From Cold Starts to Cost Savings: Elastic GPU Inference on Kubernetes

GPU infrastructure costs remain a critical bottleneck for inference-heavy applications, especially when traffic patterns are unpredictable. This architecture combines Karpenter, KEDA, and Dragonfly on EKS to achieve both cost efficiency through scale-to-zero and fast performance with 84-second cold starts, eliminating the traditional cost-performance tradeoff. Teams running LLM APIs, video processing, and embedding services can now deploy production-grade GPU resource management using fully reproducible GitOps-driven infrastructure.