AWS Cost Optimization Skill for Claude

AWS Cost Optimization & FinOps

Systematic workflows for AWS cost optimization and financial operations management.

Cost Optimization Workflow

• Discover — Find waste using aws ec2 and aws ce CLI commands to identify unused resources and cost anomalies
• Analyze — Find opportunities with aws compute-optimizer, aws cloudwatch, and aws ce for rightsizing, generation upgrades, Spot, and RI recommendations
• Prioritize — Quick wins (low risk, high savings) first, then low-hanging fruit, then strategic improvements
• Implement — Delete unused resources, rightsize instances, purchase commitments, migrate generations
• Monitor — Monthly cost reviews, tag compliance, budget variance tracking

Core Workflows

Workflow 1: Monthly Cost Optimization Review

Frequency: Run monthly (first week of each month)

Step 1: Find Unused Resources

# Find unattached EBS volumes
aws ec2 describe-volumes --filters Name=status,Values=available \
  --query 'Volumes[].{ID:VolumeId,Size:Size,Created:CreateTime}' --output table

# Find unused Elastic IPs
aws ec2 describe-addresses --filters Name=domain,Values=vpc \
  --query 'Addresses[?AssociationId==null].{IP:PublicIp,AllocationId:AllocationId}' --output table

# Find idle EC2 instances (low CPU over 14 days)
aws cloudwatch get-metric-statistics --namespace AWS/EC2 \
  --metric-name CPUUtilization --period 86400 --statistics Average \
  --start-time $(date -u -v-14d +%Y-%m-%dT%H:%M:%S) --end-time $(date -u +%Y-%m-%dT%H:%M:%S) \
  --dimensions Name=InstanceId,Value=INSTANCE_ID

# Find old snapshots (>90 days)
aws ec2 describe-snapshots --owner-ids self \
  --query 'Snapshots[?StartTime<=`2025-01-01`].{ID:SnapshotId,Size:VolumeSize,Date:StartTime}' --output table

# Find unused load balancers (no healthy targets)
aws elbv2 describe-target-health --target-group-arn TARGET_GROUP_ARN

Step 2: Analyze Cost Anomalies

# Get daily costs for the past 30 days
aws ce get-cost-and-usage \
  --time-period Start=$(date -u -v-30d +%Y-%m-%d),End=$(date -u +%Y-%m-%d) \
  --granularity DAILY --metrics BlendedCost \
  --group-by Type=DIMENSION,Key=SERVICE

# Get cost forecast for the next 30 days
aws ce get-cost-forecast \
  --time-period Start=$(date -u +%Y-%m-%d),End=$(date -u -v+30d +%Y-%m-%d) \
  --granularity MONTHLY --metric BLENDED_COST

# Compare month-over-month costs
aws ce get-cost-and-usage \
  --time-period Start=$(date -u -v-60d +%Y-%m-01),End=$(date -u +%Y-%m-%d) \
  --granularity MONTHLY --metrics BlendedCost

Step 3: Identify Rightsizing Opportunities

# Get Compute Optimizer rightsizing recommendations
aws compute-optimizer get-ec2-instance-recommendations \
  --query 'instanceRecommendations[].{Instance:instanceArn,Finding:finding,Current:currentInstanceType,Recommended:recommendationOptions[0].instanceType}'

# Check CPU utilization for a specific instance (past 30 days)
aws cloudwatch get-metric-statistics --namespace AWS/EC2 \
  --metric-name CPUUtilization --period 3600 --statistics Average Maximum \
  --start-time $(date -u -v-30d +%Y-%m-%dT%H:%M:%S) --end-time $(date -u +%Y-%m-%dT%H:%M:%S) \
  --dimensions Name=InstanceId,Value=INSTANCE_ID

# Get RDS rightsizing recommendations
aws compute-optimizer get-ecs-service-recommendations 2>/dev/null || \
  echo "Check RDS CPU/memory in CloudWatch manually"

Step 4: Generate Monthly Report

# Use the template to compile findings
cp assets/templates/monthly_cost_report.md reports/$(date +%Y-%m)-cost-report.md

# Fill in:
# - Findings from AWS CLI analysis
# - Action items
# - Team cost breakdowns
# - Optimization wins

Step 5: Team Review Meeting

• Present findings to engineering teams
• Assign optimization tasks
• Track action items to completion

Workflow 2: Commitment Purchase Analysis (RI/Savings Plans)

