sickn33/antigravity-awesome-skills/cost-optimization

Bundled upstream support

Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.

Supporting material availability

These paths remain unavailable in this catalog copy:

• <support-root>/assets/cost-analysis-template.xlsx

Do not invoke a step that requires one of these files. Obtain the original material and review its dependencies, license, and effects before use. Independent inline steps and the bundled helpers can proceed within the user’s authorized scope.

Cloud Cost Optimization

Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP.

Do not use this skill when

• The task is unrelated to cloud cost optimization
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.

Purpose

Implement systematic cost optimization strategies to reduce cloud spending while maintaining performance and reliability.

Use this skill when

• Reduce cloud spending
• Right-size resources
• Implement cost governance
• Optimize multi-cloud costs
• Meet budget constraints

Evidence and irreversible commitments

All discount percentages/terms and example instance sizes below are historical illustrations, not current quotes or workload recommendations. Verify provider/region/service eligibility and actual effective utilization before buying commitments; reservations/discount plans are financial commitments with changing exchange/cancellation rules. Use read-only billing/utilization first, compare equal workload/reliability windows and include egress, storage minimums, retrieval, taxes and idle costs. Deletion/lifecycle expiry, downsizing and alert subscriptions require exact-target authorization and tested recovery. Budget alerts are notifications, not a spending cap; serverless can retain baseline/storage/provisioned costs.

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

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

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
• Multi-cloud: CloudHealth, Cloudability, Kubecost

Reference Files

• references/tagging-standards.md - Tagging conventions
• assets/cost-analysis-template.xlsx - Cost analysis spreadsheet

Related Skills

• terraform-module-library - For resource provisioning
• multi-cloud-architecture - For cloud selection

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.