What drives the cost of an AI project for an SME: cost items, levers and public benchmarks

This guide is for owners and managers of SMEs who have a quote in front of them, or are about to ask for one, and want to understand what it is made of. It gives no rates, not even mine: a price is set on a scope, not in an article. It gives what you need to read a price: the cost items, what pushes them up or down, and public benchmarks, dated and sourced. The figures come from French sources.

Cost is a brake, but not the first one

Insee, the French national statistics office, puts the question each year to businesses that use AI and to those that do not.

One business in three cites cost, one in two cites expertise. The two are linked: without knowing what a project is made of, you cannot judge its price, and any figure looks too high. The France Num 2025 barometer adds a benchmark: three in four French small businesses and SMEs set aside a specific budget for their digital projects, and 26% use AI solutions, twice as many as in 2024.

Three kinds of spending that do not compare

The word “AI” covers three purchases of a different nature. Putting them on the same line is the first source of misunderstanding.

The first two are running expenses. The third is an investment, and is judged as one: by what it returns, not by comparison with a subscription. That is the subject of “It’s expensive”: a category problem, not a pricing problem. For Copilot’s public prices and what it covers, see Copilot or custom AI.

The rest of this guide is about the third line.

The six cost items of a custom project

The first four are paid once. The last two come back every month. A quote that shows only the first four is not complete.

The second item is the one most often forgotten. If your documents are in binders or your data in files nobody keeps up to date, the first job is not an AI job, and it is better to know before signing: see Your problem is not (yet) an AI problem.

Model usage: the item people overestimate

AI is often assumed to be “expensive to run”. Public prices say otherwise. Recorded on 1 October 2026, in US dollars, excluding tax, default region for Azure.

A simple calculation gives the order of magnitude. An SME that processes 2,000 two-page documents a month uses 4,000 pages. With custom extraction, that comes to 120 dollars a month. Microsoft states that these prices are estimates and that they vary with the contract and the region.

Three caveats. An agent that chains several calls per case uses more than a plain extraction. Dedicated or on-premises hosting replaces this usage cost with a higher fixed cost. And model prices change fast: this table will be revised. Even so, at an SME’s volumes, model usage is rarely what weighs in the total.

People’s time: the real item

What weighs is the work: understanding the process, connecting the systems, handling the cases nobody had foreseen. The freelance market gives a public benchmark. On Malt, average day rates recorded on 1 October 2026, in France, for freelancers active over the last three months:

These figures are platform averages, for freelancers. They do not describe the rates of a consultancy or an IT services company. And a day rate does not tell you the price of a project: the number of days is missing, and above all who carries the risk if that number is exceeded.

Neither mode is better in absolute terms. Time and materials suits exploration. A fixed fee suits a result that can be described. A fixed fee without a written scope is not one.

Seven levers that move a quote

The first and the fourth weigh most. Each extra system adds a connection, access rights and error cases. And taking a workload from “often right” to “reliable” requires more work than the first version: that is the difference between a demonstration and a system in production, covered in What keeps a document pipeline alive in production.

The most effective lever for bringing a price down is not negotiation. It is scope: one process, one type of document, one system, then widen.

The cost you are not invoiced for

A project also takes up your teams: interviews during scoping, examples to provide, cases to validate while the system is tuned, training. That time appears on no quote and it conditions the result. Ask for it in writing. For an order of magnitude on the diagnostic phase, see A 30-day AI diagnostic, one day of your time.

What to compare against: the cost of the process today

A price only makes sense against what the process already costs. The calculation takes four lines.

1. The volume: how many cases, documents or requests per month.
2. The time spent per unit, measured rather than estimated.
3. The fully loaded hourly cost of the people who do it.
4. What errors and delays cost: credit notes, penalties, chasing, cash tied up.

An example, with made-up figures. 400 cases a month, 12 minutes each, at 35 euros per fully loaded hour: 80 hours, or 2,800 euros a month, before errors and delays. It is against that figure, measured in your business, that a quote is read. The fourth point is often the heaviest and the least looked at: the cost of a process lies in the time that elapses, not in the time worked on it.

Public funding in France

Bpifrance, the French public investment bank, offers the Diag Data IA, funded by France 2030 under the Osez l’IA plan: eight days of work by an approved expert, spread over three months at most, for an assessment, the identification of use cases and a roadmap. It is open to independent SMEs and mid-caps with 10 to 2,000 full-time equivalent staff, at least one million euros in turnover and more than one year in existence. The subsidy is 40% for eligible businesses. The current amount and conditions are on the Bpifrance page listed in the sources.

Your region and your chamber of commerce may offer other schemes. They change often: check them at source before building them into a budget.

Eight questions to judge a quote

1. What result is promised, and how will it be measured? A figure, a method, a date.
2. What is included, and what is not? Getting the data into shape, connection to your tools, training.
3. How much will the system cost each month once in service? Hosting, model usage, monitoring, maintenance.
4. Who carries the overrun? On a fixed fee, the supplier. On time and materials, you.
5. What happens when the AI gets it wrong? A threshold, human validation, a named person.
6. Where is your data hosted, and is it used to train a model?
7. Who owns what is delivered? The code, the rules, the training data. And what does it take to change supplier?
8. How much of your teams’ time does the project need? Per week, and at which stages.

A supplier who answers all eight in writing gives you something to compare. One who answers only the first gives you a price.

Going further

Questions 1 and 3 assume a baseline figure that most SMEs do not have. That is the purpose of the 30-day AI diagnostic: your processes mapped, the gain costed in euros per priority case, and a 90-day roadmap, including when the right answer is a feature of the tools you already have. It is done on a fixed fee, set in writing before the work starts.