const monthlyAWSBill = {
  totalSpend: 2_300_000, // $2.3M/month
  breakdown: {
    ec2Instances: 1_150_000,    // 50% - mostly oversized
    dataTransfer: 345_000,      // 15% - inefficient routing
    storage: 276_000,           // 12% - redundant backups
    rds: 230_000,              // 10% - idle dev databases
    unusedEIPs: 23_000,        // 1% - forgotten resources
    zombieResources: 276_000    // 12% - truly abandoned
  },
  identifiedWaste: 805_000,     // 35% waste = $9.6M annually
  optimizationPotential: 920_000 // 40% potential savings
};

import boto3
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Tuple
import numpy as np
 
class IntelligentRightsizer:
    def __init__(self, region='us-east-1'):
        self.cloudwatch = boto3.client('cloudwatch', region_name=region)
        self.ec2 = boto3.client('ec2', region_name=region)
        
    async def analyze_instance(self, instance_id: str, days: int = 30) -> RightsizingRecommendation:
        """Analyze instance usage patterns and recommend optimal sizing."""
        
        # Collect comprehensive metrics
        metrics = await self.collect_usage_metrics(instance_id, days)
        
        # Analyze usage patterns
        usage_analysis = self.analyze_usage_patterns(metrics)
        
        # Generate rightsizing recommendation
        recommendation = self.generate_recommendation(usage_analysis)
        
        return recommendation
    
    def analyze_usage_patterns(self, metrics: Dict) -> UsageAnalysis:
        """Analyze usage patterns to identify rightsizing opportunities."""
        
        cpu_analysis = self.analyze_cpu_patterns(metrics['cpu'])
        memory_analysis = self.analyze_memory_patterns(metrics['memory'])
        network_analysis = self.analyze_network_patterns(metrics['network'])
        
        return UsageAnalysis(
            cpu_utilization=cpu_analysis,
            memory_utilization=memory_analysis,
            network_utilization=network_analysis,
            usage_patterns=self.identify_usage_patterns(metrics),
            seasonal_trends=self.detect_seasonal_trends(metrics),
            cost_impact=self.calculate_cost_impact(metrics)
        )
    
    def generate_recommendation(self, analysis: UsageAnalysis) -> RightsizingRecommendation:
        """Generate specific rightsizing recommendations."""
        
        current_instance = analysis.current_instance_type
        target_instance = self.select_optimal_instance_type(analysis)
        
        return RightsizingRecommendation(
            instance_id=analysis.instance_id,
            current_type=current_instance,
            recommended_type=target_instance,
            confidence_score=self.calculate_confidence(analysis),
            estimated_savings=self.calculate_savings(current_instance, target_instance),
            performance_impact=self.assess_performance_impact(analysis, target_instance),
            implementation_plan=self.create_implementation_plan(analysis, target_instance)
        )
 
# Usage example
rightsizer = IntelligentRightsizer()
recommendations = await rightsizer.analyze_all_instances()
 
for rec in recommendations:
    if rec.confidence_score > 0.8 and rec.estimated_savings > 100:
        print(f"Instance {rec.instance_id}: Save ${rec.estimated_savings}/month")
        print(f"Downsize from {rec.current_type} to {rec.recommended_type}")

#!/bin/bash
# Automated rightsizing with safety checks
 
rightsize_instance() {
    local instance_id=$1
    local new_instance_type=$2
    local confidence_score=$3
    
    # Safety checks
    if [ $(echo "$confidence_score < 0.8" | bc -l) ]; then
        echo "Confidence too low for automated rightsizing"
        return 1
    fi
    
    # Create snapshot for rollback
    echo "Creating snapshot for rollback capability..."
    snapshot_id=$(aws ec2 create-snapshot \
        --volume-id $(get_root_volume $instance_id) \
        --description "Pre-rightsizing snapshot" \
        --query 'SnapshotId' --output text)
    
    # Stop instance gracefully
    echo "Stopping instance $instance_id..."
    aws ec2 stop-instances --instance-ids $instance_id
    aws ec2 wait instance-stopped --instance-ids $instance_id
    
