Does this skill cover all four major cloud providers?

Yes. The skill includes specific cost optimization strategies for AWS, Azure, GCP, and OCI, with concrete examples like reserved instances, savings plans, and committed use discounts for each.

What should I focus on first when optimizing cloud costs?

Start with visibility by implementing cost allocation tags and setting up cost dashboards, then move to right-sizing resources based on utilization analysis and removing idle resources.

Does this skill include infrastructure-as-code examples?

Yes. The skill provides Terraform and HCL examples for S3 lifecycle policies, budget alerts, auto-scaling policies, and tagging strategies across AWS.

Can I use this for multi-cloud cost optimization?

Yes. The skill covers cost optimization patterns across AWS, Azure, GCP, and OCI separately, and references multi-cloud tools like CloudHealth and Cloudability for unified visibility.

Does this cover reserved instances and spot/preemptible instances?

Yes. It details reserved capacity (with specific savings percentages like 30-72% for AWS), savings plans, and spot/preemptible instances (up to 90% savings for AWS spot, 80% for GCP preemptible).

cost-optimization by wshobson

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