Unifying real-time and async inference with GKE Inference Gateway

As AI workloads transition from experimental prototypes to production-grade services, the infrastructure supporting them faces a growing utilization gap. Enterprises today typically face a binary choice: build for high-concurrency, low-latency real-time requests, or optimize for high-throughput, "async" processing.

In Kubernetes environments, these requirements are traditionally handled by separate, siloed GPU and TPU accelerator clusters. Real-time traffic is over-provisioned to handle bursts, which can lead to significant idle capacity during off-peak hours. Meanwhile, async tasks are often relegated to secondary clusters, resulting in complex software stacks and fragmented resource management.

For AI serving workloads, Google Kubernetes Engine (GKE) addresses this "cost vs. performance" trade-off with a unified platform for the full spectrum of inference patterns: GKE Inference Gateway. By leveraging an OSS-first approach, we’ve developed a stack that treats accelerator capacity as a single, fluid resource pool that can serve workloads that require serving both deterministic latency and high throughput ...