Node Feature Discovery is widely deployed across Kubernetes to turn hardware and system capabilities into labels consumed by schedulers, operators, and platform teams. As data centers add more devices and expose more features per node, NFD must publish more data, more often, across more nodes. That creates a multiplicative control-plane cost: nodes × object size × churn. We show how we profiled that cost and redesigned the path: moving built-in label computation to nfd-worker, publishing the resulting labels in NodeFeature.spec.labels, and adding noPublishFeatures to omit raw features. The result: 50% lower nfd-master memory use and an 80% smaller NFD footprint in etcd, with headroom that grows with node count. We also explain why cluster-scoped NodeFeatureRules remain centrally evaluated and extract reusable patterns for building scalable Kubernetes controllers.