AI in your own data centre.
AI in your own data centre means language models run on servers that belong to your company or are operated exclusively for you. Your data never leaves the building or the EU. This becomes worthwhile from a regular usage volume upwards, with personal or business-critical data, and anywhere traceability is required.
The problem with billing per request
Most companies start with a public AI service because getting started is easy. But as usage grows, so does the problem. First, costs rise linearly with every single request, without a budget you can reliably plan in advance. Second, data frequently leaves the building in the process, which is a problem for data protection and internal audit wherever the information is personal, confidential or competitively sensitive. Third, many public services lack the traceability an audit or a regulator demands - which model produced which answer, and when. AI in your own data centre solves all three problems at once, because cost, data location and traceability stay under your own company's control.
There is a fourth point, mentioned less often but often heavier in the long run. A company that relies entirely on a single external vendor also inherits that vendor's price changes, availability swings and decisions to retire models, without any influence of its own. Private AI infrastructure moves that dependency back into your own company.
What private AI infrastructure actually covers
Our solution is made up of several building blocks, each explainable on its own and effective together.
The language models run either on servers in your own data centre or in a named zone - a contractually fixed, physically separate environment at a provider in Germany or the EU. Which variant makes sense depends on your existing IT landscape and your regulatory requirements.
We prefer language models with open weights. That means the model itself does not belong to a single vendor who could restrict access at any time - it can be taken with you and kept running on different infrastructure if needed. Which specific model fits your use case best depends on language, subject matter and required speed; we set that choice together with you during the assessment and give a written reason for it, rather than prescribing it without explanation.
We connect the language model to your existing systems - a document archive, an ERP system, a customer database - so it works with real, current data instead of general knowledge alone.
Who may ask which question, and who may see which answer, is governed by roles and permissions that fit into your existing user management.
Every request and every answer is recorded traceably, in a form an internal audit or an external auditor can review.
A system, once set up, needs ongoing maintenance, monitoring and model updates, as described at Operations.
How this connects to our security certifications
Private AI infrastructure is not a promise, it is a verifiable technical and organisational setup. Our ISO 27001 and SOC 2 Type II certifications apply directly to this infrastructure, documented with auditor and date, not just as a general company statement. Anyone who wants to check these certifications before committing can find the detail on the Security page.
A worked example, not a guarantee
Whether owning it yourself pays off depends heavily on actual usage volume. As our own model calculation, with disclosed assumptions and explicitly not a universal guarantee, we look at an example company with medium but regular use of a language model across several departments. Under billing per request, running costs in this example rise in proportion to usage - and so to the system's success - while private infrastructure creates a largely fixed operating cost block that does not rise to the same degree as usage grows. The point at which the model flips over differs sharply from case to case and cannot responsibly be generalised. So run your own figures through the cost comparison calculator instead of relying on someone else's example.
What you keep when you leave
A vendor who effectively locks in your data and your models has no place in private AI infrastructure, precisely because independence from any single vendor is one of the main reasons for choosing this solution. Model weights, configuration and all processed data stay in a format you can take with you if you switch vendors. Exactly how this exit works, what deadlines apply and what it costs, we describe in full at Switching AI provider.
The path from assessment to operations
Building private AI infrastructure with us follows four phases. In the assessment, which takes about a week, we review your existing systems, your data situation and the legal requirements that apply to you. In the pilot, which delivers a first internal test within 72 hours, we show what a specific use case looks like on the new infrastructure. In the rollout, which takes 4 to 10 weeks depending on scope, this becomes a productive system with the access rights and audit trails your company requires. In ongoing operations afterwards, we take over monitoring, model maintenance and cost control, cancellable monthly after a minimum term.
The exact price for your case is only fixed in writing after the assessment, because it depends on data volume, the number of use cases and existing infrastructure. For orientation, the price frames are at Pricing.
Throughout the whole path, a named contact on our side stays responsible for you, so questions never get lost between changing responsibilities. After the rollout, the same person, possibly with an expanded operations team, takes over ongoing support, so no knowledge is lost at the handover between project and operations.
How this differs from a plain cloud installation
Some vendors already call an installation in any cloud environment private AI infrastructure, even when the underlying servers are shared and the cloud operator technically has access to the data. We define the term more narrowly. Private AI infrastructure with us means the servers either belong to you or are reserved exclusively for you, the location is named in the contract - usually Germany or another EU location - and no third party has access to the processed data without your consent. This distinction is usually the decisive point when a data protection officer or an audit reviews the setup.
Which sectors benefit most
Private AI infrastructure is broadly useful across sectors, but it becomes especially urgent wherever personal or business-critical data is processed at scale - healthcare, financial services or public administration, for example. Demand is also growing in manufacturing and logistics, as soon as operating data, design documents or supplier contracts are meant to feed into a language model that must never reach competitors or an external vendor. Typical use cases by sector are at Industries.
Who on your side should be involved
A project to build private AI infrastructure usually involves more than just the IT department. It helps to involve your data protection officer early, because they can assess data processing from the start rather than being asked only after the rollout. It also helps to involve the business unit that will use the use case later, because they know the exceptions and edge cases a purely technical team often misses. We moderate this exchange during the assessment, so the result is a system that holds up both technically and organisationally.