# Multi-Region Cost Optimization Strategy

## Executive Summary

This document outlines comprehensive cost optimization strategies for Mahavishnu's multi-region deployment, targeting **30%+ cost reduction** while maintaining 99.99% availability and performance SLAs.

**Target Savings:**
- Compute: 40% through spot instances and right-sizing
- Database: 35% through reserved instances and intelligent scaling
- Storage: 50% through lifecycle policies and compression
- Network: 25% through data transfer optimization
- Total: **30%+ overall cost reduction**

## 1. Compute Cost Optimization

### 1.1 Spot Instance Utilization

**Strategy:** Use spot instances for fault-tolerant, interruptible workloads

**Target Workloads:**
- Background job processing
- Batch workflow execution
- Report generation
- Data analytics
- Machine learning training

**Implementation:**

```yaml
# EKS node group configuration for spot instances
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig
metadata:
  name: mahavishnu-us-east-1
  region: us-east-1

managedNodeGroups:
  - name: spot-workers
    instanceTypes:
      - m5.xlarge
      - m5.2xlarge
      - c5.xlarge
      - c5.2xlarge
    spot: true
    minSize: 0
    maxSize: 50
    desiredSize: 10
    volumeSize: 100
    labels:
      workload: interruptible
      instance-type: spot

    # Enable capacity-optimized allocation
    capacityType: SPOT
    spotAllocationStrategy: capacity-optimized

    # Graceful shutdown handling
    instanceRefresh:
      strategy: RollingUpdate
      pauseBeforeProgress: 30
```

**Savings: 70-90% vs on-demand instances**

**Best Practices:**
1. Use multiple instance types to increase availability
2. Implement graceful shutdown (Spot Interruption Notices)
3. Use Spot Instance Data Feed for monitoring
4. Combine with On-Demand for base capacity
5. Store intermediate results in durable storage (S3, EFS)

### 1.2 Reserved Instances (RIs) and Savings Plans

**Strategy:** Commit to 1-3 year reservations for predictable baseline workloads

**Compute Reservations:**

```bash
# Purchase convertible RIs for EKS nodes
aws ec2 purchase-reserved-instances-offering \
  --reserved-instances-offering-id $(aws ec2 describe-reserved-instances-offerings \
    --instance-type m5.xlarge \
    --availability-zone us-east-1a \
    --product-description "Linux/UNIX" \
    --tenancy default \
    --offering-type "Convertible" \
    --query 'ReservedInstancesOfferings[0].ReservedInstancesOfferingId' --output text) \
  --instance-count 10 \
  --purchase-offering-type PartialUpfront

# Purchase compute savings plans for flexible capacity
aws compute-optimizer purchase-savings-plan \
  --savings-plan-type ComputeSavingsPlan \
  --hourly-commitment 10.0 \
  --upfront-payment "Partial" \
  --term "OneYear"
```

**RDS Reserved Instances:**

```bash
# Purchase RDS reserved instances
aws rds purchase-reserved-db-instances-offering \
  --reserved-db-instances-offering-id $(aws rds describe-reserved-db-instances-offerings \
    --db-instance-class db.r5.xlarge \
    --multi-az \
    --duration 1 \
    --product-description postgres \
    --offering-type PartialUpfront \
    --query 'ReservedDBInstancesOfferings[0].ReservedDBInstancesOfferingId' --output text) \
  --db-instance-count 3
```

**Savings: 40-60% vs on-demand**

**Best Practices:**
1. Use Convertible RIs for flexibility
2. Purchase Partial Upfront for better cash flow
3. Track RI utilization monthly
4. Use AWS Compute Optimizer for recommendations
5. Sell unused RIs on Reserved Instance Marketplace

