The best place to run AI agents

On-premises also gives you control over the one thing agents touch most: your data. Agents working with patient records, financial transactions, or classified materials can keep everything inside your security boundary, which makes audits, contractual obligations, and sovereignty requirements dramatically simpler. You control model versions, upgrade timing, and the entire operational surface, so there’s no silent model deprecation breaking a production agent overnight, which happens more often than anyone admits.

The cons are equally clear. Capital costs are steep, with modern inference GPUs running tens of thousands of dollars each, and you size for peak whether you hit it or not. You own the entire stack: power, cooling, hardware refresh cycles, kernel and driver updates, and the scarce skills to run GPU fleets reliably. Utilization is the killer. An on-prem GPU running at 20% is a machine that pays rent whether it works or not. And development velocity suffers since your teams spend cycles on infrastructure instead of agent behavior.

These three are not the same thing, and conflating them causes bad decisions. Public platforms, meaning hyperscale model services and agent runtimes, trade control for speed and scale. They’re right for experimentation, customer-facing agents, and anything needing the quality of a frontier model on day one. Private cloud platforms, your own VMware, OpenStack, or OpenShift-based environments are increasingly offered as bare-metal deployments within a colocation facility and give you cloudlike automation and self-service over hardware you control. For many enterprises, this is the pragmatic middle ground: dedicated inference clusters on a private cloud, fronted by cloud-native orchestration, with data and models staying put.