Inside the Principled Technologies GKE vs EKS Benchmark
Principled Technologies (PT) recently ran a hands-on benchmark that puts hard numbers behind the EKS vs GKE comparison for AI inference workloads. Using the Kubernetes inference-perf benchmark and the Llama 3.1-8B Instruct model, PT evaluated an identical inference engine on Google Kubernetes Engine (GKE) and Amazon Elastic Kubernetes Service (EKS). Both environments ran on the same hardware: eight NVIDIA A100 40GB GPUs. The only meaningful difference was how traffic was distributed across replicas. GKE used the GKE Inference Gateway, an inference-aware gateway, while EKS relied on a standard HTTP load balancer. This design choice turned out to be decisive. The GKE setup delivered 15.7% higher token throughput, 92.8% lower time to first token, and substantially better tail latency metrics, highlighting how request-routing intelligence alone can reshape AI inference performance without changing GPUs, models, or base Kubernetes primitives.
How GKE Inference Gateway Accelerates Kubernetes AI Workloads
The standout factor in the benchmark is the GKE Inference Gateway, designed explicitly for Kubernetes AI workloads. Unlike a generic HTTP load balancer, the gateway understands inference patterns and uses optimizations such as prefix-cache-aware routing. When multiple requests share context—common in multi-turn chat, template-based generation, or document Q&A—the gateway routes them to the same model replica, maximizing cache hits. This cuts redundant computation and improves accelerator utilization on GPUs and TPUs. In practice, that intelligence translated into 15.7% higher token throughput in the Principled Technologies study, roughly 1,000 additional tokens per second on the tested setup. Beyond raw throughput, inference-aware routing also stabilizes performance under load, smoothing out response times and reducing jitter that can otherwise degrade user experience in real-time AI applications and streaming interfaces.
Latency, Throughput, and Tail Behavior: Why the Numbers Matter
For production AI inference performance, the GKE results go beyond incremental gains. PT measured a 92.8% lower mean time to first token (TTFT) on GKE, with responses arriving over 2,000 milliseconds sooner than on EKS. That gap is directly perceptible to users of interactive chatbots and assistants. Inter-token latency (ITL) was 62.6% lower as well, enabling smoother token streaming and more natural-feeling responses. Perhaps most important for reliability, tail latency improved sharply: up to 83.9% lower 95th-percentile latency and 67.0% lower 95th-percentile normalized time per output token. Tail behavior is where user frustration and SLA violations usually surface. By tightening these extremes, GKE with Inference Gateway reduces the chance of sporadically slow responses during traffic spikes, which is crucial for customer-facing AI products and any workload with strict latency objectives.
When GKE Inference Gateway Changes the Cost–Performance Equation
The performance gains in the benchmark translate directly into capacity and cost efficiency implications. Higher throughput means a cluster can serve more requests per second on the same GPU footprint, or equivalently, meet a given traffic level with fewer accelerators. Lower latency and better tail behavior reduce the tendency to overprovision resources just to protect worst-case response times. The PT report highlights that these benefits are strongest for workloads where requests share prefixes or benefit from cache locality—document Q&A, retrieval-augmented generation (RAG), multi-turn conversations, and template-driven content generation. For these patterns, the GKE Inference Gateway’s routing intelligence unlocks more value from each GPU. For teams choosing between Google Cloud and AWS for AI inference infrastructure, this EKS vs GKE comparison suggests that, on identical hardware, inference-aware gateways can be as impactful as the choice of model or accelerator type.