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Most enterprise AI infrastructure conversations land on the same tension. Builders want self-service: instant access to GPUs, a stable endpoint for a model, a notebook, or a fine-tuning run that starts without a ticket. Platform teams want per-team quotas, identity-aware access, audit trails, tenant isolation, and cost attribution they can defend. Both sides are right, and most platforms make you choose between them because self-service and control get layered onto a base that was designed for neither.

The term “AI factory” is usually introduced as a hardware story: racks of accelerators, high-speed networking, and validated reference designs. That part is real, but it is not the part most organizations struggle with. The harder shift is operational. Moving from AI as a set of individual experiments to AI as a shared, governed capability that many teams depend on changes how compute is allocated, how environments are built, who owns the platform, and how the work is governed.