cost-optimization

name cost-optimization description Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies. Cloud Cost Optimization Strategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI. Purpose Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability. When to Use Reduce cloud spending Right-size resources Implement cost governance Optimize multi-cloud costs Meet budget constraints Cost Optimization Framework 1. Visibility Implement cost allocation tags Use cloud cost management tools Set up budget alerts Create cost dashboards 2. Right-Sizing Analyze resource utilization Downsize over-provisioned resources Use auto-scaling Remove idle resources 3. Pricing Models Use reserved capacity Leverage spot/preemptible instances Implement savings plans Use committed use discounts 4. Architecture Optimization Use managed services Implement caching Optimize data transfer Use lifecycle policies AWS Cost Optimization Reserved Instances Savings: 30-72% vs On-Demand Term: 1 or 3 years Payment: All/Partial/No upfront Flexibility: Standard or Convertible Savings Plans Compute Savings Plans: 66% savings EC2 Instance Savings Plans: 72% savings Applies to: EC2, Fargate, Lambda Flexible across: Instance families, regions, OS Spot Instances Savings: Up to 90% vs On-Demand Best for: Batch jobs, CI/CD, stateless workloads Risk: 2-minute interruption notice Strategy: Mix with On-Demand for resilience S3 Cost Optimization resource "aws_s3_bucket_lifecycle_configuration" "example" { bucket = aws_s3_bucket.example.id rule { id = "transition-to-ia" status = "Enabled" transition { days = 30 storage_class = "STANDARD_IA" } transition { days = 90 storage_class = "GLACIER" } expiration { days = 365 } } } Azure Cost Optimization Reserved VM Instances 1 or 3 year terms Up to 72% savings Flexible sizing Exchangeable Azure Hybrid Benefit Use existing Windows Server licenses Up to 80% savings with RI Available for Windows and SQL Server Azure Advisor Recommendations Right-size VMs Delete unused resources Use reserved capacity Optimize storage GCP Cost Optimization Committed Use Discounts 1 or 3 year commitment Up to 57% savings Applies to vCPUs and memory Resource-based or spend-based Sustained Use Discounts Automatic discounts Up to 30% for running instances No commitment required Applies to Compute Engine, GKE Preemptible VMs Up to 80% savings 24-hour maximum runtime Best for batch workloads OCI Cost Optimization Flexible Shapes Scale OCPUs and memory independently Match instance sizing to workload demand Reduce wasted capacity from fixed VM shapes Commitments and Budgets Use annual commitments for predictable spend Set compartment-level budgets with alerts Track monthly forecasts with OCI Cost Analysis Preemptible Capacity Use preemptible instances for batch and ephemeral workloads Keep interruption-tolerant autoscaling groups Mix with standard capacity for critical services Tagging Strategy AWS Tagging locals { common_tags = { Environment = "production" Project = "my-project" CostCenter = "engineering" Owner = "team@example.com" ManagedBy = "terraform" } } resource "aws_instance" "example" { ami = "ami-12345678" instance_type = "t3.medium" tags = merge( local.common_tags, { Name = "web-server" } ) } Reference: See references/tagging-standards.md Cost Monitoring Budget Alerts # AWS Budget resource "aws_budgets_budget" "monthly" { name = "monthly-budget" budget_type = "COST" limit_amount = "1000" limit_unit = "USD" time_period_start = "2024-01-01_00:00" time_unit = "MONTHLY" notification { comparison_operator = "GREATER_THAN" threshold = 80 threshold_type = "PERCENTAGE" notification_type = "ACTUAL" subscriber_email_addresses = ["team@example.com"] } } Cost Anomaly Detection AWS Cost Anomaly Detection Azure Cost Management alerts GCP Budget alerts OCI Budgets and Cost Analysis Architecture Patterns Pattern 1: Serverless First Use Lambda/Functions for event-driven Pay only for execution time Auto-scaling included No idle costs Pattern 2: Right-Sized Databases Development: t3.small RDS Staging: t3.large RDS Production: r6g.2xlarge RDS with read replicas Pattern 3: Multi-Tier Storage Hot data: S3 Standard Warm data: S3 Standard-IA (30 days) Cold data: S3 Glacier (90 days) Archive: S3 Deep Archive (365 days) Pattern 4: Auto-Scaling resource "aws_autoscaling_policy" "scale_up" { name = "scale-up" scaling_adjustment = 2 adjustment_type = "ChangeInCapacity" cooldown = 300 autoscaling_group_name = aws_autoscaling_group.main.name } resource "aws_cloudwatch_metric_alarm" "cpu_high" { alarm_name = "cpu-high" comparison_operator = "GreaterThanThreshold" evaluation_periods = "2" metric_name = "CPUUtilization" namespace = "AWS/EC2" period = "60" statistic = "Average" threshold = "80" alarm_actions = [aws_autoscaling_policy.scale_up.arn] } Cost Optimization Checklist Implement cost allocation tags Delete unused resources (EBS, EIPs, snapshots) Right-size instances based on utilization Use reserved capacity for steady workloads Implement auto-scaling Optimize storage classes Use lifecycle policies Enable cost anomaly detection Set budget alerts Review costs weekly Use spot/preemptible instances Optimize data transfer costs Implement caching layers Use managed services Monitor and optimize continuously Tools AWS: Cost Explorer, Cost Anomaly Detection, Compute Optimizer Azure: Cost Management, Advisor GCP: Cost Management, Recommender OCI: Cost Analysis, Budgets, Cloud Advisor Multi-cloud: CloudHealth, Cloudability, Kubecost Related Skills terraform-module-library - For resource provisioning multi-cloud-architecture - For cloud selection