Unlocking Creative AI Architectures: Demystifying GKE Inference Gateway and Its Extensible Use Cases

Faced with deploying AI inference workloads to meet demand in an environment where accelerators are becoming more and more scarce, AI engineers are starting to get creative about serving architectures. GKE Inference Gateway can enable a wide world of architectural choices to meet your needs — whether you want to build deployments that scale with usage, or respond to batched requests, or need the lowest latency response times to constant requests.

Here is a guide to understanding what GKE Inference Gateway is, how it works under the hood, how you can extend it, and the potential architectural use cases we brainstormed to help you get the most out of your scarce AI Accelerator (GPUs and TPUs) hardware.

1. GKE Inference Gateway 101: The Basics

Traditionally, Kubernetes networking relied on standard L7 Ingress or basic Gateway API routing, which distributes requests based on simple variables like URL path, hostname, or HTTP headers. For general microservices, this is perfect. But for Large Language Models (LLMs), it falls short.

LLM serving is highly stateful and computationally volatile. The memory of your AI accelerator is heavily consumed by the Key-Value (KV) cache — the stored memory of previous tokens in a conversation. If a load balancer randomly routes sequential prompts of the same conversation to different AI accelerator nodes, each node must recalculate the entire history from scratch, spiking your Time-to-First-Token (TTFT) and wasting valuable compute cycles.

GKE Inference Gateway acts as an extension of the standard Kubernetes Gateway API designed specifically to bridge the gap between application networking and hardware-level accelerator metrics. According to the GKE Inference Gateway Overview, the gateway routes requests by reading telemetry directly from your model servers, optimizing based on:

• KV Cache Hits: Directing requests with matching prompt prefixes to the same node to maximize cache reuse.
• Request Queue Depth: Bypassing heavily loaded model servers even if they have a cache hit, preventing node starvation.

Accelerator Utilization: Monitoring real-time AI accelerator load to intelligently balance the cluster.

2. Under the Hood: How GKE Inference Gateway Can Be Extended

GKE Inference Gateway isn’t a proprietary black box. It is built as an open-source-first stack leveraging the SIG-Multicluster Gateway API Inference Extension, which is an official Kubernetes project designed to optimize self-hosted generative models.

At the center of its extensible architecture are two primary mechanisms:

The External Processing (ext-proc) & Endpoint Picker

The gateway interceptor utilizes Envoy’s **External Processing (ext-proc) API. When a request comes in, it is sent to an external gRPC service called the [Endpoint Picker (EPP)](https://gateway-api-inference-extension.sigs.k8s.io/)**.

The EPP calculates a dynamic performance score for every model replica in an InferencePool using a configurable multi-objective scoring formula:

Because the EPP is built on the open-source Kubernetes Gateway API Inference Extension, platform teams can write custom scheduling plugins to override or extend these scoring heuristics, implementing bespoke routing strategies suited to their specific hardware layouts.

Body-Based Routing (BBR)

Because clients query LLMs using JSON payloads (such as the standard OpenAI API specification) where the target model name is hidden inside the HTTP body rather than the headers, the gateway implements a Body-Based Routing (BBR) extension.

The BBR parser reads the incoming JSON payload, extracts the “model” field, and injects it as a new HTTP request header: X-Gateway-Model-Name. Standard Gateway API HTTPRoute rules can then match against this injected header to seamlessly split or mirror traffic across different hardware backends. To configure this setup, consult the Configure Body-Based Routing guide.

3. High-Value Architectural Use Cases

By taking advantage of GKE Inference Gateway’s native capabilities and its extensibility hooks, engineering teams can build highly creative serving topologies.

Use Case A: The Infinite Context RAG (Prefix-Cache Optimization)

• The Problem: In document search, coding assistants, or multi-turn RAG applications, users feed massive context windows (e.g., entire codebases or 50-page PDFs) into the prompt. Recalculating this context on every turn is incredibly expensive and slow.
• The Architecture: By enabling Prefix-Cache Aware Routing, the Inference Gateway inspects incoming request prefixes. It ensures that any client querying the same document or codebase is directed to the exact AI accelerator replica that already has those calculations warmed up in its local KV cache.
• The Benefit: As discussed in Google’s engineering breakdown on how GKE Inference Gateway improved Vertex AI latency, migrating production workloads to this architecture doubled prefix cache hit rates (from 35% to 70%) and slashed tail latency (P95 TTFT) by 52%.

Use Case B: Cost-Effective Multi-Tenant SaaS (Dynamic LoRA Multiplexing)

• The Problem: Running dedicated AI accelerator pools for dozens of different customers, each requiring a slightly fine-tuned model variant, is a fast track to cloud-cost bankruptcy.
• The Architecture: Instead of deploying dozens of full-sized base models, you deploy a single base model pool on a shared set of accelerators. The Inference Gateway tracks which adapters are currently active on which nodes.
• The Benefit: When a tenant requests their specific model, the gateway prioritizes routing them to an AI accelerator replica that already has their LoRA adapter loaded. This multiplexes dozens of custom tenants on a fraction of the hardware, maximizing model density and lowering your hardware TCO. For detailed step-by-step guidance on setting up these resources, see the Deploy GKE Inference Gateway guide and the vLLM Serving Tutorial.

