Building an AI factory on Kubernetes

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An AI factory is not just a model or a cluster. It is a pool of GPUs that many teams draw from at once: one team fine-tuning, another serving inference, a third running evaluations, all on the same accelerators. NVIDIA frames it as “infrastructure for the full AI lifecycle, from data preparation through training, fine-tuning, and high-volume inference”. In an enterprise that means one fleet, many teams, and different quotas, policies, and trust boundaries layered on top. The hard question is no longer how to train a model. It is how to give every team safe, isolated access to the same expensive hardware without anyone stepping on anyone else.

Two years ago every platform team was building a developer platform. Kubernetes already had mature primitives for containers, RBAC, autoscaling, and policy. What it did not have was a clean answer for accelerators, or for keeping tenants apart on the same nodes. That is the gap an AI factory has to close, and the cloud native ecosystem now supplies most of the parts to close it.

The bottleneck is utilization, not model serving

Accelerators are the dominant capital expense in the building, and the metric that decides whether that spend pays off is utilization, not a peak tokens-per-second number from a single run. The market grades GPU clouds the same way. SemiAnalysis’s ClusterMAX scores providers on security, networking, storage, reliability, and support rather than raw throughput, and its security criteria reward hard per-tenant isolation, down to per-tenant Kubernetes clusters and DPU-based isolation, while flagging weak …

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• Originalquelle / Hersteller-Advisory lesen: https://www.cncf.io/blog/2026/08/27/building-an-ai-factory-on-kubernetes/
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