A design and evaluation reference for running agentic AI inside infrastructure you control: your own cloud account today, your own facility later.

In this white paper, you'll discover:

• What running agentic AI inside your boundary requires: A controlled deployment has to answer four questions: Does the workload need a trust boundary?
• Cost model and key sensitivities: A credible cost model states the range, assumptions, utilisation, staffing, and exit cost.
• What the platform has to do: A private AI environment is nine cooperating planes.
• Reference architecture: Security review asks for three drawings: the components, how data moves between them, and how the network is arranged.
• GPU and compute sizing: Sizing conversations go wrong because they start from the parameter count.

Download the full white paper below for the complete analysis, architecture detail, and implementation guidance.