Sensitive data
Prompts include confidential material.
Contracts, code, financial data, patient data, legal documents and customer records are better processed in a controlled environment.
Cloud AI APIs are fast to start. Customer-premises AI appliances are built for enterprises that need direct control over inference, identity, operational records, model routing and compliance-sensitive workloads.
The right choice depends on whether convenience or direct infrastructure control matters more for the workload.
| Cloud AI | On-Prem AI with LLM Machines | |
|---|---|---|
| Data location | Requests are processed in provider infrastructure | Nothing leaves your infrastructure |
| Model control | Models and revisions follow the provider catalogue | You approve models, revisions, routes and access policies |
| Operational ownership | Provider-operated service | Customer-controlled appliance with managed operations |
| Update control | Provider release schedule | Approved maintenance windows and controlled rollback |
| Application portability | Provider-specific APIs, models and policies | Customer-owned applications and a documented API contract |
| Product source rights | Defined by the provider service | First-party source available for internal use; upstream licences preserved |
| Compliance | Requires provider review and compensating controls | Designed around EU AI Act, GDPR, NIS2 and Data Act needs |
| Best fit | Low-risk experiments and public data | Sensitive data, regulated teams and repeat workloads |
On-prem AI becomes more compelling as sensitivity, operational control and integration depth increase.
Contracts, code, financial data, patient data, legal documents and customer records are better processed in a controlled environment.
Chat Completions is the documented API protocol your favourite apps and harnesses use to access approved local models while your team retains the application experience.
Metadata-only audit, user access, model policy and operational controls are easier to defend when the platform is inside your perimeter.
We size an appliance around your model needs, capacity, data sensitivity and deployment constraints.