    # Change instance type
    echo "Changing instance type to $new_instance_type..."
    aws ec2 modify-instance-attribute \
        --instance-id $instance_id \
        --instance-type Value=$new_instance_type
    
    # Start instance
    echo "Starting instance with new size..."
    aws ec2 start-instances --instance-ids $instance_id
    aws ec2 wait instance-running --instance-ids $instance_id
    
    # Validate performance
    if validate_performance $instance_id; then
        echo "Rightsizing successful! Monitoring for 24 hours..."
        schedule_performance_monitoring $instance_id 24
    else
        echo "Performance validation failed. Rolling back..."
        rollback_instance $instance_id $snapshot_id
    fi
}

interface PredictiveScaler {
  forecastDemand(timeHorizon: number): Promise<DemandForecast>;
  optimizeScalingPolicy(forecast: DemandForecast): ScalingPolicy;
  implementPreemptiveScaling(): Promise<void>;
}
 
class AIAutoScaler implements PredictiveScaler {
  private readonly ml_model: DemandPredictionModel;
  
  async forecastDemand(timeHorizon: number): Promise<DemandForecast> {
    const historicalData = await this.getHistoricalMetrics(90); // 90 days
    const externalFactors = await this.getExternalFactors(); // events, holidays, etc.
    
    const prediction = await this.ml_model.predict({
      historical: historicalData,
      external: externalFactors,
      horizon: timeHorizon
    });
    
    return {
      expectedLoad: prediction.load,
      confidenceInterval: prediction.confidence,
      scalingEvents: this.identifyScalingEvents(prediction),
      costProjection: this.calculateCostImpact(prediction)
    };
  }
  
  optimizeScalingPolicy(forecast: DemandForecast): ScalingPolicy {
    return {
      scaleOutTriggers: this.optimizeScaleOutPolicy(forecast),
      scaleInTriggers: this.optimizeScaleInPolicy(forecast),
      preemptiveActions: this.generatePreemptiveActions(forecast),
      costGuardrails: this.setCostLimits(forecast)
    };
  }
}

# CloudFormation template for predictive auto-scaling
PredictiveAutoScalingGroup:
  Type: AWS::AutoScaling::AutoScalingGroup
  Properties:
    PredictiveScalingPolicy:
      - PolicyName: DemandBasedScaling
        PredictiveScalingMode: ForecastAndScale
        SchedulingBufferTime: 300  # 5 minutes ahead
        MaxCapacityBreachBehavior: IncreaseMaxCapacity
        MaxCapacityBuffer: 20  # 20% buffer
        
        TargetTrackingConfiguration:
          TargetValue: 70.0
          PredefinedMetricSpecification:
            PredefinedMetricType: ASGAverageCPUUtilization
        
        # Custom metrics for better prediction
        CustomMetrics:
          - MetricName: ApplicationRequestRate
            Namespace: MyApp/Performance
            Dimensions:
              - Name: Environment
                Value: Production
          
          - MetricName: DatabaseConnections
            Namespace: MyApp/Database
            Weight: 0.3  # Lower weight for secondary metric

# Cost analysis comparison
def analyze_scaling_costs():
    reactive_scaling_costs = {
        'over_provisioning': 280_000,  # Annual cost of reactive over-provisioning
        'performance_issues': 150_000,  # Cost of slow response times
        'manual_intervention': 45_000,  # Operations overhead
        'total': 475_000
    }
    
    predictive_scaling_costs = {
        'optimized_provisioning': 185_000,  # Right-sized proactive scaling
        'performance_boost': -50_000,  # Revenue from better performance
        'automation_savings': -40_000,  # Reduced manual work
        'ml_infrastructure': 15_000,  # Cost of prediction models
        'total': 110_000
    }
    
    savings = reactive_scaling_costs['total'] - predictive_scaling_costs['total']
    print(f"Annual savings from predictive scaling: ${savings:,}")
    # Output: Annual savings from predictive scaling: $365,000
 
analyze_scaling_costs()

