Cost Optimization

When to use this skill

Use Cost Optimization when an AI agent needs a reusable SKILL.md workflow for this job: 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.

When not to use it

Skip Cost Optimization when the task is outside the analysis category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/cost-optimization/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/cost-optimization/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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-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

```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 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 - 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 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

```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

Intended uses

• Reduce cloud spending
• Right-size resources
• Implement cost governance
• Optimize multi-cloud costs
• Meet budget constraints