Cloud Auto-Scaling Cheat Sheet

Updated 2026-05-25

Next Topic: Cloud Auto-Scaling Cheat Sheet

Cloud auto-scaling dynamically adjusts compute resources based on demand, allowing applications to maintain performance during traffic spikes while minimizing costs during low-utilization periods. This capability has evolved from simple threshold-based reactions into sophisticated predictive systems using machine learning that anticipate load changes before they occur. Understanding the distinction between horizontal scaling (adding instances) and vertical scaling (increasing instance size), along with when to apply reactive versus proactive strategies, determines whether your infrastructure scales efficiently or burns budget fighting fires.

What This Cheat Sheet Covers

This topic spans 14 focused tables and 116 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Scaling ApproachesTable 2: Dynamic Scaling PoliciesTable 3: Kubernetes Auto-ScalingTable 4: Platform-Specific Auto-ScalingTable 5: Scaling Policies and ConfigurationTable 6: Metrics and MonitoringTable 7: Advanced Scaling TechniquesTable 8: Database and Serverless Auto-ScalingTable 9: Cost Optimization and RightsizingTable 10: Testing and ValidationTable 11: Health Checks and ReliabilityTable 12: Notifications and ObservabilityTable 13: Common Anti-Patterns and PitfallsTable 14: Implementation and Infrastructure as Code

Table 1: Core Scaling Approaches

The choice between horizontal and vertical scaling, and between reactive and proactive strategies, is the most fundamental decision in any auto-scaling design — get it wrong and you either over-provision constantly or chase demand with lag.

Strategy	Example	Description
Horizontal Scaling (Scale Out)	Add 3 web servers `min=2, max=10`	• Increases capacity by adding more instances to distribute workload • provides fault tolerance and theoretically unlimited scalability but requires stateless design or external session management
Vertical Scaling (Scale Up)	t3.medium → t3.xlarge 2 vCPU → 4 vCPU	• Increases capacity by upgrading to a larger instance type • simpler implementation with no architectural changes but hits hardware limits and typically requires instance replacement
Reactive Scaling	CPU > 70% for 5 min → add instance	• Responds to observed metrics after load increases • simple to configure and prevents over-provisioning, but introduces lag between demand surge and new capacity being ready
Proactive Scaling	Scale at 08:00 daily Based on forecast	• Adds capacity before anticipated load using schedules or ML predictions • eliminates reactive lag but risks over-provisioning if forecasts are inaccurate

Table 1: Core Scaling Approaches

Strategy	Example	Description
Horizontal Scaling (Scale Out)	Add 3 web servers `min=2, max=10`	• Increases capacity by adding more instances to distribute workload • provides fault tolerance and theoretically unlimited scalability but requires stateless design or external session management
Vertical Scaling (Scale Up)	t3.medium → t3.xlarge 2 vCPU → 4 vCPU	• Increases capacity by upgrading to a larger instance type • simpler implementation with no architectural changes but hits hardware limits and typically requires instance replacement
Reactive Scaling	CPU > 70% for 5 min → add instance	• Responds to observed metrics after load increases • simple to configure and prevents over-provisioning, but introduces lag between demand surge and new capacity being ready
Proactive Scaling	Scale at 08:00 daily Based on forecast	• Adds capacity before anticipated load using schedules or ML predictions • eliminates reactive lag but risks over-provisioning if forecasts are inaccurate