class StorageOptimizer {
  async auditStorageWaste(): Promise<StorageWasteReport> {
    const s3Waste = await this.analyzeS3Waste();
    const ebsWaste = await this.analyzeEBSWaste();
    const snapshotWaste = await this.analyzeSnapshotWaste();
    
    return {
      s3: {
        duplicateData: s3Waste.duplicates,  // $45K/month
        inappropriateStorageClass: s3Waste.classOptimization,  // $32K/month
        zombieMultipartUploads: s3Waste.multipart,  // $8K/month
        unusedVersions: s3Waste.versioning  // $18K/month
      },
      ebs: {
        oversizedVolumes: ebsWaste.oversized,  // $28K/month
        unusedVolumes: ebsWaste.unused,  // $15K/month
        inefficientTypes: ebsWaste.typeOptimization  // $12K/month
      },
      snapshots: {
        orphanedSnapshots: snapshotWaste.orphaned,  // $22K/month
        excessiveRetention: snapshotWaste.retention  // $35K/month
      },
      totalMonthlySavings: 215_000  // $2.58M annually
    };
  }
  
  async implementStorageOptimization(): Promise<void> {
    // Implement S3 lifecycle policies
    await this.optimizeS3StorageClasses();
    
    // Right-size EBS volumes
    await this.rightsizeEBSVolumes();
    
    // Clean up snapshots
    await this.optimizeSnapshotRetention();
    
    // Implement intelligent archiving
    await this.enableIntelligentArchiving();
  }
}

{
  "Rules": [
    {
      "ID": "IntelligentTieringRule",
      "Status": "Enabled",
      "Filter": {
        "Prefix": "data/"
      },
      "Transitions": [
        {
          "Days": 0,
          "StorageClass": "INTELLIGENT_TIERING"
        }
      ]
    },
    {
      "ID": "ArchiveOldData",
      "Status": "Enabled",
      "Filter": {
        "Prefix": "backups/"
      },
      "Transitions": [
        {
          "Days": 30,
          "StorageClass": "GLACIER"
        },
        {
          "Days": 90,
          "StorageClass": "DEEP_ARCHIVE"
        }
      ]
    },
    {
      "ID": "CleanupMultipartUploads",
      "Status": "Enabled",
      "AbortIncompleteMultipartUpload": {
        "DaysAfterInitiation": 1
      }
    }
  ]
}

class ReservedInstanceOptimizer:
    def __init__(self):
        self.ce_client = boto3.client('ce')  # Cost Explorer
        self.ec2_client = boto3.client('ec2')
        
    def optimize_ri_portfolio(self, timeframe_months: int = 12) -> RIRecommendations:
        """Generate optimized RI recommendations based on usage patterns."""
        
        # Analyze current usage patterns
        usage_data = self.analyze_instance_usage(timeframe_months)
        
        # Identify stable workloads suitable for RIs
        stable_workloads = self.identify_stable_workloads(usage_data)
        
        # Calculate optimal RI mix
        ri_recommendations = self.calculate_optimal_ri_mix(stable_workloads)
        
        return ri_recommendations
    
    def identify_stable_workloads(self, usage_data: Dict) -> List[StableWorkload]:
        """Identify workloads with consistent usage patterns."""
        stable_workloads = []
        
        for instance_type, usage in usage_data.items():
            # Calculate usage stability metrics
            usage_variance = np.var(usage.daily_hours)
            avg_utilization = np.mean(usage.daily_hours)
            
            # Consider workload stable if:
            # 1. Low variance in daily usage
            # 2. High average utilization
            # 3. Consistent usage over multiple months
            if (usage_variance < 4.0 and  # Less than 4 hours variance
                avg_utilization > 16 and  # More than 16 hours/day
                len(usage.monthly_data) >= 3):  # At least 3 months data
                
                stable_workloads.append(StableWorkload(
                    instance_type=instance_type,
                    average_usage=avg_utilization,
                    stability_score=self.calculate_stability_score(usage),
                    ri_recommendation=self.recommend_ri_type(usage)
                ))
        
        return stable_workloads
    
    def calculate_optimal_ri_mix(self, workloads: List[StableWorkload]) -> RIRecommendations:
        """Calculate the optimal mix of 1-year and 3-year RIs."""
        