### 1.3 Right-Sizing Instances

**Strategy:** Continuously monitor and adjust instance sizes based on actual usage

**Automation Script:**

```python
# scripts/right_size_instances.py

import boto3
import statistics

def get_instance_metrics(instance_id, days=7):
    """Get CPU and memory utilization for instance."""
    cloudwatch = boto3.client('cloudwatch')

    # Get CPU utilization
    cpu_stats = cloudwatch.get_metric_statistics(
        Namespace='AWS/EC2',
        MetricName='CPUUtilization',
        Dimensions=[{'Name': 'InstanceId', 'Value': instance_id}],
        StartTime=datetime.now() - timedelta(days=days),
        EndTime=datetime.now(),
        Period=3600,
        Statistics=['Average', 'Maximum']
    )

    # Get memory utilization (via CloudWatch Agent)
    memory_stats = cloudwatch.get_metric_statistics(
        Namespace='CWAgent',
        MetricName='mem_used_percent',
        Dimensions=[{'Name': 'InstanceId', 'Value': instance_id}],
        StartTime=datetime.now() - timedelta(days=days),
        EndTime=datetime.now(),
        Period=3600,
        Statistics=['Average', 'Maximum']
    )

    return {
        'cpu_avg': statistics.mean([p['Average'] for p in cpu_stats['Datapoints']]),
        'cpu_max': max([p['Maximum'] for p in cpu_stats['Datapoints']]),
        'memory_avg': statistics.mean([p['Average'] for p in memory_stats['Datapoints']]),
        'memory_max': max([p['Maximum'] for p in memory_stats['Datapoints']]),
    }

def recommend_instance_type(metrics, current_type):
    """Recommend right-sized instance type based on metrics."""
    instance_sizing = {
        't3.medium': {'cpu': 2, 'memory': 4},
        't3.large': {'cpu': 2, 'memory': 8},
        'm5.xlarge': {'cpu': 4, 'memory': 16},
        'm5.2xlarge': {'cpu': 8, 'memory': 32},
        'm5.4xlarge': {'cpu': 16, 'memory': 64},
        'c5.xlarge': {'cpu': 4, 'memory': 8},
        'c5.2xlarge': {'cpu': 8, 'memory': 16},
        'r5.xlarge': {'cpu': 4, 'memory': 32},
        'r5.2xlarge': {'cpu': 8, 'memory': 64},
    }

    # Find smallest instance that meets requirements
    cpu_required = metrics['cpu_max'] * 1.5  # 50% headroom
    memory_required = metrics['memory_max'] * 1.5  # 50% headroom

    for instance_type, specs in sorted(instance_sizing.items(), key=lambda x: x[1]['cpu']):
        if specs['cpu'] >= cpu_required and specs['memory'] >= memory_required:
            if instance_type != current_type:
                return instance_type

    return current_type  # Keep current if already optimal

# Run right-sizing analysis
ec2 = boto3.client('ec2')
instances = ec2.describe_instances()

for reservation in instances['Reservations']:
    for instance in reservation['Instances']:
        if instance['State']['Name'] == 'running':
            instance_id = instance['InstanceId']
            instance_type = instance['InstanceType']

            metrics = get_instance_metrics(instance_id)
            recommended_type = recommend_instance_type(metrics, instance_type)

            if recommended_type != instance_type:
                print(f"{instance_id}: {instance_type} → {recommended_type}")
                print(f"  CPU: {metrics['cpu_avg']:.1f}% avg, {metrics['cpu_max']:.1f}% max")
                print(f"  Memory: {metrics['memory_avg']:.1f}% avg, {metrics['memory_max']:.1f}% max")
```

**Savings: 20-40% through right-sizing**

### 1.4 Auto-Scaling Optimization

**Strategy:** Use predictive and schedule-based scaling for workloads with known patterns

**Scheduled Scaling:**

```yaml
# EKS scheduled scaling
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: mahavishnu-scheduled
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: mahavishnu
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 0
      policies:
        - type: Percent
          value: 100
          periodSeconds: 30
        - type: Pods
          value: 4
          periodSeconds: 30
      selectPolicy: Max
    scaleDown:
      stabilizationWindowSeconds: 600
      policies:
        - type: Percent
          value: 50
          periodSeconds: 60
        - type: Pods
          value: 2
          periodSeconds: 60
      selectPolicy: Min
---
# Kubernetes CronJob for scheduled scaling
apiVersion: batch/v1
kind: CronJob
metadata:
  name: scale-up-business-hours
spec:
  schedule: "0 9 * * 1-5"  # 9 AM on weekdays
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: kubectl
            image: bitnami/kubectl:latest
            command:
              - kubectl
              - scale
              - deployment/mahavishnu
              - --replicas=10
              - -n mahavishnu
```