Use Case C: The “Zero-Waste” Cluster (Unified Real-Time & Async Processing)

• The Problem: Real-time conversational chatbots require massive over-provisioning to handle sudden traffic spikes, leaving expensive AI accelerators sitting idle during off-peak hours. Meanwhile, batch tasks (like document summarization or vector indexing) are relegated to secondary, fragmented clusters.
• The Architecture: You unify your workloads into a single, fluid accelerator pool. Real-time queries are sent directly to the GKE Inference Gateway with high priority. Async or batch requests are published to a Cloud Pub/Sub queue. An Async Processor Agent acts as an extensible consumer, pulling items from Pub/Sub and feeding them to the gateway only when there is idle AI accelerator capacity. This pattern is fully detailed in Google Cloud’s article on unifying real-time and async inference.
• The Benefit: If a human user starts a chat, the gateway instantly prioritizes their traffic and temporarily “squeezes out” or delays the async background jobs. This fills the hardware “valleys” with background work, ensuring close to 100% utilization without degrading real-time SLAs.

Use Case D: Regional Capacity Bursting (Multi-Cluster Resilience)

• The Problem: You hit strict regional accelerator quota caps or experience local hardware outages, but your AI application cannot afford downtime.
• The Architecture: You configure a Multi-Cluster GKE Inference Gateway. A central “config cluster” handles the global traffic management while your model pools run in distinct “target clusters” across multiple geographic regions (e.g., Iowa, Frankfurt, Tokyo). As highlighted in the Google Cloud Blog on scaling workloads with multi-cluster gateway, you define load-balancing rules linked to custom metrics (like fleet-wide KV cache utilization) using a GCPBackendPolicy. You can find a complete configuration example in the Elastic Cross-Region HA guide.
• The Benefit: If your primary region gets overwhelmed or hits a physical capacity limit, the global gateway automatically bursts the excess traffic to a secondary region with available GPU/TPU headroom. This delivers elastic, global high-availability for enterprise AI applications.

4. How to Get Started

If you are ready to move your model-serving logic out of custom, fragile application code and push it to the platform level, GKE Inference Gateway is your answer.

To deploy it in your GKE cluster:

1. Enable the standard Gateway API on GKE (version 1.32+ is recommended). If you want to dive into the technical implementation, check out the GKE Inference Gateway Walkthrough.
2. Define your InferencePool and InferenceObjective Custom Resources to manage your model instances and their serving priorities.
3. Install the Body-Based Router using Helm to parse OpenAI-compliant payloads.
4. Configure HPA (Horizontal Pod Autoscaler) to scale based on custom metrics like inference_pool_average_kv_cache_utilization instead of outdated CPU or memory usage metrics.
5. Explore advanced architectures, such as deploying LLMs with Ray Serve on GKE Inference Gateway, or setting up GKE managed DRANET with Nvidia B200 GPUs.

By leveraging these flexible, cloud-native building blocks, you can build a resilient, cost-effective, and blazing-fast AI inference platform that extracts every last drop of performance from your cloud accelerators.

Learn More

If you want to learn more about this useful technique, there is now a ton of great guidance available. Here are a just a few of the things I read to prepare for writing this:

1. Unifying real-time and async inference with GKE Inference Gateway | Google Cloud Blog, https://cloud.google.com/blog/products/containers-kubernetes/unifying-real-time-and-async-inference-with-gke-inference-gateway
2. About GKE Inference Gateway | GKE networking — Google Cloud Documentation, https://docs.cloud.google.com/kubernetes-engine/docs/concepts/about-gke-inference-gateway
3. Implementing High-Performance LLM Serving on GKE: An Inference Gateway Walkthrough, https://cloud.google.com/blog/topics/developers-practitioners/implementing-high-performance-llm-serving-on-gke-an-inference-gateway-walkthrough
4. How GKE Inference Gateway improved latency for Vertex AI | Google Cloud Blog, https://cloud.google.com/blog/products/containers-kubernetes/how-gke-inference-gateway-improved-latency-for-vertex-ai
5. https://docs.cloud.google.com/kubernetes-engine/docs/concepts/about-gke-inference-gateway#:~:text=GKE%20Inference%20Gateway%20is%20an,observability%20of%20AI%20inference%20workloads.
6. Kubernetes Gateway API Inference Extension: Introduction, https://gateway-api-inference-extension.sigs.k8s.io/
7. kubernetes-sigs/gateway-api-inference-extension — GitHub, https://github.com/kubernetes-sigs/gateway-api-inference-extension
8. Configure Body-Based Routing | GKE networking — Google Cloud Documentation, https://docs.cloud.google.com/kubernetes-engine/docs/how-to/configure-body-based-routing
9. Serve an LLM with GKE Inference Gateway | GKE AI/ML — Google Cloud Documentation, https://docs.cloud.google.com/kubernetes-engine/docs/how-to/serve-with-gke-inference-gateway
10. Deploy GKE Inference Gateway | GKE networking — Google Cloud Documentation, https://docs.cloud.google.com/kubernetes-engine/docs/how-to/deploy-gke-inference-gateway
11. Serve an LLM with GKE Inference Gateway | GKE AI/ML — Google Cloud Documentation, https://docs.cloud.google.com/kubernetes-engine/docs/tutorials/serve-with-gke-inference-gateway
12. Multi-cluster GKE Inference Gateway helps scale AI workloads | Google Cloud Blog, https://cloud.google.com/blog/products/containers-kubernetes/multi-cluster-gke-inference-gateway-helps-scale-ai-workloads
13. https://docs.cloud.google.com/kubernetes-engine/docs/concepts/about-multi-cluster-inference-gateway
14. Serve an LLM with multi-cluster Ray Serve and GKE Inference Gateway | GKE AI/ML, https://docs.cloud.google.com/kubernetes-engine/docs/how-to/serve-multi-cluster-ray-inference-gateway
15. Experimenting with GPUs: GKE managed DRANET and Inference Gateway AI Deployment | Google Cloud Blog, https://cloud.google.com/blog/topics/developers-practitioners/experimenting-with-gpus-gke-managed-dranet-and-inference-gateway-ai-deployment