Cloud Cost Optimization

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

Sby Skills Guide Bot
DevOpsIntermediate
7/27/2026
Claude CodeCursorWindsurfCopilotCodex
#cloud-cost-optimization#finops#aws#azure#gcp

Recommended for

Automated summary

This skill provides a framework and patterns for reducing cloud costs across AWS, Azure, and GCP through right-sizing, commitment discounts, tagging, and budget alerts.

Strengths

  • Covers the three major cloud providers with pricing-model guidance and indicative savings ranges.
  • Offers a structured framework spanning visibility, right-sizing, pricing models, and architecture optimization.
  • Includes Terraform examples for S3 lifecycle policies, tagging, and AWS budgets.
  • Provides concrete guidance on cost governance, tagging strategy, and budget alerts.

Limitations

  • Savings figures and services change quickly, so they must be verified against current pricing.
  • The content is mostly advisory and does not directly integrate with billing APIs or native cost dashboards.
  • The risk level is marked unknown, and some examples are partial or truncated.
When to use it

Use this skill when you need to analyze or reduce a cloud bill, right-size resources, or establish multi-cloud cost governance.

When not to use it

Do not use it for tasks unrelated to cloud cost optimization, or as a substitute for current pricing verification and dedicated FinOps tooling.

Security analysis

Safe
Quality score85/100

The skill contains only informational guidance and configuration examples for cloud cost optimization. It does not include any executable commands, destructive actions, network exfiltration, or obfuscated payloads. No tools are declared or required to run, and the HCL/Terraform snippets are declarative configuration examples, not executed by the skill itself.

No concerns found

How this analysis is made

Examples

AWS cost review
Review our AWS environment for cost savings: find idle and over-provisioned resources, estimate Savings Plan or Reserved Instance coverage, and propose a tagging and budget-alert plan.
Azure commitment options
Our Azure VM spend is too high. Compare Reserved VM Instances and Azure Hybrid Benefit for our Windows and SQL Server workloads, and outline an implementation plan.
Terraform cost controls
Write Terraform for an S3 lifecycle policy plus a shared cost-allocation tagging convention we can apply across AWS accounts.

name: cost-optimization description: "Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP." risk: unknown source: community date_added: "2026-02-27"

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.
  • If detailed examples are required, open resources/implementation-playbook.md.

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

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

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3. Load the skill

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Claude Code
test -f '.skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md' && test -s '.skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/cost-optimization' && cp -R '.skill-downloads/cloud-cost-optimization-ms2s7ch5' '.claude/skills/cost-optimization'

Cursor
test -f '.skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md' && test -s '.skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md' && mkdir -p '.cursor/skills' && test ! -e '.cursor/skills/cost-optimization' && cp -R '.skill-downloads/cloud-cost-optimization-ms2s7ch5' '.cursor/skills/cost-optimization'

Codex
test -f '.skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md' && test -s '.skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md' && mkdir -p '.agents/skills' && test ! -e '.agents/skills/cost-optimization' && cp -R '.skill-downloads/cloud-cost-optimization-ms2s7ch5' '.agents/skills/cost-optimization'

OpenSkills

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Official OpenSkills installation
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Read .skill-downloads/cloud-cost-optimization-ms2s7ch5/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

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