Optimize Cloud Spending Across AWS, Azure, and GCP

This skill implements systematic cloud cost optimization across AWS, Azure, and GCP while preserving performance and reliability. Its framework covers visibility (cost allocation tags, cost management tools, budget alerts, dashboards), right-sizing (utilization analysis, downsizing over-provisioned resources, auto-scaling, removing idle resources), pricing models (reserved capacity, spot/preemptible instances, savings plans, committed use discounts), and architecture optimization (managed services, caching, data-transfer optimization, lifecycle policies). Per cloud: AWS Reserved Instances save 30-72% over 1- or 3-year terms, Compute and EC2 Instance Savings Plans save 66% and 72% respectively across EC2/Fargate/Lambda, and Spot Instances save up to 90% for batch, CI/CD, or stateless workloads that tolerate a 2-minute interruption notice, alongside an S3 lifecycle configuration transitioning objects to Standard-IA at 30 days and Glacier at 90. Azure offers Reserved VM Instances up to 72% savings, Azure Hybrid Benefit up to 80% savings by reusing existing Windows Server/SQL Server licenses, and Advisor recommendations for right-sizing and unused-resource cleanup. GCP offers Committed Use Discounts up to 57% (resource- or spend-based), automatic Sustained Use Discounts up to 30% with no commitment on Compute Engine and GKE, and Preemptible VMs up to 80% savings for batch workloads with a 24-hour maximum runtime.

Use it to reduce cloud spending, right-size resources, implement cost governance, optimize multi-cloud costs, or meet a budget constraint. It does not apply outside cloud cost optimization - a different domain or tool is out of scope. It pairs with a separate terraform-module-library skill for the resource provisioning itself and a multi-cloud-architecture skill for cloud selection decisions.

Inputs: current resource utilization and pricing data across AWS, Azure, and/or GCP, and a target budget or savings goal. Outputs: cost-allocation tag schemes (Terraform locals merged into resource tags), a budget alert (an aws_budgets_budget with a percentage-threshold email notification), architecture patterns - serverless-first for event-driven workloads, environment-sized RDS instances, a multi-tier S3 storage lifecycle (Standard to Standard-IA to Glacier to Deep Archive), and CloudWatch-driven auto-scaling policies - and a 15-item cost optimization checklist covering tagging, unused-resource cleanup, right-sizing, reserved capacity, auto-scaling, storage classes, anomaly detection, and budget alerts.

AWS Cost Explorer, Cost Anomaly Detection, and Compute Optimizer; Azure Cost Management and Advisor; GCP Cost Management and Recommender; multi-cloud tools CloudHealth, Cloudability, and Kubecost; and Terraform for provisioning tagged, lifecycle-managed, auto-scaling resources. A bundled references/tagging-standards.md covers tagging conventions and assets/cost-analysis-template.xlsx provides a cost analysis spreadsheet.

resource "aws_budgets_budget" "monthly" {
  name              = "monthly-budget"
  budget_type       = "COST"
  limit_amount      = "1000"
  limit_unit        = "USD"
}

Platform and finance-ops teams reducing AWS, Azure, or GCP spend through right-sizing, reserved or spot pricing, storage lifecycle policies, and budget governance.