When: Quarterly or when usage patterns stabilize

Step 1: Analyze Current Usage

# Get EC2 RI purchase recommendations
aws ce get-reservation-purchase-recommendation --service "Amazon Elastic Compute Cloud - Compute" \
  --lookback-period-in-days SIXTY_DAYS --term-in-years ONE_YEAR --payment-option NO_UPFRONT

# Get RDS RI purchase recommendations
aws ce get-reservation-purchase-recommendation --service "Amazon Relational Database Service" \
  --lookback-period-in-days SIXTY_DAYS --term-in-years ONE_YEAR --payment-option NO_UPFRONT

# Get Savings Plans recommendations
aws ce get-savings-plans-purchase-recommendation \
  --savings-plans-type COMPUTE_SP --lookback-period-in-days SIXTY_DAYS \
  --term-in-years ONE_YEAR --payment-option NO_UPFRONT

Step 2: Review Recommendations

Evaluate each recommendation:

✅ Good candidate if:
  - Running 24/7 for 60+ days
  - Workload is stable and predictable
  - No plans to change architecture
  - Savings > 30%

❌ Poor candidate if:
  - Workload is variable or experimental
  - Architecture changes planned
  - Instance type may change
  - Dev/test environment

Step 3: Choose Commitment Type

Reserved Instances:

• Standard RI: Highest discount (63%), no flexibility
• Convertible RI: Moderate discount (54%), can change instance type
• Best for: Specific instance types, stable workloads

Savings Plans:

• Compute SP: Flexible across instance types, regions (66% savings)
• EC2 Instance SP: Flexible across sizes in same family (72% savings)
• Best for: Variable workloads within constraints

Decision Matrix:

Known instance type, won't change → Standard RI
May need to change types → Convertible RI or Compute SP
Variable workloads → Compute Savings Plan
Maximum flexibility → Compute Savings Plan

Step 4: Purchase and Track

• Purchase through AWS Console or CLI
• Tag commitments with purchase date and owner
• Monitor utilization monthly
• Aim for >90% utilization

Reference: See references/best_practices.md for detailed commitment strategies

Workflow 3: Instance Generation Migration

When: During architecture reviews or optimization sprints

Step 1: Detect Old Instances

# Find old-generation instances (t2, m4, c4, r4, etc.)
aws ec2 describe-instances --filters Name=instance-state-name,Values=running \
  --query 'Reservations[].Instances[?starts_with(InstanceType, `t2.`) || starts_with(InstanceType, `m4.`) || starts_with(InstanceType, `c4.`) || starts_with(InstanceType, `r4.`)].{ID:InstanceId,Type:InstanceType,Name:Tags[?Key==`Name`]|[0].Value}' \
  --output table

# Find all running instance types to review generations
aws ec2 describe-instances --filters Name=instance-state-name,Values=running \
  --query 'Reservations[].Instances[].{ID:InstanceId,Type:InstanceType,Name:Tags[?Key==`Name`]|[0].Value}' \
  --output table
# Look for: t2→t3, m4→m6i, c4→c6i, r4→r6i, Intel→Graviton (20% savings)

Step 2: Prioritize Migrations

Quick Wins (Low Risk):

t2 → t3: Drop-in replacement, 10% savings
m4 → m5: Better performance, 5% savings
gp2 → gp3: No downtime, 20% savings

Medium Effort (Test Required):

x86 → Graviton (ARM64): 20% savings
- Requires ARM64 compatibility testing
- Most modern frameworks support ARM64
- Test in staging first

Step 3: Execute Migration

For EC2 (x86 to x86):

• Stop instance
• Change instance type
• Start instance
• Verify application

For Graviton Migration:

• Create ARM64 AMI or Docker image
• Launch new Graviton instance
• Test thoroughly
• Cut over traffic
• Terminate old instance

Step 4: Validate Savings

• Monitor new costs in Cost Explorer
• Verify performance is acceptable
• Document migration for other teams

Reference: See references/best_practices.md → Compute Optimization

Workflow 4: Spot Instance Evaluation

When: For fault-tolerant workloads or Auto Scaling Groups

Step 1: Identify Candidates

# Check current Spot pricing vs On-Demand for target instance types
aws ec2 describe-spot-price-history --instance-types m5.large m5.xlarge m6i.large \
  --product-descriptions "Linux/UNIX" --start-time $(date -u +%Y-%m-%dT%H:%M:%S) \
  --query 'SpotPriceHistory[].{Type:InstanceType,AZ:AvailabilityZone,Price:SpotPrice}' --output table