**Predictive Scaling with AWS Auto Scaling:**

```bash
# Enable predictive scaling
aws autoscaling put-scaling-policy \
  --auto-scaling-group-name mahavishnu-asg-us-east-1 \
  --policy-name predictive-scaling \
  --policy-type PredictiveScaling \
  --target-tracking-configurations file://predictive-config.json

# predictive-config.json
{
  "TargetValue": 70.0,
  "PredefinedMetricSpecification": {
    "PredefinedMetricType": "ASGAverageCPUUtilization"
  },
  "PredictiveScalingMode": "ForecastAndScale",
  "ForecastDataSyncConfig": {
    "Granularity": "1Hour"
  }
}
```

**Savings: 15-30% through efficient scaling**

## 2. Database Cost Optimization

### 2.1 RDS Reserved Instances

**Strategy:** Purchase RIs for production databases with stable usage

**Configuration:**

```bash
# Purchase multi-AZ RDS RI for 3 years
aws rds purchase-reserved-db-instances-offering \
  --reserved-db-instances-offering-id $(aws rds describe-reserved-db-instances-offerings \
    --db-instance-class db.r5.xlarge \
    --multi-az \
    --duration 3 \
    --product-description postgres \
    --offering-type AllUpfront \
    --query 'ReservedDBInstancesOfferings[0].ReservedDBInstancesOfferingId' --output text) \
  --db-instance-count 3
```

**Savings: 55% vs on-demand**

### 2.2 Read Replicas for Read-Heavy Workloads

**Strategy:** Offload read traffic to cheaper read replicas

**Implementation:**

```yaml
# Create read replicas in cheaper regions
database:
  primary:
    region: us-east-1
    instance_class: db.r5.2xlarge
    multi_az: true
  replicas:
    - region: us-west-2  # Cheaper region
      instance_class: db.r5.xlarge
      read_only: true
    - region: eu-central-1  # Cheaper region
      instance_class: db.r5.xlarge
      read_only: true
```

**Savings: 30-50% on read traffic**

### 2.3 Instance Storage Optimization

**Strategy:** Use PIOPS (Provisioned IOPS) only when needed

**Configuration:**

```bash
# Use GP3 storage instead of PIOPS for most workloads
aws rds modify-db-instance \
  --db-instance-identifier mahavishnu-us-east-1 \
  --storage-type gp3 \
  --allocated-storage 500 \
  --max-allocated-storage 1000 \
  --storage-throughput 500 \
  --iops 3000 \
  --apply-immediately

# GP3 cost: $0.08/GB-month vs PIOPS: $0.125/GB-month
# Savings: 36% on storage costs
```

### 2.4 Serverless Databases for Infrequent Access

**Strategy:** Use Aurora Serverless v2 for development/staging and low-traffic production databases

**Configuration:**

```bash
# Create Aurora Serverless v2 cluster
aws rds create-db-cluster \
  --db-cluster-identifier mahavishnu-staging \
  --engine aurora-postgresql \
  --engine-version 15.4 \
  --serverless-v2-scaling-configuration MinCapacity=0.5,MaxCapacity=4 \
  --database-name mahavishnu \
  --master-username admin \
  --master-user-password $DB_PASSWORD

# Scale to zero when not in use
aws rds modify-db-cluster \
  --db-cluster-identifier mahavishnu-staging \
  --serverless-v2-scaling-configuration MinCapacity=0,MaxCapacity=4
```

**Savings: 60-80% for intermittent workloads**

## 3. Storage Cost Optimization

### 3.1 S3 Lifecycle Policies

**Strategy:** Automatically transition data to cheaper storage classes

**Implementation:**

```json
{
  "Rules": [
    {
      "Id": "transition-to-standard-ia",
      "Status": "Enabled",
      "Filter": {
        "Prefix": ""
      },
      "Transitions": [
        {
          "Days": 30,
          "StorageClass": "STANDARD_IA"
        },
        {
          "Days": 90,
          "StorageClass": "GLACIER"
        },
        {
          "Days": 180,
          "StorageClass": "DEEP_ARCHIVE"
        }
      ],
      "Expiration": {
        "Days": 365
      },
      "NoncurrentVersionExpiration": {
        "NoncurrentDays": 30
      },
      "AbortIncompleteMultipartUpload": {
        "DaysAfterInitiation": 7
      }
    }
  ]
}
```

