Pricing guide
AI application pricing without the hype
The AI in your product is a monthly bill, not just a one-off development cost.
First, let’s be clear about what you are actually buying: almost no company "builds an AI" — it consumes existing models (OpenAI, Anthropic, Google) through APIs and pays for the development that wraps them around its own data and processes. With that settled, the 2026 orientation ranges: integrating an AI capability into an existing application — automatic document summaries, support-ticket classification, extracting data from scanned invoices — costs €3,000–15,000. A dedicated AI feature, such as semantic search across company documents or an assistant that answers from your internal data, runs €10,000–50,000. Products built around AI as the central proposition exceed €50,000. And watch the line below the estimate that many vendors gloss over: inference — the calls to the models — bills monthly, an indicative €50 to over €1,000/month depending on volume. An AI feature crunching thousands of documents daily can cost as much per year in API fees as it cost to build.
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Key takeaways
AI in an existing app: €3,000–15,000
One precise use case grafted onto software you already run: document data extraction, automatic classification, summaries, suggested replies. The best investment-to-result ratio in the whole AI spectrum.
Dedicated AI feature: €10,000–50,000
An assistant over company data or semantic search through document bases — RAG architecture, data preparation and indexing, testing on real cases. Dirty data, not the model, is the main cost driver here.
Complex AI product: €50,000+
AI is the core of the product, not a feature: data pipelines, systematic evaluation of answer quality, continuous tuning. Territory for funded startups or mature companies, not a first software investment.
Inference: €50–1,000+/month
Every call to the model costs money, proportional to the text volume processed. Demand a monthly estimate on your real volume before contracting — the difference between a profitable project and a recurring surprise.
What is hype and what makes money
Makes money, measurably: automatic data extraction from documents — a company manually processing 500 supplier invoices a month, at 3 minutes each, burns 25 hours monthly; a €6,000–8,000 AI-extraction integration cuts that to spot-check verification, with rough payback inside a year. Classifying support requests with suggested answers pays off too, as does semantic search in technical archives where engineers lose hours hunting for precedents. The common denominator: high volume, a repetitive task, and tolerance for a small error rate covered by human review.
Still hype, for now, for a small or mid-sized company: "autonomous AI agents" running your processes end to end without supervision — the technology produces spectacular demos and expensive mistakes in production. The same goes for training your own model from scratch: you have neither the data volumes nor the budget, and API models are almost always superior for your cases. The honest rule before any estimate: if you cannot state in two sentences which concrete decision or operation the AI takes over and how many hours or errors it removes monthly, you do not have an AI project — you have a curiosity. Curiosities get tested with a €3,000–5,000 pilot, not a €50,000 commitment.
Frequently asked questions
Can the AI run on company data without sending it outside?
Yes, in two ways: open models hosted on your own infrastructure — maximum control, but server and administration costs from a few hundred euros per month — or commercial APIs with contractual clauses excluding the use of your data for training, the standard route for most companies. For sensitive data (medical, legal), settle this before a single line of code is written.
What does an AI pilot cost before committing to the big investment?
An indicative €3,000–8,000 for a prototype on a single use case, with your real data, in 4–6 weeks. The honest deliverable of a pilot is a number — the percentage of documents processed correctly, the hours saved per week — on which you decide to scale up or stop without further losses.
Why does the inference bill swing so much from month to month?
Because you pay per volume processed, not a flat subscription: a month with three times the documents means a bill three times higher. It stays under control through a smaller model for the simple tasks, trimming the context you send, and consumption caps built into the application — request them during development, not after the first surprise invoice.
The AI makes mistakes — who is liable for errors in my application?
You are, toward your customers — which is why sound design puts a human in the loop wherever an error is costly: invoice extraction gets spot-check validation, replies to sensitive customers get approved before sending. A serious AI application is sized from the start around the error rate your process tolerates, not around an assumption that the model is infallible.
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Let’s talk about your project
Message us on WhatsApp or send an email — you talk directly to a developer.
office@northdan.com · +40 752 070 247