Configure autoscaling for Kubernetes, VMs, and serverless workloads based on metrics, schedules, and custom indicators.

Autoscaling Configuration Table of Contents [Overview]( overview) [When to Use]( when to use) [Quick Start]( quick start) [Reference Guides]( reference guides) [Best Practices]( best practices) Overview Implement autoscaling strategies to automatically adjust resource capacity based on demand, ensuring cost efficiency while maintaining performance and availability. When to Use Traffic driven workload scaling Time based scheduled scaling Resource utilization optimization Cost reduction High traffic event handling Batch processing optimization Database connection pooling Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents [Kubernetes Horizontal Pod Autoscaler](references/kubernetes horizontal pod autoscaler.md) Kubernetes Horizontal Pod Autoscaler [AWS Auto Scaling](references/aws auto scaling.md) AWS Auto Scaling [Custom Metrics Autoscaling](references/custom metrics autoscaling.md) Custom Metrics Autoscaling [Autoscaling Script](references/autoscaling script.md) Autoscaling Script [Monitoring Autoscaling](references/monitoring autoscaling.md) Monitoring Autoscaling Best Practices ✅ DO Set appropriate min/max replicas Monitor metric aggregation window Implement cooldown periods Use multiple metrics Test scaling behavior Monitor scaling events Plan for peak loads Implement fallback strategies ❌ DON'T Set min replicas to 1 Scale too aggressively Ignore cooldown periods Use single metric only Forget to test scaling Scale below resource needs Neglect monitoring Deploy without capacity tests

autoscaling-configuration