A comprehensive retrospective on Kubernetes autoscaling evolution from 2016 to 2025—covering the current landscape, emerging patterns, cost optimization strategies, and future trends.
KEDA 2.12 delivers new scalers, improved performance, enhanced security features, and better integration with Kubernetes autoscaling for event-driven workloads.
KEDA 2.11 delivers new scalers, improved performance, enhanced security features, and better integration with Kubernetes autoscaling for event-driven workloads.
KEDA 2.10 introduces new scalers, improved HTTP add-on, and better observability for dynamic autoscaling in Kubernetes.
Hard-won lessons from running autoscaling in production—covering right-sizing, metrics selection, stabilization windows, observability, and common failure modes.
Orchestrating Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler together for comprehensive Kubernetes autoscaling—covering coordination strategies, conflict avoidance, and cost optimization.
Kubernetes 1.19 graduates HPA v2 to GA, marking the maturity of custom metrics autoscaling. This post covers production patterns, metrics pipelines, and real-world deployment strategies.
Kubernetes 1.12 introduces HPA v2beta2 with stable custom metrics support, enabling autoscaling on application metrics, queue depth, and cloud service metrics beyond CPU and memory.
Kubernetes 1.12 graduates kubelet TLS bootstrap and Azure VMSS to GA, introduces RuntimeClass, volume snapshot alpha, and major autoscaling improvements for large clusters.
A look at the first production-ready autoscaling stack for Kubernetes—covering Cluster Autoscaler and Horizontal Pod Autoscaler v2.