Sovereign AI, in the cloud or in your company
Start with control over data, models and access. Then choose the deployment that fits your company.
Editorial analysis and illustrative technical scenarios. They do not define the features or integrations included in a UTOMAI subscription. See Private AI On-prem pre-launch information.
AI-generated editorial illustration. Not a product photograph or a customer case study.
A company asking for sovereign AI usually wants practical answers: where documents are stored, where the model processes a request, who can access the workspace and what happens when a provider changes. A platform should make these choices understandable before the team starts using it.
One platform, different deployment choices
UTOMAI provides private AI in two modes: dedicated cloud servers or a dedicated machine at the customer site. Both combine a shared workspace, configured model execution and agreed management. The choice determines the infrastructure and operating responsibilities.
The planned Cloud offer is based on hosting in Italy, with European locations on request. Storage, inference, backups and operating access must each have a confirmed location and scope. An Italian application server does not establish where an external model processes its inputs.
Describe the boundary
Open-weight models are the intended foundation. External frontier models can be requested from European or non-European providers, with the destination and terms agreed separately. Local inference can avoid sending inputs to external LLM providers for that processing; integrations and managed services may still create other data flows.
A useful sovereignty review records model choices, access, retention, external services and operating responsibilities. No single deployment automatically provides complete isolation or regulatory compliance. The useful outcome is a platform whose boundaries the company can explain and choose.
