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. On-prem AI appliances are built for enterprises that need control over data, audit logs, cost, model routing and compliance-sensitive workloads.
The right choice depends on whether convenience or control matters more for the workload.
| Cloud AI | On-Prem AI with LLM Machines | |
|---|---|---|
| Data path | Prompts and files leave your environment | Processing stays inside your perimeter |
| Cost | Per-token and usage-based | Owned hardware plus service retainer |
| Auditability | Depends on provider controls | Logs and controls live in your environment |
| Lock-in | Provider-specific APIs, models and policies | Open-source stack and OpenAI-compatible gateway |
| 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 usage, risk and integration depth increase.
Contracts, code, financial data, patient data, legal documents and customer records are better processed in a controlled environment.
When many teams query AI every day, hardware ownership can beat per-token billing and rate limits.
Audit trails, user access, model routing and retention rules are easier to defend when the platform is inside your perimeter.
We size a 36-month TCO model against your current usage, data sensitivity and deployment constraints.