        recommendations = []
        
        for workload in workloads:
            # Calculate savings for different RI terms
            one_year_savings = self.calculate_ri_savings(workload, term_years=1)
            three_year_savings = self.calculate_ri_savings(workload, term_years=3)
            
            # Factor in business risk (prefer shorter terms for less stable workloads)
            risk_adjusted_savings = {
                1: one_year_savings * workload.stability_score,
                3: three_year_savings * (workload.stability_score * 0.8)  # Discount for uncertainty
            }
            
            optimal_term = max(risk_adjusted_savings, key=risk_adjusted_savings.get)
            
            recommendations.append(RIRecommendation(
                instance_type=workload.instance_type,
                quantity=workload.average_usage,
                term_years=optimal_term,
                estimated_savings=risk_adjusted_savings[optimal_term],
                confidence_level=workload.stability_score
            ))
        
        return RIRecommendations(
            recommendations=recommendations,
            total_annual_savings=sum(r.estimated_savings for r in recommendations),
            implementation_priority=sorted(recommendations, key=lambda x: x.estimated_savings, reverse=True)
        )
 
# Example usage
ri_optimizer = ReservedInstanceOptimizer()
recommendations = ri_optimizer.optimize_ri_portfolio(12)
 
print(f"Total annual savings from optimized RIs: ${recommendations.total_annual_savings:,.2f}")

#!/bin/bash
# Automated RI portfolio management
 
manage_ri_portfolio() {
    # Analyze current RI utilization
    ri_utilization=$(aws ce get-ri-utilization \
        --time-period Start=2024-01-01,End=2024-12-31 \
        --granularity MONTHLY \
        --query 'UtilizationsByTime[*].Total.UtilizationPercentage' \
        --output text)
    
    # If utilization is below 80%, consider modifications
    for util in $ri_utilization; do
        if [ $(echo "$util < 80" | bc -l) -eq 1 ]; then
            echo "RI utilization below threshold: $util%"
            
            # Get modification recommendations
            aws ce get-rightsizing-recommendation \
                --service EC2-Instance \
                --configuration RightsizingType=Modify
        fi
    done
    
    # Check for new RI opportunities
    aws ce get-ri-purchase-recommendation \
        --service EC2-Instance \
        --lookback-period-in-days 60 \
        --term-in-years 1 \
        --payment-option ALL_UPFRONT
}

class NetworkOptimizer {
  async analyzeDataTransferCosts(): Promise<DataTransferAnalysis> {
    const analysis = {
      interRegionTransfer: await this.analyzeInterRegionCosts(),
      internetEgress: await this.analyzeInternetEgressCosts(),
      intraAZTransfer: await this.analyzeIntraAZCosts(),
      cloudFrontOptimization: await this.analyzeCDNOptimization()
    };
    
    return {
      currentMonthlyCost: this.calculateCurrentCosts(analysis),
      optimizationOpportunities: this.identifyOptimizations(analysis),
      projectedSavings: this.calculatePotentialSavings(analysis)
    };
  }
  
  async optimizeDataTransfer(): Promise<OptimizationPlan> {
    // 1. Implement CloudFront for static content
    const cdnPlan = await this.planCDNOptimization();
    
    // 2. Optimize inter-region architecture
    const regionPlan = await this.optimizeRegionalArchitecture();
    
    // 3. Implement VPC endpoints
    const vpcEndpointPlan = await this.planVPCEndpoints();
    
    return {
      implementations: [cdnPlan, regionPlan, vpcEndpointPlan],
      estimatedSavings: this.calculateTotalSavings([cdnPlan, regionPlan, vpcEndpointPlan]),
      timeline: this.createImplementationTimeline()
    };
  }
}

# CloudFormation for VPC Endpoints to reduce NAT Gateway costs
VPCEndpointS3:
  Type: AWS::EC2::VPCEndpoint
  Properties:
    VpcId: !Ref MyVPC
    ServiceName: !Sub 'com.amazonaws.${AWS::Region}.s3'
    VpcEndpointType: Gateway
    RouteTableIds:
      - !Ref PrivateRouteTable
 
