Networking architectures for AI inference model serving
๐ This post compares two reference networking architectures for AI inference model serving: one tailored for Google Kubernetes Engine (GKE) and one for mixed or alternative backends. It explains a common control-plane pattern using Private Service Connect, optional Apigee, and Model Armor as a centralized entry point for secure, private inference calls. The GKE design adds a specialized GKE Inference Gateway, inference pools, and replica sets for GPU/TPU workloads. The multi-backend design uses a regional internal Application Load Balancer, a Cloud Run payload processor service extension to inject model headers, and Network Endpoint Groups to route to heterogeneous backends.
