Building a Kubernetes-native pattern for AI infrastructure at scale

Running AI inference on Kubernetes at scale requires more than getting a model to respond. This post outlines a three-layer cloud-native architecture for production AI infrastructure: a model layer using KAITO for declarative lifecycle management, an inference access layer using liteLLM as a unified gateway, and a compute layer using GPU Flex Nodes for elastic cross-cloud scheduling. The pattern is illustrated through an event-driven incident triage use case, addressing common pain points like fragmented GPU capacity, inconsistent inference interfaces, and batch-oriented infrastructure that struggles with bursty demand.