On-Premise AI · · 5 min read
On-premise AI becomes necessary when data must not leave the organisation. Decision criteria include data classification, sector regulation, closed-network requirements and existing infrastructure capacity.
On-premise AI is chosen when data must not leave the organisation. This need appears frequently in operations handling large volumes of personal data.
Decision criteria include data classification, sector regulation, closed-network requirements and existing infrastructure capacity.
Some organisations prefer a hybrid model: sensitive data is processed in-house while general scenarios run in the cloud.
Frequently asked questions
- When is on-premise AI needed?
- On-premise AI is needed when data must not leave the organisation. This need frequently arises in operations handling large volumes of personal data.
- What criteria guide the choice of on-premise AI?
- Decision criteria include data classification, sector regulation, closed-network requirements and existing infrastructure capacity.
- How does a hybrid AI model handle sensitive data?
- In a hybrid model, sensitive data is processed in-house while general scenarios run in the cloud.
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