The term on-premises (abbreviated: on-prem) refers to operating IT systems on an organization’s own infrastructure, as opposed to cloud-based services. In the AI context, on-premises AI means that the model, data and computing power are entirely under the control of the operating organization.

For organizations with sensitive data, regulatory requirements or compliance concerns, on-premises AI is the only option that ensures no data leaves the organization. This particularly applies to: personal data ( Art. 44 ff. — transfer to third countries), trade secrets and intellectual property, research and development data, clinical and patient-related data, and regulated financial data ().

A key risk with cloud AI that is structurally eliminated with on-premises AI is the US CLOUD Act: US authorities can require US companies under 18 U.S.C. § 2713 to hand over data — regardless of which country the servers are located in. This risk is structurally absent with a German manufacturer without a US corporate entity.

Silent AI is an on-premises AI appliance: hardware, model, vector database and connectors are combined in a tested unit and run entirely within the customer’s local network.

Frequently asked questions

For general tasks (image and video analysis, translations, coding assistance), the largest cloud LLMs are often more capable because they were trained with more computing power. For accessing internal enterprise knowledge, on-premises AI with RAG architecture is the better solution: it knows the organization's own documents, respects access rights and never leaves the network. Silent AI does not replace cloud AI — it complements it for the secure handling of internal, sensitive data.
AI appliances like Silent AI significantly reduce operational effort: hardware, model, connectors and vector database come as a tested unit. Deployment takes days, not months. CARE maintenance contracts ensure updates and support for 1, 3 or 5 years at predictable terms — without the usage-based costs of cloud services.