Three reports from this year show why compute power is becoming a strategic question for European companies. According to Siemens board member Cedrik Neike, European data centers run 20 to 30 percent above US levels in operating costs, while companies, in his estimate, would pay only 5 to 10 percent more for it.
At the same time, top-tier US AI is getting cheaper. In September, Anthropic cut the token price of Claude Opus 5.5 by 20 percent against its predecessor. The sovereign European alternative remains expensive, because Europe is struggling with high electricity prices and tight grids, according to the EU Institute for Security Studies. If sovereign compute costs up to 30 percent more, can that premium be saved somewhere else?
The license fee is usually the smallest problem. What gets expensive is compute, integration, operations and above all wrong answers. We show you how to calculate the true cost of enterprise AI and how SenseDrive keeps it low from the start.
In the budget meeting everything looks manageable: an AI assistant for a few dozen euros per user per month. That fits into any department budget.
Six months later the bill looks different. Cloud costs rise with every query. IT builds interfaces nobody planned for. And in service, an experienced colleague checks every AI answer by hand again, because nobody wants to rely on it.
On top of that comes a development many companies in Europe are only now noticing: anyone who does not want to put their data into a US cloud pays more. The willingness to do so is greater than Neike assumes. In KPMG's Cloud Monitor 2026, 49 percent of the companies surveyed in Germany accept a premium of up to 20 percent for sovereign offerings, and a further 20 percent accept up to 30 percent. Only one company in five would therefore carry the full premium of up to 30 percent.
The gap cannot be negotiated away. It can be narrowed, if you do the arithmetic in the right place.
Anyone who looks only at the price per user sees one of five cost blocks. The other four appear in no quote, but they land in the budget:
Cost per query or per token therefore says little. A cheap answer that is wrong ends up costing you more than an expensive one that is right.
The right metric is cost per resolved case: per service question answered, per complaint closed, per quote completed. Hardly any company asks its AI vendors for this number. Ask for it anyway.
In the market, SenseDrive faces four typical alternatives. Each has an advantage. And each shifts costs to a place that does not appear in the quote.
The general AI assistant is quick to introduce and is paid per seat, for every employee, whether they use it daily or once a month. It supports its statements only in part, checks no contradictions and does not know your equipment knowledge. Questions about a service manual therefore often come back incomplete, and more often still when the assistant reaches the manual only through a connected file share. Running it yourself, including the language model, is rarely possible. The compute therefore runs in the provider's cloud, at the provider's price level. The cost of checking the result is carried by your team.
Enterprise search finds documents reliably, but it delivers a list of hits instead of a source-verified answer. Reading, comparing and deciding which source applies is left to the employee. Whether two sources contradict each other is not checked. So the costs land exactly with the experienced colleagues you wanted to relieve.
The corporate stack with a digital twin can do a great deal: source references, equipment knowledge, your own operation including the language model. It detects contradictions through a central data model. But that data model first has to be built and then maintained permanently. The rollout becomes a major project, and the costs move into integration and operations.
The in-house build looks cheapest, because the building blocks are freely available. The typical mistakes arise in operation. Every query goes to the largest available model, and the model is given more text than necessary. Both drive up compute costs. Contradiction detection, permission logic and quality monitoring have to be developed by your team first and then run permanently.
The common pattern: without the check, your experts pay in rework. With the check, it arrives as a major project or has to be built in-house. SenseDrive combines checking, cited passages and a modular rollout with a pilot of 10 to 14 weeks. Which solution is genuinely cheaper is still often unknown, because the invisible costs are not measured. This classification is based on typical product features; individual vendors may differ.
A look at the knowledge base solutions on the market for industrial processes confirms the pattern. Some bill strictly per user per month and offer cloud operation only. Others charge a monthly flat fee for knowledge access, which makes the actual price per user hard to trace. What integration, operations and errors cost in the end usually stays open.
The real cost risk sits deeper, in the architecture itself. Many of these solutions pass large parts of the knowledge base to the language model with every query. At the start this barely shows. It becomes a problem as soon as a project runs successfully. Every new user brings additional queries, and every new source enlarges the data volume each of those queries works with. The compute costs therefore grow with both at once. The successful rollout becomes the most expensive scenario.
SenseDrive – Your own industrial knowledge platform for source-verified answers, from the shop floor to the office.
SenseDrive measures success in cost per resolved case. Behind that are five deliberate decisions in the architecture:
We will not calculate a saving for you from somebody else's study, because a business case only holds if it rests on your own numbers.
That is why SenseDrive starts with a measurement. In the discovery meeting we select one or two use cases with your specialist team and collect around fifteen of the hardest questions from your department, together with the relevant documents. Before we read in the first source, we record the baseline: how long does a source-verified answer take today? How often does somebody have to ask again?
In the fixed-price proof of concept that follows, we measure the same figures again, against success criteria agreed in advance: time to a source-verified answer, share of source-verified answers, contradictions detected. From the comparison come your business case and a clear go/no-go decision. And if it does not hold, we will tell you so. If it does, the pilot with real users follows, and the step-by-step expansion from the first use case to the knowledge platform for service, sales, quality and engineering.
Do you know the cost per resolved case that your current AI solution causes, or only the price per user?
And which cost block would be the largest at your company: compute, integration or the rework after wrong answers?
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