Private AI deployment means running an AI model and the systems around it inside an environment your organization controls, such as its own cloud account or its own data center. Prompts, documents and outputs can then stay inside that boundary, which can make data residency, confidentiality and audit requirements easier to meet.
What are the main ways to deploy AI privately?
There are three common patterns, and the right one depends on your data, your team and your regulators:
- In your cloud account: the AI application and model run in your own cloud environment, under your security controls.
- On premises: everything runs on hardware in your own data center, for data that can't leave your network.
- Hybrid: sensitive steps stay on premises or in your account, while less sensitive steps use a managed service.
Private deployment still asks something of you: someone has to run, secure and update the system. NIST's definition of cloud computing is a useful reference for who controls what in each cloud service model (IaaS, PaaS, SaaS).1 Soren AI has deployed in the cloud, on premises and hybrid; see our work with banks and credit unions.
Frequently asked questions
Does private AI deployment mean training our own model?
No. It means running an existing model inside a boundary you control, such as an open-weight model hosted in your own cloud account or data center. Training a model from scratch is rarely needed. Connecting the model to your documents, systems and rules is what makes it useful.
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