On-Premises Deployment

Despite the cloud's dominance, there are legitimate reasons to deploy AI systems on your own hardware. Highly regulated industries - healthcare, finance, defence, government - may face data residency requirements or security policies that preclude cloud use. Organisations with very predictable, sustained workloads may find that owning hardware is cheaper over three to five years than renting it from a cloud provider. And in some cases, the latency or bandwidth requirements of an application make local deployment necessary. On-premises AI infrastructure typically means procuring NVIDIA DGX systems or similar GPU servers, setting up the networking and storage to support them, and managing the software stack yourself. This requires significant upfront capital investment and ongoing operational expertise. Kubernetes-based platforms like Kubeflow can help manage on-premises AI workloads, but the operational burden is substantially higher than using managed cloud services. A hybrid approach is increasingly common - using on-premises infrastructure for sensitive data and steady-state workloads while bursting to the cloud for training runs or demand spikes. This offers flexibility but adds complexity in managing workloads across environments. If you're considering on-premises deployment, be realistic about the total cost of ownership, including not just hardware but power, cooling, space, staff, and the opportunity cost of managing infrastructure rather than building products.