AWS Cost Optimization

AWS Cost Optimization & FinOps

Systematic workflows for AWS cost optimization and financial operations management.

Cost Optimization Workflow

1. Discover — Find waste using aws ec2 and aws ce CLI commands to identify unused resources and cost anomalies
2. Analyze — Find opportunities with aws compute-optimizer, aws cloudwatch, and aws ce for rightsizing, generation upgrades, Spot, and RI recommendations
3. Prioritize — Quick wins (low risk, high savings) first, then low-hanging fruit, then strategic improvements
4. Implement — Delete unused resources, rightsize instances, purchase commitments, migrate generations
5. 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):

1. Stop instance
2. Change instance type
3. Start instance
4. Verify application

For Graviton Migration:

1. Create ARM64 AMI or Docker image
2. Launch new Graviton instance
3. Test thoroughly
4. Cut over traffic
5. 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"

1. 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
   

2. Check Cost Explorer in the AWS Console for visual breakdown

3. Review CloudTrail for resource creation events

4. Check for AutoScaling events

5. Verify no Reserved Instances expired

"I need to reduce costs by X%"

Follow the optimization workflow:

1. Run the discovery commands from Workflow 1 above (unused resources, cost anomalies, rightsizing)
2. Calculate total potential savings
3. Prioritize by: Savings Amount x (1 / Effort)
4. Focus on quick wins first
5. 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.