The great migration: Why every AI platform is converging on Kubernetes

Kubernetes has evolved from a microservices platform into the de facto infrastructure for AI workloads. By 2026, 82% of container users run Kubernetes in production and 66% of organizations use it for generative AI inference. The post traces three eras of Kubernetes adoption and covers the full AI stack: data processing with Apache Spark and Kubeflow, ML pipeline orchestration with Argo Workflows, distributed training with gang scheduling tools like Kueue and Volcano, LLM inference serving with vLLM/KServe, and agentic workloads using LangGraph and KEDA. GPU optimization strategies including MIG, time-slicing, DRA, and Karpenter are discussed, along with multi-cluster scheduling for large-scale AI. Emerging areas include control plane scalability beyond etcd, unified agent operators, and a new CNCF AI conformance program.