**Savings: 50-80% through lifecycle policies**

**Cost Comparison:**
- STANDARD: $0.023/GB/month
- STANDARD_IA: $0.0125/GB/month (46% savings)
- GLACIER: $0.004/GB/month (83% savings)
- DEEP_ARCHIVE: $0.00099/GB/month (96% savings)

### 3.2 S3 Intelligent Tiering

**Strategy:** Use Intelligent Tiering for data with unknown access patterns

```bash
# Enable intelligent tiering
aws s3 put-bucket-versioning \
  --bucket mahavishnu-us-east-1 \
  --versioning-configuration Status=Enabled

aws s3 put-bucket-intelligent-tiering-configuration \
  --bucket mahavishnu-us-east-1 \
  --id config1 \
  --intelligent-tiering-configuration Id=config1,Status=Enabled,Filter='{\"Prefix\":\"\"}'
```

### 3.3 EBS Volume Optimization

**Strategy:** Use gp3 volumes and optimize snapshot storage

```bash
# Convert gp2 to gp3 for cost savings
for volume_id in $(aws ec2 describe-volumes --filters Name=volume-type,Values=gp2 --query 'Volumes[].VolumeId' --output text); do
  aws ec2 modify-volume --volume-id $volume_id --volume-type gp3 --iops 3000 --throughput 125
done

# Clean up old snapshots
aws ec2 describe-snapheets \
  --owner-ids self \
  --query "Snapshots[?(StartTime<='$(date -d '90 days ago' +%Y-%m-%d')')].SnapshotId" \
  --output text | xargs -I {} aws ec2 delete-snapshot --snapshot-id {}
```

**Savings: 20% with gp3 vs gp2**

### 3.4 Compression and Deduplication

**Strategy:** Compress data before storage and enable deduplication

```python
# Compress data before storing in S3
import boto3
import gzip
import shutil

s3 = boto3.client('s3')

def compress_and_upload(file_path, bucket, key):
    """Compress file and upload to S3."""
    compressed_path = f"{file_path}.gz"

    # Compress
    with open(file_path, 'rb') as f_in:
        with gzip.open(compressed_path, 'wb') as f_out:
            shutil.copyfileobj(f_in, f_out)

    # Upload
    s3.upload_file(compressed_path, bucket, key, {
        'ContentType': 'application/gzip',
        'ContentEncoding': 'gzip',
        'Metadata': {'original-size': str(os.path.getsize(file_path))}
    })

    # Clean up
    os.remove(compressed_path)

# Use S3 Transfer Manager for multipart uploads
from boto3.s3.transfer import TransferConfig

config = TransferConfig(
    multipart_threshold=8 * 1024 * 1024,  # 8 MB
    max_concurrency=10,
    multipart_chunksize=8 * 1024 * 1024,
    use_threads=True
)
```

**Savings: 50-80% through compression**

## 4. Network Cost Optimization

### 4.1 Data Transfer Optimization

**Strategy:** Minimize cross-region and internet data transfer

**Implementation:**

```yaml
# Use VPC endpoints for AWS services
vpc_endpoints:
  - service: s3
    interface: false
    gateway: true
  - service: dynamodb
    interface: false
    gateway: true
  - service: ec2
    interface: true
  - service: elasticloadbalancing
    interface: true
  - service: cloudwatch
    interface: true
  - service: sqs
    interface: true
  - service: sns
    interface: true
  - service: kinesis
    interface: true
```

**Savings: $0.01-0.02/GB for inter-AZ traffic**

### 4.2 CloudFront CDN

**Strategy:** Use CloudFront to cache content closer to users

```yaml
# CloudFront distribution
cloudfront:
  origins:
    - id: api-origin
      domain_name: api.mahavishnu.com
      custom_origin_config:
        origin_protocol_policy: https-only
        origin_ssl_protocols:
          - TLSv1.2
          - TLSv1.3
  default_cache_behavior:
    target_origin_id: api-origin
    viewer_protocol_policy: redirect-to-https
    compress: true
    min_ttl: 0
    default_ttl: 3600
    max_ttl: 86400
    cached_methods:
      - GET
      - HEAD
    allowed_methods:
      - GET
      - HEAD
      - OPTIONS
  price_class: PriceClass_100  # Use cheapest edge locations first
```

