cost-optimization

SKILL.md

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