On-premise AI: On-premise AI explained: requirements, effort, alternatives.
What on-premise AI means today, what it requires in hardware, operation and expertise, and when a private cloud or hybrid setup is the better choice.
In short: On-premise AI means running AI on your own servers. This gives you direct technical control. But it requires hardware, operation and updates – or a partner who takes over operation.
Direct control and your own operating effort.
An AI node – a compute node – needs processing power, storage, network, backup, access control and ongoing maintenance for models and platform.
A Swiss private cloud moves server operation to a provider. Scope and control depend on the contract, architecture and access rights. In hybrid operation, sensitive tasks run locally and others externally.
On-premise, private cloud, hybrid.
| On-premise | Private cloud | Hybrid | |
|---|---|---|---|
| Data control | complete | contractual, dedicated | per data class |
| Hardware | your own | rented | mixed |
| Operation | your own IT or a partner | Partners | both |
| Scaling | buy additional compute nodes | book compute nodes | depends on architecture and contract |
| Initial effort | high | medium | medium |
When on-premise makes sense.
- Regulatory requirements or trade secrets demand it.
- Infrastructure and operational expertise are already in place.
- Operation without dependence on the internet is needed (plant, construction site).
- Otherwise: consider a private cloud; control and effort depend on contract and architecture.
What this article is based on.
- Own project experience at Swiss AI Labs with compute nodes and operational support; no external study
Products, Labs and case studies.
Frequently asked questions: on-premise AI
What hardware do we need?
That depends on the models and the load; we size the setup according to the tasks.
Who does the updates?
Your IT or Swiss AI Labs as part of AI Operations.
Have a specific process assessed.
The AI potential assessment examines the benefits, prerequisites and risks of the process.