Kubernetes autoscaling adjusts resources dynamically based on demand. Horizontal Pod Autoscaler adds or removes pods. Vertical Pod Autoscaler adjusts resource requests. Cluster Autoscaler adds or removes nodes. Combining these capabilities creates responsive, cost-efficient clusters.
HPA Configuration
HPA scales pods based on metrics—CPU, memory, or custom metrics. Configure target utilization balancing performance against cost. Set appropriate min and max replicas preventing under and over-scaling. Use multiple metrics for more sophisticated scaling decisions.
- Start with CPU-based HPA as baseline, add custom metrics as needed
- Set realistic target utilization—80% leaves headroom for bursts
- Configure stabilization windows preventing thrashing during traffic fluctuations
- Use KEDA for event-driven scaling based on queue depths and external metrics
- Test scaling behavior under load before production deployment
VPA and Cluster Autoscaler
VPA adjusts individual pod resource requests based on usage history. Use VPA recommendations to right-size workloads. Cluster Autoscaler provisions nodes when pods can't schedule and removes underutilized nodes. Configure node pools strategically for different workload types.