AI infrastructure refers to the hardware, orchestration layers, hosting environments, and monitoring stacks that make intelligent systems operational. It includes GPU scheduling, microservices, storage design, inference optimization, security boundaries, and failover architecture. Hosting then governs how these models are deployed - privately, securely, and in alignment with your organization's requirements.

SMBs often face a paradox: public cloud tools provide convenience but expose teams to vendor lock-in, unpredictable billing, privacy constraints, and limited flexibility. Private infrastructure reverses the equation. You gain control over the full stack - the model weights, the GPU environment, the security perimeter, and the data governance rules. With the right architecture, even small teams can operate with enterprise-level capability.

Infrastructure is not overhead. It is the skeleton that makes intelligence durable.