“Cloud-first” has long been the rule. But when it comes to AI workloads and constraints related to cost, latency, sovereignty, compliance, and energy consumption, this principle is reaching its limits. Should AI be run in the public cloud, in a multi-cloud environment, in a hybrid setup, at the edge, or on-premises? The choice of infrastructure has become a strategic decision, where performance, cost control, and vendor dependency must now be balanced.