Benchmarking Kubernetes AI Inference: GKE vs EKS on Equal Footing
Principled Technologies recently ran a hands-on benchmark comparing Google Kubernetes Engine (GKE) and Amazon Elastic Kubernetes Service (EKS) for production-scale generative AI inference. Both platforms hosted the same inference engine serving the Llama 3.1-8B Instruct model, with deployments backed by eight NVIDIA A100 40GB GPUs and using the Kubernetes inference-perf benchmark. The only material difference was how each environment distributed requests: GKE used the inference-aware GKE Inference Gateway, while EKS relied on a standard HTTP load balancer. This controlled setup makes the resulting data a strong indicator of how infrastructure and traffic management choices affect Kubernetes AI inference in real-world scenarios. For platform teams standardizing on containers, the study isolates the impact of the Kubernetes control plane and gateway layer on model behavior, rather than differences in model code, hardware, or scaling policies.
Measured Gains: Throughput, Latency, and Tail Behavior on GKE
The benchmark results highlight a clear GKE inference performance advantage over EKS. GKE with Inference Gateway delivered 15.7% higher output token throughput, processing roughly 1,000 more tokens per second than the EKS setup on the same GPU configuration. Latency gains were even more dramatic: mean time to first token (TTFT) was 92.8% lower on GKE, cutting more than 2,000 milliseconds from initial response time and substantially improving perceived responsiveness in chat and streaming user interfaces. Inter-token latency (ITL) was 62.6% lower, enabling smoother token streaming once responses began. Crucially for production reliability, GKE also showed up to 83.9% lower 95th-percentile tail latency and a 67.0% lower 95th-percentile normalized time per output token, reducing the likelihood of outlier slow requests that can degrade user experience and complicate SLO management.
Why GKE Pulls Ahead: Inference-Aware Routing and Cache Locality
According to Principled Technologies, the performance gap is driven primarily by inference-aware optimizations in the GKE Inference Gateway. A key feature is prefix-cache-aware routing, which steers requests that share context—such as similar prompts, document segments, or conversation histories—to the same model replica. This boosts cache hit rates and avoids recomputing identical or overlapping prefixes. In practice, that means better utilization of GPU accelerators, higher throughput, and lower latency, particularly for workloads like multi-turn AI chat, retrieval-augmented generation (RAG), document Q&A, and template-based text generation where requests often reuse common prefixes. By contrast, a standard HTTP load balancer typically lacks awareness of model caches or tokenization behavior, distributing traffic more generically. The PT report suggests that aligning traffic management with inference engine characteristics is emerging as a critical lever for inference engine optimization on Kubernetes.
Implications for Platform Teams Designing Production AI on Kubernetes
For organizations building large-scale AI services on Kubernetes, the study underscores how deeply infrastructure choices can shape GKE inference performance and overall user experience. When two clusters run the same model on equivalent GPUs yet deliver markedly different throughput and latency, the Kubernetes distribution, gateway layer, and routing logic become strategic decisions, not just operational details. Lower time to first token and better tail latency translate directly into more responsive conversational and RAG applications, or conversely, the ability to serve the same workload with fewer GPU nodes. Platform and MLOps teams evaluating EKS vs GKE should factor inference-aware capabilities—such as cache-local routing and AI-optimized gateways—into their reference architectures, rather than focusing solely on node pricing, autoscaling, or generic networking. As generative AI traffic grows, these optimizations may be decisive in meeting SLOs and controlling infrastructure sprawl.