Where data lives should depend on the data.
Choose on-prem, cloud or hybrid by data sensitivity, compute needs and budget.
Hybrid
Best for
- Sensitive data stays in-house
- Everyday tasks run in the cloud
Trade-offs
- Security and flexibility together
- Needs planning up front
Four criteria
Security
Which data must never leave the company.
Performance
How fast, and how many people at once.
Build cost
Hardware, licenses and development.
Upkeep
Who maintains it, and how it's updated.
FAQ
Can a small or mid-sized company run private AI?
Yes. Start with a proof of concept on one workflow, then choose the deployment by data sensitivity and budget.
Can we start in the cloud and move on-prem later?
We can plan for that from day one, deciding early which data may need to come back in-house.
Start with one problem. No need to decide what to buy first.