How cloud-native tooling is enabling distributed AI inference on heterogeneous edge hardware, slashing latency and infrastructure costs for production workloads. Forward-thinking platform teams are moving AI inference out of centralized GPU data centers and into distributed Kubernetes clusters running closer to data sources, cutting response latency from hundreds of milliseconds to single digits. Mature cloud-native tooling including KServe, vLLM, and eBPF-based observability has made this...