Amazon EKS just cranked its Kubernetes cluster limit to 100,000 nodes—a 10x jump. The secret sauce? A reworked etcd with an internal journal system and in-memory storage. Toss in tight API server tuning and network tweaks, and the result is wild: 500 pods per second, 900K pods, 10M+ objects, no sweat—even under real AI/ML load.
What changed: This blows up the old playbook. Instead of juggling multiple clusters for scale, teams can now run massive ML workloads on a single, packed control plane. Fewer moving parts. Fewer headaches. More actual work done.
