A comprehensive retrospective on Kubernetes autoscaling evolution from 2016 to 2025—covering the current landscape, emerging patterns, cost optimization strategies, and future trends.
Karpenter 0.32 delivers improved node provisioning algorithms, enhanced cost optimization features, better spot instance management, expanded cloud provider support, and advanced consolidation strategies.
KEDA 2.12 delivers new scalers, improved performance, enhanced security features, and better integration with Kubernetes autoscaling for event-driven workloads.
Karpenter 0.31 delivers improved node provisioning, enhanced cost optimization, better spot instance management, and expanded cloud provider support.
KEDA 2.11 delivers new scalers, improved performance, enhanced security features, and better integration with Kubernetes autoscaling for event-driven workloads.
Predictive autoscaling uses machine learning and historical patterns to pre-scale workloads before traffic arrives, reducing latency and improving cost efficiency compared to reactive scaling.
A comprehensive comparison of Karpenter and Cluster Autoscaler—covering architectural differences, performance, cost optimization, multi-cloud support, and migration strategies.
Karpenter 0.30 delivers node autoscaling improvements, multi-cloud support enhancements, and performance optimizations for dynamic node provisioning.
KEDA 2.10 introduces new scalers, improved HTTP add-on, and better observability for dynamic autoscaling in Kubernetes.
KEDA 2.8 expands autoscaling capabilities with new scalers, improved HTTP add-on, and enhanced observability for event-driven Kubernetes workloads.