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Autoscaling Configuration

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• 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:

# hpa-configuration.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: myapp-hpa
  namespace: production
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: myapp
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

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

Weekly Installs

109

Repository

aj-geddes/usefu…-prompts

GitHub Stars

121

First Seen

Jan 21, 2026

Security Audits

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Installed on

gemini-cli92

opencode92

codex88

cursor85

claude-code84

github-copilot76