# List Auto Scaling Groups (good Spot candidates)
aws autoscaling describe-auto-scaling-groups \
  --query 'AutoScalingGroups[].{Name:AutoScalingGroupName,Min:MinSize,Max:MaxSize,Desired:DesiredCapacity}' --output table

# Check which ASGs already use mixed instances (Spot)
aws autoscaling describe-auto-scaling-groups \
  --query 'AutoScalingGroups[?MixedInstancesPolicy!=null].AutoScalingGroupName'

Spot candidates: instances in ASGs, dev/test/staging environments, batch processing, CI/CD servers.

Step 2: Assess Suitability

Excellent for Spot:

• Stateless applications
• Batch jobs
• CI/CD pipelines
• Data processing
• Auto Scaling Groups

NOT suitable for Spot:

• Databases (without replicas)
• Stateful applications
• Real-time services
• Mission-critical workloads

Step 3: Implementation Strategy

Option 1: Fargate Spot -- Set capacityProviderStrategy with FARGATE_SPOT weight 70, FARGATE weight 30.

Option 2: EC2 Auto Scaling with Spot -- Use MixedInstancesPolicy with OnDemandBaseCapacity: 2, OnDemandPercentageAboveBaseCapacity: 30, SpotAllocationStrategy: capacity-optimized, and multiple instance type overrides (m5.large, m5a.large, m5n.large).

Option 3: EC2 Spot Fleet -- aws ec2 request-spot-fleet --spot-fleet-request-config file://spot-fleet.json

Step 4: Implement Interruption Handling -- Poll instance metadata at /latest/meta-data/spot/instance-action for the 2-minute termination notice. On notice: gracefully shutdown, save state, drain connections, exit.

Reference: See references/best_practices.md → Compute Optimization → Spot Instances

Quick Reference

Use AWS CLI commands directly -- Claude Code can run aws ce, aws ec2, aws cloudwatch, and aws compute-optimizer commands to perform cost analysis.

Monthly review commands:

• aws ec2 describe-volumes --filters Name=status,Values=available (unused volumes)
• aws ce get-cost-and-usage --granularity DAILY --metrics BlendedCost (cost trends)
• aws compute-optimizer get-ec2-instance-recommendations (rightsizing)

Quarterly optimization commands:

• aws ce get-reservation-purchase-recommendation --service EC2 (RI analysis)
• aws ec2 describe-instances with instance type filters (old generations)
• aws ec2 describe-spot-price-history (Spot pricing)

Add --region REGION and --profile PROFILE to any AWS CLI command as needed.

Service-Specific Optimization

Compute Optimization

Key Actions:

• Migrate to Graviton (20% savings)
• Use Spot for fault-tolerant workloads (70% savings)
• Purchase RIs for stable workloads (40-65% savings)
• Right-size oversized instances

Reference: references/best_practices.md → Compute Optimization

Storage Optimization

Key Actions:

• Convert gp2 → gp3 (20% savings)
• Implement S3 lifecycle policies (50-95% savings)
• Delete old snapshots
• Use S3 Intelligent-Tiering

Reference: references/best_practices.md → Storage Optimization

Network Optimization

Key Actions:

• Replace NAT Gateways with VPC Endpoints (save $25-30/month each)
• Use CloudFront to reduce data transfer costs
• Colocate resources in same AZ when possible

Reference: references/best_practices.md → Network Optimization

Database Optimization

Key Actions:

• Right-size RDS instances
• Use gp3 storage (20% cheaper than gp2)
• Evaluate Aurora Serverless for variable workloads
• Purchase RDS Reserved Instances

Reference: references/best_practices.md → Database Optimization

Service Alternatives Decision Guide

Need help choosing between services?

Question: "Should I use EC2, Lambda, or Fargate?"

Answer: See references/service_alternatives.md → Compute Alternatives

Question: "Which S3 storage class should I use?"

Answer: See references/service_alternatives.md → Storage Alternatives

Question: "Should I use RDS or Aurora?"

Answer: See references/service_alternatives.md → Database Alternatives

Question: "NAT Gateway vs VPC Endpoint vs NAT Instance?"

