What does AI consultancy cost? The short answer
For most Dutch and Benelux businesses, AI consultancy costs fall into three clear bands. A focused AI audit and strategy engagement typically runs from around €2,500, a proof of concept that proves the value on your own data sits around €20,000, and a full production launch starts from roughly €50,000 and scales with scope. These are the real, published tiers we work with at Crux Digits, and they are a useful anchor for any AI project cost conversation. The honest caveat: these are typical, indicative figures, not a quote. The final number always depends on your use case, your data, and how deeply the solution has to integrate with your existing systems.
If you only read one paragraph, read that one. The rest of this guide explains why those numbers look the way they do, the different ways AI work is priced, and the practical levers you can pull to keep spend under control while still getting measurable return.
Why there is no single price for AI
The phrase "AI consultancy" covers an enormous range of work. At one end, it might mean a two-week strategy review that tells you where artificial intelligence will actually pay off in your business. At the other end, it can mean a fully built, integrated, monitored product that runs every day inside your operations. Those are wildly different deliverables, so it is no surprise they carry wildly different price tags.
The good news is that AI consultancy prices are far more predictable than most buyers expect, once you know which model you are buying. Vague quotes usually come from vague scopes. Tighten the scope and the price becomes knowable.
The three main AI consultancy pricing models
Almost all AI work is sold in one of three ways. Which one fits depends on how clearly the work is defined and how much risk each side wants to carry.
- Fixed-price audit or strategy. Best when you have a clear, scoped deliverable — an opportunity assessment, a prioritised shortlist of use cases, a feasibility and ROI estimate, and a roadmap you keep. Because the output is well defined, this can be quoted as a fixed fee. Our AI Audit & Strategy is the typical entry point here.
- Fixed-scope proof of concept (PoC) or pilot. Best when one high-value use case is already clear and you want to prove it works on your real data before committing to a full build. Success metrics are agreed up front and tested against a manual baseline, ending in a clear go / no-go.
- Production build, then time and materials or a retainer. Best for the full integrated system and the ongoing iteration, support and monitoring that follow. The build itself is usually scoped and priced as a project; the support that comes after is billed monthly or by day rate.
A quick note on hourly rates, because buyers always ask. Specialist AI engineering day rates in the Netherlands and wider Benelux generally land in the multiple-hundreds-of-euros-per-day region, with senior data science and ML engineering at the higher end. We deliberately prefer fixed scopes over open-ended hourly billing wherever possible, because a fixed price aligns incentives and removes the budget anxiety of an open meter.
What actually drives AI implementation costs
If you want to predict your AI implementation costs, look at these five drivers. They explain almost all of the variation between a €2,500 engagement and a six-figure build.
- Scope. One narrow, well-chosen use case is dramatically cheaper than "transform the whole company with AI". Breadth is the single biggest cost multiplier.
- Data readiness. Clean, accessible, well-labelled data lowers cost. Messy, scattered or missing data adds preparation work — and data prep is frequently the quiet majority of effort in a real project. If you are weighing this up, our explainer on what AI terms actually mean is a useful primer; see the AI glossary.
- Integration. A standalone demo is cheap. Connecting AI to your CRM, ERP, ticketing system or internal APIs so it works in the flow of real work is where much of the engineering effort sits. This is the heart of AI implementation.
- Compliance and risk. The EU AI Act and GDPR add rigour for higher-risk use cases. That rigour costs a little more up front but protects you from far larger costs later, and it adds genuine value for regulated sectors.
- Build versus ongoing support. A one-off build costs less up front than a continuously maintained, monitored, improved system. Decide early whether you are buying a project or a long-term capability.
The architecture you choose changes the price too
Two solutions that look identical to a user can cost very differently to build and run. A simple automation that routes documents is not the same as an autonomous agent that reasons over multiple steps. If you are unsure which you need, our comparison of an AI agent versus a chatbot explains the trade-offs in plain language, and it has real cost implications: agents are more capable but carry more build, testing and running cost than a scripted assistant.
The same is true for the automation layer underneath. The tooling you standardise on affects both build speed and ongoing licence cost. Our breakdown of n8n versus Make versus Zapier walks through where each makes financial sense, which matters because the wrong platform choice can quietly inflate your monthly bill for years.
How to keep your AI project budget under control
The single most effective way to control AI spend is to start small and prove value before you scale. Instead of committing to a large programme on faith, scope one high-value use case, build a proof of concept, measure the return, and only then invest in the full build. This is exactly why our second conversation comes with a working prototype rather than a sixty-page document — you see real value, and real cost, before the big commitment.
A few more practical levers:
- Quantify the ROI first. Every roadmap should include a projected return per use case, so the spend is justified by a number, not a hope. AI that does not pay back is not worth doing.
- Sequence the work. Audit, then prove, then build. Each step de-risks the next and avoids over-investing in an idea before it has earned the budget.
- Reuse what you have. Existing clean data, existing APIs and existing platforms all reduce cost. You rarely need to build everything from scratch.
- Look at real examples. Seeing how comparable projects were scoped helps you budget realistically; browse our case studies for illustrative engagements.
What this looks like at Crux Digits
We start with a free consultation. If you need direction, a fixed-price audit gives you a prioritised, costed roadmap. If the use case is already clear, we go straight to a scoped proof of concept on your own data. When it works, we build it for production and integrate it into your systems. Every step has a fixed scope and a price agreed up front, every roadmap includes projected ROI, and you always have a named expert rather than a ticket queue. You can see the full, published numbers on our pricing page.
That is the whole philosophy: no jargon, fixed scope, measurable return. AI should pay for itself, and you should be able to see how before you spend a euro.
Get a real number for your project
Indicative ranges are useful for planning, but the only number that matters is the one for your specific use case. Tell us what you are trying to achieve and we will give you a fixed, no-obligation quote after a short scoping call. Start on our pricing page or book a free consultation, and you will walk away knowing exactly what your AI project would cost — and what it would return.
Frequently asked questions
What does AI consultancy cost in the Netherlands?
It depends on scope, but as an indicative guide a focused AI audit and strategy engagement typically starts around €2,500, a proof of concept on your own data around €20,000, and a full production launch from roughly €50,000, scaling with complexity. These are typical published ranges, not a guarantee — the exact figure comes from a short scoping call and a fixed quote.
What drives the price of an AI implementation?
Five factors drive almost all of it: the scope (one use case versus company-wide change), how ready and clean your data is, how deeply the solution must integrate with your existing systems, compliance and risk requirements such as the EU AI Act and GDPR, and whether you need a one-off build or an ongoing, maintained system.
How can we keep AI consultancy costs down?
Start small and prove value before you scale. Scope one high-value use case, build a proof of concept, measure the return, and only then invest in a full build. Quantify the projected ROI up front, sequence the work as audit then prove then build, and reuse existing clean data, APIs and platforms wherever possible.
Is a fixed price better than an hourly rate for AI work?
For well-defined work, a fixed price is usually better for the buyer. It aligns incentives, removes the budget anxiety of an open meter, and forces a clear scope. Time and materials or a retainer makes more sense for genuinely open-ended, evolving work such as ongoing support and iteration after a system is live.
Does Crux Digits work with clients across the Benelux and Europe?
Yes. Crux Digits is based in Utrecht, the Netherlands, and serves clients across the Netherlands, the wider Benelux and Europe. Every engagement is GDPR-aware and EU AI Act-conscious, with fixed scope, fixed price and a named expert as the standard way of working.