Cost Optimization

by wshobson46891e7e60daNo licenseListed Oct 8, 2026Updated Oct 8, 2026

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.

AI-generated overview

Guides cloud cost optimization across AWS, Azure, GCP and OCI with rightsizing, tagging, pricing models and budgets.

What it does
This skill provides a structured framework for reducing cloud spending across AWS, Azure, GCP and OCI. It covers visibility and cost allocation tagging, resource rightsizing, reserved and committed pricing models, spot and preemptible instances, storage lifecycle policies, budget alerts and anomaly detection. It also includes architecture patterns such as serverless, right-sized databases, multi-tier storage and auto-scaling, plus a cost optimization checklist and a list of provider cost tools.
When to use it
Use it when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance and budget policies. It also fits multi-cloud cost reviews and meeting budget constraints.
Requirements
Instructions only; no scripts are shipped. It references a tagging standards document in the skill folder. Applying the guidance assumes access to cloud provider consoles, cost management tools and infrastructure-as-code tooling such as Terraform.

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-DemandTerm: 1 or 3 yearsPayment: All/Partial/No upfrontFlexibility: Standard or Convertible

Savings Plans

Compute Savings Plans: 66% savingsEC2 Instance Savings Plans: 72% savingsApplies to: EC2, Fargate, LambdaFlexible across: Instance families, regions, OS

Spot Instances

Savings: Up to 90% vs On-DemandBest for: Batch jobs, CI/CD, stateless workloadsRisk: 2-minute interruption noticeStrategy: Mix with On-Demand for resilience

S3 Cost Optimization

hcl
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

hcl
locals {  common_tags = {    Environment = "production"    Project     = "my-project"    CostCenter  = "engineering"    Owner       = "[email protected]"    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

hcl
# AWS Budgetresource "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 = ["[email protected]"]  }}

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 RDSStaging: t3.large RDSProduction: r6g.2xlarge RDS with read replicas

Pattern 3: Multi-Tier Storage

Hot data: S3 StandardWarm data: S3 Standard-IA (30 days)Cold data: S3 Glacier (90 days)Archive: S3 Deep Archive (365 days)

Pattern 4: Auto-Scaling

hcl
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

Source and attribution

Source:wshobson/agentsinplugins/cloud-infrastructure/skills/cost-optimizationat commit46891e7

License: No license

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