Answer: See references/service_alternatives.md → Networking Alternatives

FinOps Governance & Process

Three-phase rollout: (1) Foundation -- Enable Cost Explorer, set up Budgets, define tagging strategy. (2) Visibility -- Enforce tags, run AWS CLI cost analysis commands, set up monthly reviews. (3) Culture -- Cost metrics in engineering KPIs, architecture reviews, optimization sprints.

Full guide: references/finops_governance.md

Monthly Review Process

Week 1: Data Collection

• Run AWS CLI cost analysis commands (see Workflow 1)
• Export Cost & Usage Reports
• Compile findings

Week 2: Analysis

• Identify trends
• Find opportunities
• Prioritize actions

Week 3: Team Reviews

• Present to engineering teams
• Discuss optimizations
• Assign action items

Week 4: Executive Reporting

• Create executive summary
• Forecast next quarter
• Report optimization wins

Template: See assets/templates/monthly_cost_report.md

Detailed Process: See references/finops_governance.md → Monthly Review Process

Cost Optimization Checklist

Quick Wins (Do First)

• [ ] Delete unattached EBS volumes
• [ ] Delete old EBS snapshots (>90 days)
• [ ] Release unused Elastic IPs
• [ ] Convert gp2 → gp3 volumes
• [ ] Stop/terminate idle EC2 instances
• [ ] Enable S3 Intelligent-Tiering
• [ ] Set up AWS Budgets and alerts

Medium Effort (This Quarter)

• [ ] Right-size oversized instances
• [ ] Migrate to newer instance generations
• [ ] Purchase Reserved Instances for stable workloads
• [ ] Implement S3 lifecycle policies
• [ ] Replace NAT Gateways with VPC Endpoints (where applicable)
• [ ] Enable automated resource scheduling (dev/test)
• [ ] Implement tagging strategy and enforcement

Strategic Initiatives (Ongoing)

• [ ] Migrate to Graviton instances
• [ ] Implement Spot for fault-tolerant workloads
• [ ] Establish monthly cost review process
• [ ] Set up cost allocation by team
• [ ] Implement chargeback/showback model
• [ ] Create FinOps culture and practices

Troubleshooting Cost Issues

"My bill suddenly increased"

• Check daily cost breakdown by service:

   aws ce get-cost-and-usage \
     --time-period Start=$(date -u -v-30d +%Y-%m-%d),End=$(date -u +%Y-%m-%d) \
     --granularity DAILY --metrics BlendedCost \
     --group-by Type=DIMENSION,Key=SERVICE

• Check Cost Explorer in the AWS Console for visual breakdown
• Review CloudTrail for resource creation events
• Check for AutoScaling events
• Verify no Reserved Instances expired

"I need to reduce costs by X%"

Follow the optimization workflow:

• Run the discovery commands from Workflow 1 above (unused resources, cost anomalies, rightsizing)
• Calculate total potential savings
• Prioritize by: Savings Amount x (1 / Effort)
• Focus on quick wins first
• Implement strategic changes for long-term

"How do I know if Reserved Instances make sense?"

Run RI analysis:

aws ce get-reservation-purchase-recommendation \
  --service "Amazon Elastic Compute Cloud - Compute" \
  --lookback-period-in-days SIXTY_DAYS --term-in-years ONE_YEAR --payment-option NO_UPFRONT

Look for:

• Instances running 60+ days consistently
• Workloads that won't change
• Savings > 30%

"Which resources can I safely delete?"

Find unused resources:

aws ec2 describe-volumes --filters Name=status,Values=available --output table
aws ec2 describe-addresses --query 'Addresses[?AssociationId==null]' --output table
aws ec2 describe-snapshots --owner-ids self \
  --query 'Snapshots[?StartTime<=`2025-01-01`]' --output table

Safe to delete (usually):

• Unattached EBS volumes (after verifying)
• Snapshots > 90 days (if backups exist elsewhere)
• Unused Elastic IPs (after verifying not in DNS)
• Stopped EC2 instances > 30 days (after confirming abandoned)

Always verify with resource owner before deletion!

Additional Resources

References: references/best_practices.md | references/service_alternatives.md | references/finops_governance.md

Templates: assets/templates/monthly_cost_report.md

CLI Tools: Use AWS CLI commands directly -- Claude Code can run aws ce, aws ec2, aws cloudwatch, and aws compute-optimizer commands to perform cost analysis. No additional dependencies required.