Notre avis
Ce skill fournit des stratégies et des modèles pour optimiser les coûts cloud sur AWS, Azure et GCP, en se concentrant sur le redimensionnement des ressources, les modèles de tarification et la gouvernance des coûts.
Points forts
- Couverture multi-cloud (AWS, Azure, GCP) avec des conseils spécifiques à chaque fournisseur.
- Approche structurée avec un framework en quatre étapes : visibilité, redimensionnement, modèles de prix et architecture.
- Exemples concrets de code Terraform et de configuration (cycle de vie S3, balisage).
- Intègre des stratégies avancées comme les instances spot, les réservations et les plans d'épargne.
Limites
- Ne fournit pas d'analyse financière automatisée ; nécessite des données d'utilisation existantes.
- Les exemples sont principalement orientés AWS, avec moins de détails pour Azure et GCP.
- Les recommandations sont génériques et peuvent nécessiter une adaptation à des environnements spécifiques.
Utilisez ce skill lorsque vous devez réduire les dépenses cloud, analyser les coûts d'infrastructure ou mettre en œuvre une gouvernance des coûts.
Ne l'utilisez pas si la tâche est sans rapport avec l'optimisation des coûts cloud ou si elle nécessite des domaines spécialisés comme la sécurité ou l'architecture avancée.
Analyse de sécurité
SûrThe skill provides advisory cloud cost optimization guidance and Terraform examples. It contains no executable commands, destructive actions, or data exfiltration risks.
Aucun point d'attention détecté
Exemples
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
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