The dominant container orchestration platform for production AI workloads. By 2026, Kubernetes, with extensions like KServe, Ray on Kubernetes, and the Kubeflow
Kubernetes is the operating system of AI infrastructure: GPU scheduling, autoscaling inference fleets, job orchestration for training, and the substrate beneath most ML platforms. The AI era added dedicated machinery, device plugins, queueing (Kueue), and inference-aware autoscaling, making K8s fluency an AI-platform prerequisite.
Pricing: Open-source free; managed control planes (EKS/GKE/AKS) bill modest per-cluster fees plus compute.
API-first app teams often don't; anyone operating self-hosted models, shared GPUs, or training pipelines almost certainly does: it's where utilization and reliability get engineered.
Device plugins expose GPUs, node pools and taints isolate them, queueing systems (Kueue) arbitrate scarce capacity, and inference autoscalers react to latency/queue depth rather than CPU: a distinct operational playbook.