Private AI: definition, distinctions and when it pays off
Private AI refers to AI systems whose models, data and logs run in an environment the company controls itself: its own data centre, a German hosting provider or a dedicated environment. It differs from cloud AI in three points: a known location, the company's own access control and verifiable evidence.
What private AI means
Private AI is not a product. It is an answer to the question of where your data is processed. The term now appears in almost every vendor presentation, yet it often means different things. Some vendors mean a dedicated account in a public cloud, others a server in the basement, and still others merely a promise not to use inputs for training.
We use a clear definition: private AI refers to AI systems whose models, data and logs run in an environment the company controls itself. That can be your own data center, a server at a German hosting provider, or a dedicated environment that no third party can access.
AI in your own data center is therefore the strictest form of private AI, though not the only one. What matters is not who owns the building, but who decides over the model, the data and access, and who can prove it.
Three characteristics: location, access, evidence
Three characteristics distinguish private AI from cloud AI. If a solution fails to meet any one of them, it is not private in the strict sense.
Location. The model runs on hardware whose location and operator you know. You know which data center houses the GPUs, who has physical access, and which jurisdiction the operator is subject to.
Access. You decide who can see inputs and outputs, including with respect to the vendor. This includes a role and permission concept for your team, encryption in transit and at rest, and a binding commitment that inputs will not be used to train third party models.
Evidence. Every use is logged, and you have access to these logs. Data protection, IT security and the works council can check what the system does instead of having to rely on promises. Sovereignty that cannot be verified is marketing. We describe the evidence we provide for our systems under Security.
Private AI, cloud AI and on premises compared
| Characteristic | Private AI | Cloud AI | On premises |
|---|---|---|---|
| Core idea | Control over model, data and logs | Using a model as a service via an API | Running hardware and software in your own building |
| Where processing takes place | Your own data center, a German hosting provider or a dedicated environment | The vendor's data centers, location often selectable | Your own data center |
| Who controls access | Your company | The vendor, under its contract terms | Your company |
| Jurisdiction | Known and selectable | That of the vendor and its parent company | That of your company |
| Evidence and logs | Fully accessible | Limited, depending on the vendor | Complete, if set up |
| Operational effort | Internally or with an operations partner | With the vendor | Internally |
| Cost logic | Mostly fixed, cost effective at high volume | Variable, per token | Mostly fixed |
| Typical use | Confidential and regulated data, high volume | Experiments, public data, low volume | Strict data retention requirements |
The three terms are often mixed up. The following table puts them in context.
The difference between private AI and on premises is important. On premises describes a location; private AI describes a principle of control. An on premises installation that continuously sends usage data to a manufacturer is not private. A dedicated environment at a German hosting provider with complete logging and no third party access, on the other hand, can be.
The misconception about European server locations
A common misconception is that data is protected as long as the servers are in Europe. That is not true. The US CLOUD Act applies regardless of server location. A European data center owned by a US corporation is therefore not automatically private, because US authorities can demand disclosure under certain conditions.
A 2025 hearing before the French Senate showed how serious this question is. A representative of Microsoft France was asked whether he could guarantee that data would remain out of reach of US authorities. His answer was: "Non, je ne peux pas le garantir", meaning: No, I cannot guarantee that.
Companies feel this tension. According to the Bitkom Cloud Report 2026, 85% of the companies surveyed feel too dependent on US clouds, yet 71% use them anyway. 43% see no equivalent European alternative, and only 12% accept a price premium of 10 to 20% for such an alternative. The market thus shows a double gap: there is a lack of offerings that are equivalent, and they must not be noticeably more expensive.
When private AI pays off and when it does not
Private AI is not an end in itself. It pays off in three situations:
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Sensitive data: You process personal, confidential or regulated data, such as client files, personnel records, engineering data or health information.
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High volume: Usage is so extensive that pricing per token becomes expensive. Our cost comparison calculator shows where the threshold lies for your company.
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Your own terminology: You want to adapt models to your terms, documents and processes and keep these adaptations as your own knowledge.
For first experiments with public data and low volume, on the other hand, the cloud is faster and cheaper. Many companies do well with a hybrid approach: confidential information stays in their own network, while noncritical tasks run through an external API when needed. A gateway between application and model controls which request goes where.
It is important to define the data classes in advance. Only once it is clear which information must never leave your own network can the routing be configured properly and justified to data protection and the works council.
How to get started
Private AI does not mean doing everything yourself. It means knowing where everything happens. We build and operate private AI systems so that your IT stays in control without having to carry every task itself.
The first question is where the system will run. For some companies, their own data center is a given, because data retention requirements demand it or because capacity already exists. Others start with a dedicated environment at a German hosting provider and move the hardware into their own building later. Both paths lead to private AI, as long as location, access and evidence are clarified.
Getting started begins with a clearly scoped use case. We have a first internal pilot running in 72 hours, and it shows with your real data whether data quality and interfaces hold up. For production use we plan 4 to 10 weeks, depending on data, interfaces and approvals. Our work does not end there: with us, monitoring, updates and retraining are part of operations. You can find out more under AI in your own data center.
