A GPU quotation can arrive with an attractive hourly rate, a clear allocation of accelerator capacity, and a deployment schedule that appears ready to support the next stage of an AI workload. Yet the commercial terms rarely reveal the complete operating conditions attached to that capacity, particularly when the infrastructure provider controls the underlying site, cooling architecture, network environment, storage systems, and operational personnel. What looks like a straightforward compute purchase can introduce dependencies involving the physical location of sensitive information, the movement of model artifacts, access to proprietary workloads, and the ability to recover operations when something goes wrong. These dependencies become more consequential when AI workloads move beyond experimentation and begin processing confidential information, proprietary training material, customer records, or commercially sensitive inference activity.