Benchmark Overview: GKE Pulls Ahead in Kubernetes AI Inference
Infrastructure is quickly becoming a decisive factor in AI application performance, and new testing from Principled Technologies highlights a clear gap between Google Kubernetes Engine (GKE) and Amazon Elastic Kubernetes Service (EKS) for Kubernetes AI inference workloads. Using the Llama 3.1-8B Instruct model and the Kubernetes inference-perf benchmark, Principled Technologies compared an inference engine running on GKE with GKE Inference Gateway against the same engine on EKS fronted by a standard HTTP load balancer. Both environments used identical hardware: clusters backed by eight NVIDIA A100 40GB GPUs. Despite the hardware parity, the GKE configuration demonstrated higher throughput, lower latency, and better tail behavior. For enterprises standardizing on Kubernetes AI inference, these results suggest that the orchestration layer and its gateway design can influence real-world performance as much as model choice or GPU count.
Key Results: Higher Throughput and Dramatically Lower Latency on GKE
The EKS vs GKE benchmark revealed that GKE with Inference Gateway delivered 15.7% higher output token throughput than EKS using a standard HTTP load balancer, translating to roughly 1,000 additional tokens per second on the tested setup. This uplift in GKE inference performance can enable higher concurrency or reduced hardware requirements for equivalent loads. Latency gains were even more striking. Time to first token (TTFT) was 92.8% lower on GKE, with mean TTFT more than 2,000 milliseconds shorter than on EKS—an improvement that users will perceive immediately in interactive chat and streaming applications. Inter-token latency (ITL) was 62.6% lower on GKE, contributing to smoother, faster token streaming. Tail behavior improved as well, with up to 83.9% lower 95th-percentile tail latency and a 67.0% lower 95th-percentile normalized time per output token.
Why GKE Wins: Inference-Aware Gateway and Prefix-Cache Optimization
The headline numbers reflect architectural differences rather than raw compute power. According to Principled Technologies, GKE’s advantage stems from the inference-aware optimizations built into the GKE Inference Gateway. A standout feature is prefix-cache-aware routing, which steers requests that share context or prompt prefixes to the same model replica. This design maximizes cache hits, reducing redundant computation and improving utilization of GPU accelerators. The result is both higher throughput and lower end-to-end latency under realistic load. By contrast, the EKS configuration relied on a standard HTTP load balancer that is unaware of model context, treating inference traffic like generic web requests. For workloads such as multi-turn conversations, template-based generation, retrieval-augmented generation (RAG), and document Q&A—where requests often share long prefixes—this inference gateway optimization can translate directly into faster responses and more predictable performance.
Enterprise Implications: Designing for Production-Grade AI Inference
For enterprises moving generative AI from experiments into production, the benchmark underscores how infrastructure choices shape user experience and cost profiles. A 15.7% throughput gain and double-digit reductions in multiple latency metrics on identical GPUs indicate that platform-level decisions can meaningfully influence capacity planning. Lower TTFT improves perceived responsiveness for customer-facing agents, while better inter-token and tail latency reduce the risk of slow or stalled responses under peak load. At the same time, GKE’s native Inference Gateway can simplify architecture by embedding inference-specific routing and caching logic into the Kubernetes layer rather than forcing teams to build custom gateways atop generic load balancers. Principled Technologies recommends that organizations whose workloads benefit from shared prefixes and cache locality consider GKE with Inference Gateway to improve responsiveness, stability, and overall efficiency in production AI inference deployments.