A new era of enterprise AI is beginning This article explores ai factories infrastructures. . What started out as experimentation—single models, few pilots, and proofs of concept—is now developing into AI factories, which are infrastructures specifically built to facilitate high-velocity optimization, always-on inference, and continuous model training at scale.

The upcoming wave of enterprise transformation across industries is being driven by these environments. However, as AI factories grow, new threats unique to AI are introduced, significantly increasing the cyberattack surface. These days, models, apps, data pipelines, and underlying infrastructure are all closely related—and becoming more visible. An architectural framework that views AI factories as cohesive, functional systems rather than disparate, loosely coupled parts is necessary.

Where Cloud-First Presumptions Start Fraying Public cloud platforms are still useful for many AI workloads and for experimentation.

However, production AI factories impose long-term limitations, including low-latency data paths, predictable performance, persistent access to accelerated compute, and stringent governance requirements. These realities are driving businesses more and more toward purpose-built deployments, frequently on premises or in strictly regulated hybrid models, for long-running training, performance-sensitive optimization, and low-latency inference—particularly in regulated or latency-sensitive environments. This isn't a rejection of cloud.