Google Cloud now publishes two concrete networking reference patterns for AI inference model serving—one that assumes the model runs exclusively on Google Kubernetes Engine (GKE) and another that works with any backend type. The shift gives engineers a defined entry point, optional API‑management integration, and an inline safety layer, which directly influences how you design, deploy, and secure inference workloads.
Common networking building blocks
Both patterns share three core services:
- Private Service Connect inference endpoint: Provides a private IP address inside the consumer VPC, keeping inference traffic off the public internet.
- Apigee API Management (optional): An extension processor can enforce client identity checks, rate limits, and quota before traffic reaches compute resources.
- Model Armor: An inline checkpoint that scans prompts and responses for injection attempts and sensitive data leakage.
GKE‑only inference pattern
When the model resides in GKE, the architecture adds two GKE‑specific components:
- GKE Inference Gateway: Deployed as an internal Application Load Balancer (
gke-l7-rilb) that parses request payloads, appliesHTTPRouterules, and routes calls to the appropriate model‑serving pods. - Inference pools: Logical groups of identical model replicas that the gateway can load‑balance across, simplifying scaling and version management.
The gateway sits behind the Private Service Connect endpoint, so inbound traffic first terminates TLS, then passes through optional Apigee processing, and finally reaches Model Armor before being handed to the inference pool. This ordering isolates the compute layer from direct client exposure and centralizes policy enforcement.
Backend‑agnostic inference pattern
For environments that use non‑GKE backends—such as Cloud Run, Compute Engine, or third‑party services—the reference design replaces the GKE Inference Gateway with a standard Cloud Load Balancer that terminates TLS and forwards traffic to the Private Service Connect endpoint. The same optional Apigee layer and Model Armor checkpoint remain in place, preserving a consistent security posture across all deployment targets.
Because the load balancer is a generic L7 entry point, the pattern does not prescribe a specialized routing engine; instead, routing logic must be handled by the backend service itself or by additional service‑mesh components if required.
Related CloudNinjas coverage: Google Cloud.
What This Means For Practitioners
Adopting these patterns forces a clear separation between the public entry point and the model execution environment. Engineers should evaluate existing ingress configurations and replace any direct internet‑facing endpoints with Private Service Connect‑backed entry points. Security teams can leverage Model Armor as a unified safety filter, while API‑management owners can decide whether the optional Apigee layer adds needed governance. For GKE operators, the Inference Gateway offers a purpose‑built ingress that simplifies routing to inference pools, but it also introduces an extra service to monitor and scale. Teams using other backends must ensure their load balancer configuration can meet the same TLS termination and routing requirements without the gateway’s built‑in rules. Overall, the new reference architectures provide a repeatable, security‑first foundation for serving AI models at scale, but they also require a review of networking, policy, and observability tooling to align with the prescribed flow.
