How Much Does AI Integration Cost? Cost Layers and Real Ranges

"How much does AI integration cost?" sounds like a question about a price list, but it's a question about architecture. The gap between wiring Claude into a single process and building an assistant that reads your entire company knowledge base is an order of magnitude — both in the build price and in the monthly bill. Below we break the cost of AI integration into layers, show what actually moves the price, and say plainly when it isn't worth doing at all. No magic ROI figures from a sales deck.

What "AI integration with a system" is (and what it isn't)

Let's clear the term out of the way first, because "AI integration" today means everything and nothing.

It's not a chatbot bolted onto a website that answers "what are your opening hours." It's also not a plugin generating product descriptions off to the side. Those things can be useful, but they aren't integration with a system.

AI integration with a system means wiring a model (Claude, GPT) into a specific workflow, or into the product itself — where until now a human or a rigid rule did the work. In practice it's a handful of patterns we combine as needed:

• RAG on company documents — the model answers from your data: procedures, contracts, tickets, documentation, not generic knowledge from the internet. The documents go into a vector database, and the model only gets the fragments relevant to the question into its context.
• Embeddings and semantic search — instead of matching keywords, the system understands the meaning of a query. "Complaint about a damaged shipment" will find tickets described in completely different words.
• Function calling — the model doesn't just talk, it takes actions: creates a record in the CRM, issues an invoice, runs a database search, calls an external API. That's the difference between "an assistant that suggests" and "an assistant that acts."
• Automated data processing — classifying emails, extracting data from invoices and PDFs, summarising long documents, tagging tickets. The things someone does by hand a few hundred times a day.
• In-panel assist — a contextual helper built into your application that sees a specific user's data and works inside their process, not in a detached chat window.

The common thread: the AI sits inside the system and has access to the data and the actions. That is the valuable — and more expensive — part. Not the model itself.

What the cost is made of: four layers

The cost of an integration isn't a single number, because it's made of four independent layers. Two are one-time, two recur every month.

The most important distinction to remember:

• Build cost (one-time) — the integration layer plus the data pipeline. For us, a simple, well-defined integration starts at around €1,900 net. That's, say, a single process: classifying incoming tickets, or a RAG assistant over one tidy set of documents.
• Running cost (monthly) — the model API plus maintenance. You pay per token on every request, so the bill grows with usage, not with the passing of time. There's no single number here — it depends on volume and how economically the integration was built.

Why is this distinction critical? Because you can build an integration cheaply and then overpay to run it: the wrong model for a simple task, no caching, dumping whole documents into every request instead of just the relevant fragments. And the other way around — a solid pipeline can cut the monthly API bill by an order of magnitude. That's why we estimate the running cost before writing a line of code.

What actually drives the price

Pricing an integration isn't a question of "how many screens," but "how much uncertainty has to be tamed." The five factors that move the budget the most:

• Number of data sources. One clean source (e.g. a knowledge base in a single format) is a simple pipeline. Five sources — Confluence, a mailbox, PDFs, a SQL database and a legacy system that exports to CSV — are five separate integrations, each with its own quirks.
• Data quality. The most commonly underestimated item. If the documents are inconsistent, duplicated, unstructured and full of scans with no text layer, most of the budget goes on tidying up the data before the model ever sees it. "Garbage in, garbage out" is merciless in AI.
• Required accuracy. An assistant that suggests things to a human can be wrong sometimes — the human corrects it. A system that makes the decision itself (books an entry, rejects an application, answers a customer unsupervised) needs a layer of validation, tests and fallbacks. That can double the cost of the integration layer.
• On-premise vs cloud. The cheapest and fastest option is calling a cloud model API. If the data can't leave your infrastructure, we move into private deployments or self-hosted models — a different order of cost and competence.
• Privacy and compliance (GDPR). Personal data, sensitive data, trade secrets — these require anonymisation, data processing agreements (DPAs), control over what reaches the model, and a deliberate choice of provider. It's not an "add-on," it's a requirement you have to price in from the start.

An example return on investment

We won't give you an invented "300% ROI in three months," because any such number from the internet is guesswork. Instead — an estimated mechanism you can plug your own numbers into.

Picture a support team that, for example, spends a few hours a day on the manual classification and first-draft reply to repetitive tickets. An integration that automatically tags the ticket, attaches context from the knowledge base and proposes a ready draft reply doesn't eliminate the human — it shortens the handling time of a single ticket. If you estimate a drop from a few minutes per ticket to a few dozen seconds, then at sufficient volume the time saved, counted in work hours per week, pays back the build cost over a period you can easily calculate on your own data.

The point is this: ROI from AI integration comes from volume and repetition, not from the magic of the model. Count how many times a day you perform a given step, how long it takes and what an hour of work costs — and only then set that against the monthly running cost. If the numbers don't add up, no technology will fix that.

When it's worth it, and when to skip it

AI integration isn't the answer to everything. When it is worth it:

• the process is frequent and repetitive — dozens or hundreds of times a day,
• it's based on text and data: classification, extraction, search, editing,
• you have the volume that turns a small saving on one ticket into real hours,
• the effect can be measured: time, number of errors, cost of handling.

When it's better to skip it — or start with something simpler:

• the process is rare (a few times a month) — the build and maintenance cost will never pay off,
• the rules change every week — you'll maintain the integration more often than you use it,
• underneath lies a mess in the data or an unclear process — AI won't tidy that up, it only makes it visible; fix the process first,
• you need 100%, deterministic correctness where a plain rule does it more cheaply and more reliably.

Sometimes the cheapest "AI integration" is a well-written script with no model at all. A good software house will tell you that plainly, instead of pushing a model where it doesn't belong.

What it costs with us

At SEVENEDGE an AI integration starts at €1,900 net for the first, well-defined process, and we estimate the running cost (API) before we write anything — so you know the monthly bill, not just the invoice for the build. We lock scope the same way as on any project: a concrete process, a measurable effect, a fixed price for the build, and the full source code in your repository.

If you want to see how this looks on concrete processes, take a look at the AI automation and integration page, and for ballpark ranges — the pricing page. And once you have a process in mind and want to know whether it's worth it at all — get in touch. We'll tell you straight whether it's a job for AI or for a plain script.