Gateway API for AI Inference: Routing LLM Traffic on Kubernetes

As LLM inference moves onto Kubernetes, teams need more than basic Ingress rules. The Gateway API provides expressive routing, header-based model selection, and integration points for policy enforcement—ideal for multi-model platforms and gradual model rollouts.

Common patterns include weighted traffic splits for A/B testing new models, request-level rate limits to protect GPU pools, and mTLS between gateway and inference pods. Combined with Horizontal Pod Autoscaling and KEDA, organizations can scale inference services based on queue depth or token throughput.

cloudstrata implements Gateway API–based inference layers on OpenShift and vanilla Kubernetes, connecting them to observability, cost tracking, and enterprise identity systems so AI traffic is secure, measurable, and production-ready.