Our review
This skill provides strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP, focusing on resource right-sizing, pricing models, and cost governance.
Strengths
- Multi-cloud coverage (AWS, Azure, GCP) with specific guidance for each provider.
- Structured approach with a four-step framework: visibility, right-sizing, pricing models, and architecture.
- Concrete code examples using Terraform and configuration (S3 lifecycle, tagging).
- Includes advanced strategies such as spot instances, reservations, and savings plans.
Limitations
- Does not provide automated financial analysis; requires existing usage data.
- Examples are mainly AWS-oriented, with less detail for Azure and GCP.
- Recommendations are generic and may need adaptation to specific environments.
Use this skill when you need to reduce cloud spending, analyze infrastructure costs, or implement cost governance.
Do not use this skill if the task is unrelated to cloud cost optimization or if it requires specialized domains like security or advanced architecture.
Security analysis
SafeThe skill provides advisory cloud cost optimization guidance and Terraform examples. It contains no executable commands, destructive actions, or data exfiltration risks.
No concerns found
Examples
Analyze my AWS account usage and suggest ways to reduce costs using reserved instances, spot instances, and right-sizing recommendations.Recommend a strategy for Azure reserved VM instances including hybrid benefit options and sizing flexibility to maximize savings.I want to reduce my GCP Compute Engine costs. Suggest how to leverage committed use discounts and sustained use discounts with example configurations.name: cost-optimization description: "Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing c..." metadata: author: ncdevshiv version: "1.0" category: other updated: 2026-02-25 risk: unknown source: community
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 conventionsassets/cost-analysis-template.xlsx- Cost analysis spreadsheet
Related Skills
terraform-module-library- For resource provisioningmulti-cloud-architecture- For cloud selection
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