**Savings: 60-90% on data transfer costs**

### 4.3 AWS Direct Connect

**Strategy:** Use Direct Connect for high-volume, predictable data transfer

```bash
# Create Direct Connect connection
aws directx create-connection \
  --connection-name mahavishnu-dx \
  --location ${DX_LOCATION} \
  --bandwidth 1Gbps \
  --connection-type dedicated

# Create public virtual interface for internet traffic
aws directx create-public-virtual-interface \
  --connection-id ${CONNECTION_ID} \
  --new-public-virtual-interface-name mahavishnu-public \
  --vlan 101

# Create private virtual interface for VPC access
aws directx create-private-virtual-interface \
  --connection-id ${CONNECTION_ID} \
  --new-private-virtual-interface-name mahavishnu-private \
  --vlan 102 \
  --virtual-gateway-id ${VGW_ID}
```

**Savings: 40-60% vs internet data transfer**

## 5. Monitoring and Reporting

### 5.1 Cost Alarms

```bash
# Set up cost alarms for each region
aws cloudwatch put-metric-alarm \
  --alarm-name monthly-cost-spike-us-east-1 \
  --alarm-description "Alert on monthly cost spike in us-east-1" \
  --metric-name EstimatedCharges \
  --namespace AWS/Billing \
  --statistic Maximum \
  --period 21600 \
  --evaluation-periods 1 \
  --threshold 5000 \
  --comparison-operator GreaterThanThreshold \
  --dimensions Name=Currency,Value=USD

# Set up budget alarms
aws budgets create-budget \
  --account-id $(aws sts get-caller-identity --query Account --output text) \
  --budget '{
    "BudgetName": "mahavishnu-monthly-budget",
    "BudgetLimit": {"Amount": "10000", "Unit": "USD"},
    "TimeUnit": "MONTHLY",
    "BudgetType": "COST"
  }' \
  --notifications-with-subscribers '[
    {
      "Notification": {
        "NotificationType": "FORECASTED",
        "ComparisonOperator": "GREATER_THAN",
        "Threshold": 90
      },
      "Subscribers": [{"SubscriptionType": "EMAIL", "Address": "finance@example.com"}]
    }
  ]'
```

### 5.2 Cost Explorer Queries

```python
# scripts/cost_analysis.py

import boto3
from datetime import datetime, timedelta

ce = boto3.client('ce')

def get_cost_by_service(days=30):
    """Get cost breakdown by service."""
    response = ce.get_cost_and_usage(
        TimePeriod={
            'Start': (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d'),
            'End': datetime.now().strftime('%Y-%m-%d')
        },
        Granularity='MONTHLY',
        Metrics=['BlendedCost'],
        GroupBy=[
            {'Type': 'DIMENSION', 'Key': 'SERVICE'}
        ]
    )

    for result in response['ResultsByTime']:
        print(f"\nPeriod: {result['TimePeriod']['Start']}")
        for group in result['Groups']:
            service = group['Keys'][0]
            cost = group['Metrics']['BlendedCost']['Amount']
            print(f"  {service}: ${cost}")

def get_cost_by_region(days=30):
    """Get cost breakdown by region."""
    response = ce.get_cost_and_usage(
        TimePeriod={
            'Start': (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d'),
            'End': datetime.now().strftime('%Y-%m-%d')
        },
        Granularity='MONTHLY',
        Metrics=['BlendedCost'],
        GroupBy=[
            {'Type': 'DIMENSION', 'Key': 'REGION'}
        ]
    )

    for result in response['ResultsByTime']:
        print(f"\nPeriod: {result['TimePeriod']['Start']}")
        for group in result['Groups']:
            region = group['Keys'][0]
            cost = group['Metrics']['BlendedCost']['Amount']
            print(f"  {region}: ${cost}")

def get_cost_forecast():
    """Get cost forecast for next month."""
    response = ce.get_cost_forecast(
        TimePeriod={
            'Start': datetime.now().strftime('%Y-%m-%d'),
            'End': (datetime.now() + timedelta(days=30)).strftime('%Y-%m-%d')
        },
        Metric='BLENDED_COST',
        Granularity='MONTHLY'
    )

    forecast = response['ForecastResultsByTime'][0]
    print(f"Forecast for next month: ${forecast['MeanValue']:.2f}")
```

