Production AI Inference on Kubernetes: The 7 Things It Actually Requires (and Who Should Run It)

Production LLM inference on Kubernetes requires specialized capabilities beyond a standard Deployment and CPU-based Horizontal Pod Autoscaler, including GPU device management and sharing, accelerator-aware scheduling, rapid model loading, scaling based on queue depth or token latency, model-aware routing, idle-capacity cost controls, and token-level observability. The article argues that large models have minute-scale cold starts, long-lived streaming responses, nonlinear batching behavior, and costly GPU capacity, requiring components such as vLLM or SGLang, KServe or llm-d, KEDA, Kueue, Karpenter, the NVIDIA GPU Operator, and Gateway API inference extensions. It notes that agentic workloads add state, bursty tool calls, egress controls, credentials, and tenant isolation. Self-hosting can become less expensive than per-token APIs at sustained high utilization, particularly when organizations can use cloud commitment discounts, but operational complexity and upgrade overhead remain substantial. KubeCon North America 2026’s dedicated AI Inference and Agentic track is presented as evidence that Kubernetes is adapting to these operational demands, while deployment choices range from managed APIs and GPU platforms to internally operated clusters and developer platforms that run workloads in a customer’s cloud account.

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