Cloud Cost Optimization

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       = "[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

# 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 = ["[email protected]"]
  }
}

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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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior error resolutions and debugging strategies. The hybrid search excels here — BM25 finds exact error codes/​stack traces while vectors find semantically similar past issues.

# Check for prior debugging/​diagnostics context before starting
python3 execution/​memory_manager.py auto --query "error patterns and debugging solutions for Cost Optimization"

Storing Results

After completing work, store debugging/​diagnostics decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "Root cause: memory leak from unclosed DB connections in pool — fixed with context manager" \
  --type error --project <project> \
  --tags cost-optimization debugging

Multi-Agent Collaboration

Store error resolutions so any agent encountering the same issue retrieves the fix instantly instead of re-debugging.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Debugged and resolved critical issue — root cause documented for future reference" \
  --project <project>

Self-Annealing Loop

When this skill resolves an error, store the fix in memory AND update the relevant directive. The system gets stronger with each resolved issue.

BM25 Exact Match

Error codes, stack traces, and log messages are best found via BM25 keyword search. The hybrid system automatically uses exact matching for these patterns.

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