration no vendor supports. This is where a custom pilot earns its cost, because the result is an asset you own rather than rent.

A good audit answers build-versus-buy explicitly, per use case, and is not afraid to recommend buying — the goal is your return, not a bigger project. If you are still weighing whether to engage a consultancy at all, our guides on AI consulting in the Netherlands and the best AI consultants in the Netherlands cover what to look for.

Hourly rate versus fixed price: reading the real signal

You will see two pricing models for AI work, and they put the risk in very different places. With an open day rate of around €150 per hour and no fixed scope, every hour of debugging and every meeting bills back to you — so the consultant has no financial reason to be fast. A fixed price flips that incentive: overruns come out of their margin, not your budget, so they scope tightly and deliver efficiently.

Day rates are not inherently wrong. For genuinely open-ended work — architecture reviews, mentoring an internal team, staff augmentation you direct day to day — an hourly rate is fair, because nobody can fix the price of work nobody can yet define. The warning sign is a consultant who insists on an open day rate for something that clearly could be fixed-scoped, such as an audit or a bounded pilot. That choice tells you who is carrying the risk. A fuller treatment of rates and ranges lives in our AI consultancy pricing in the Netherlands guide.

How to avoid wasting a pilot

A wasted pilot is rarely about the technology failing. Far more often, the money goes nowhere because the project was set up to fail before any code was written. The avoidable mistakes are consistent:

• No success metric agreed up front. If you cannot say in advance what number makes the pilot a success — a target accuracy, hours saved, error rate cut — you cannot judge the result, and it stalls in "interesting, but…" limbo. Define it first.
• Piloting on fake data. A demo on a clean public dataset proves the technology exists; it proves nothing about your business. Insist the pilot runs on your own data, however messy — that is the only evidence that transfers to production.
• Throwaway code. A one-off demo means paying again to rebuild it for production. Ask up front whether the PoC is designed to graduate.
• No owner inside the business. A pilot with no internal sponsor to act on the result quietly dies after the final presentation. Name the person who will carry it forward before you begin.
• Skipping the audit. Jumping straight to a €20,000 build without a €2,500 audit is how companies pilot the wrong use case at full price. The audit is cheap insurance.

The total cost most quotes leave out

The build price is rarely the whole story. Before comparing two figures, ask what each includes beyond the initial delivery, because the cheaper headline often carries the heavier running cost. An external model called through an API has an ongoing per-request cost that scales with usage; a smaller model you host yourself costs more to build but less to run. Models also drift as the world moves away from their training data, so production systems need monitoring and periodic retraining — a quote that ignores this is quoting a prototype, not a product. And the most expensive long-term outcome is lock-in: paying rent forever on something you can never take in-house. A slightly higher fixed build price that leaves you owning the code, models and documentation usually beats a cheap prototype that becomes a perpetual subscription.

Turning these ranges into a real number

Every figure here is a benchmark, not a quote — the only way to know what your project costs is to scope your use case, data and compliance needs. That is what the €2,500 audit is for: a bounded first step that tells you what the build will cost before you commit, and which often reveals that the cheapest answer is to buy a tool or pilot a smaller idea than you imagined. Crux Digits is a boutique AI consultancy in Nieuwegein, province of Utrecht, founded in 2022, bilingual in English and Dutch, with thirteen delivered case studies. See the full ladder on the pricing page — the honest first move is always the audit, never the build.