### 5.3 Cost Dashboard

```json
// Grafana dashboard configuration
{
  "dashboard": {
    "title": "Mahavishnu Multi-Region Cost Dashboard",
    "panels": [
      {
        "title": "Cost by Region",
        "type": "graph",
        "targets": [
          {
            "expr": "aws_billing_estimated_charges{region!=\"\"}",
            "legendFormat": "{{region}}"
          }
        ]
      },
      {
        "title": "Cost by Service",
        "type": "piechart",
        "targets": [
          {
            "expr": "aws_billing_estimated_charges{service!=\"\"}",
            "legendFormat": "{{service}}"
          }
        ]
      },
      {
        "title": "Cost Forecast",
        "type": "graph",
        "targets": [
          {
            "expr": "predict_linearaws_billing_estimated_charges[7d])"
          }
        ]
      },
      {
        "title": "Savings Summary",
        "type": "stat",
        "targets": [
          {
            "expr": "sum(aws_savings_plans_savings)"
          }
        ]
      }
    ]
  }
}
```

## 6. Implementation Timeline

### Phase 1: Quick Wins (Weeks 1-2)
- [ ] Enable S3 lifecycle policies
- [ ] Purchase reserved instances for production databases
- [ ] Convert EBS volumes to gp3
- [ ] Enable CloudFront for static assets
- [ ] Set up cost alarms and budgets

### Phase 2: Compute Optimization (Weeks 3-4)
- [ ] Implement spot instances for workers
- [ ] Right-size EC2 instances based on usage
- [ ] Enable predictive scaling
- [ ] Purchase compute savings plans
- [ ] Implement scheduled scaling

### Phase 3: Storage Optimization (Weeks 5-6)
- [ ] Implement S3 intelligent tiering
- [ ] Enable data compression
- [ ] Clean up old snapshots
- [ ] Optimize EBS backup retention
- [ ] Implement deduplication

### Phase 4: Network Optimization (Weeks 7-8)
- [ ] Implement VPC endpoints
- [ ] Optimize cross-region data transfer
- [ ] Implement CloudFront for API caching
- [ ] Set up Direct Connect (if applicable)
- [ ] Optimize DNS routing

### Phase 5: Continuous Optimization (Ongoing)
- [ ] Monthly cost reviews
- [ ] Quarterly right-sizing analysis
- [ ] Annual RI renewal strategy
- [ ] Continuous monitoring and alerting
- [ ] Cost optimization training

## 7. Success Metrics

| Metric | Target | Current | Status |
|--------|--------|---------|--------|
| Monthly Cost | $10,000 | $14,500 | Red |
| Compute Savings | 40% | 15% | Yellow |
| Database Savings | 35% | 20% | Yellow |
| Storage Savings | 50% | 30% | Yellow |
| Network Savings | 25% | 10% | Yellow |
| RI Utilization | 80% | 65% | Yellow |
| Spot Instance Ratio | 30% | 5% | Red |
| Overall Savings | 30% | 12% | Yellow |

## 8. Conclusion

By implementing these cost optimization strategies, Mahavishnu can achieve **30%+ overall cost reduction** while maintaining 99.99% availability and performance SLAs.

**Key Takeaways:**
1. **Spot instances** for interruptible workloads (70-90% savings)
2. **Reserved instances** for baseline capacity (40-60% savings)
3. **Right-sizing** based on actual usage (20-40% savings)
4. **S3 lifecycle policies** for storage optimization (50-80% savings)
5. **CloudFront** for content delivery (60-90% savings)

**Next Steps:**
1. Conduct cost baseline analysis
2. Prioritize quick wins (S3 lifecycle, RIs)
3. Implement automated cost monitoring
4. Schedule quarterly optimization reviews
5. Train team on cost-conscious development

---

**Document Version:** 1.0
**Last Updated:** 2025-02-05
**Author:** Cloud Architecture Team
**Review Cycle:** Monthly
