Private AI on-prem: when you cannot use the cloud
When business needs Private AI on-prem or private cloud: security requirements, sensitive data, air-gapped setups, and how to choose a deployment model.
A cloud chat is convenient, but for some companies it is an immediate stop: data, regulation, perimeter, vendors.
Typical reasons “public cloud is not allowed”
This is not only paranoia. There are often formal constraints: security policy, customer requirements, industry rules, personal data or secrets.
- Data must not leave the company perimeter
- You need an air-gapped or isolated environment
- Prompts and documents cannot be sent to an external provider
- Audit, model control and clear ownership are required
What deployment options look like
Private AI is not one single approach. Teams usually choose between on-prem, private cloud and hybrid.
- On-prem: maximum control, higher infrastructure and MLOps requirements
- Private cloud: faster start, still a dedicated perimeter
- Hybrid: sensitive workloads inside, the rest outside under policy controls
What else matters besides “installing a model”
The model is only one part. You also need data connectors, access rights, quality monitoring, index updates and an operating process. Gleyk is that layer: the model is yours to choose, scenarios do not get rewritten.
Otherwise you get a “local chat” that still does not know your documents and is not embedded in how teams work.
How to decide
Start not with GPU choice, but with a data and risk map: which sources are critical, who uses them, what cannot leave, which scenario will show value fast.