What is Private Cloud AI?

Private cloud AI is the deployment model that gives regulated businesses the most control over their data. Instead of sending your documents and queries to a shared AI service where they are processed alongside other organisations data, private cloud AI runs the model within your own cloud tenancy. Your data stays in your environment, processed by infrastructure that only you control.

For mid-market firms handling sensitive regulated data, this control matters. When a financial advisory firm sends client portfolio data to a public AI API for analysis, that data transits through and is processed on infrastructure shared with thousands of other users. Even with contractual protections, the firm has limited visibility into how that infrastructure is managed. With private cloud AI, the processing happens on dedicated resources within your own AWS, Azure, or Google Cloud account, subject to your security policies and access controls.

The practical barrier to private cloud AI was historically cost. Running a capable AI model required expensive GPU instances that put it beyond the reach of most mid-market firms. This has changed significantly. Services like AWS Bedrock allow you to run leading foundation models within your own cloud tenancy at per-token pricing that is accessible to firms of all sizes. You get the control of private infrastructure without the capital expenditure of dedicated hardware.

Private cloud AI also simplifies your compliance position. Data processing agreements are with your cloud provider rather than an AI vendor. Data residency can be guaranteed by selecting UK cloud regions. Audit logs are under your control. And you can implement your own retention and deletion policies without depending on a third-party provider to honour them.

The trade-off is operational complexity. Private cloud AI requires someone to manage the infrastructure, handle model updates, and monitor performance. For mid-market firms without a large technology team, managed services and infrastructure-as-code approaches reduce this burden substantially, but it should be factored into your planning.