VPCEndpointDynamoDB:
  Type: AWS::EC2::VPCEndpoint
  Properties:
    VpcId: !Ref MyVPC
    ServiceName: !Sub 'com.amazonaws.${AWS::Region}.dynamodb'
    VpcEndpointType: Gateway
    RouteTableIds:
      - !Ref PrivateRouteTable
 
# Interface endpoints for other services
VPCEndpointSSM:
  Type: AWS::EC2::VPCEndpoint
  Properties:
    VpcId: !Ref MyVPC
    ServiceName: !Sub 'com.amazonaws.${AWS::Region}.ssm'
    VpcEndpointType: Interface
    SubnetIds:
      - !Ref PrivateSubnet1
      - !Ref PrivateSubnet2
    SecurityGroupIds:
      - !Ref VPCEndpointSecurityGroup
    PrivateDnsEnabled: true

class ZombieResourceHunter:
    def __init__(self):
        self.session = boto3.Session()
        self.resource_scanners = {
            'ec2': self.scan_ec2_zombies,
            'rds': self.scan_rds_zombies,
            'elb': self.scan_load_balancer_zombies,
            'eip': self.scan_elastic_ip_zombies,
            's3': self.scan_s3_zombies,
            'lambda': self.scan_lambda_zombies
        }
    
    async def hunt_zombies(self) -> ZombieReport:
        """Comprehensive zombie resource detection."""
        zombie_report = ZombieReport()
        
        for service, scanner in self.resource_scanners.items():
            zombies = await scanner()
            zombie_report.add_service_zombies(service, zombies)
        
        return zombie_report
    
    async def scan_ec2_zombies(self) -> List[ZombieResource]:
        """Find unused EC2 instances and volumes."""
        ec2 = self.session.client('ec2')
        zombies = []
        
        # Find stopped instances that haven't been used in 30+ days
        instances = ec2.describe_instances(
            Filters=[{'Name': 'instance-state-name', 'Values': ['stopped']}]
        )
        
        for reservation in instances['Reservations']:
            for instance in reservation['Instances']:
                last_used = self.get_last_cloudwatch_activity(instance['InstanceId'])
                days_idle = (datetime.now() - last_used).days
                
                if days_idle > 30:
                    zombies.append(ZombieResource(
                        resource_id=instance['InstanceId'],
                        resource_type='EC2 Instance',
                        cost_per_month=self.calculate_instance_cost(instance),
                        last_activity=last_used,
                        confidence=0.9 if days_idle > 60 else 0.7
                    ))
        
        # Find unattached EBS volumes
        volumes = ec2.describe_volumes(
            Filters=[{'Name': 'status', 'Values': ['available']}]
        )
        
        for volume in volumes['Volumes']:
            age_days = (datetime.now() - volume['CreateTime'].replace(tzinfo=None)).days
            if age_days > 7:  # Unattached for more than a week
                zombies.append(ZombieResource(
                    resource_id=volume['VolumeId'],
                    resource_type='EBS Volume',
                    cost_per_month=self.calculate_volume_cost(volume),
                    last_activity=volume['CreateTime'],
                    confidence=0.95
                ))
        
        return zombies
    
    async def scan_rds_zombies(self) -> List[ZombieResource]:
        """Find unused RDS instances."""
        rds = self.session.client('rds')
        zombies = []
        
        instances = rds.describe_db_instances()
        
        for db in instances['DBInstances']:
            # Check CloudWatch metrics for connection activity
            connections = self.get_rds_connection_metrics(db['DBInstanceIdentifier'])
            
            if self.is_database_unused(connections):
                zombies.append(ZombieResource(
                    resource_id=db['DBInstanceIdentifier'],
                    resource_type='RDS Instance',
                    cost_per_month=self.calculate_rds_cost(db),
                    last_activity=self.get_last_rds_activity(db),
                    confidence=0.8
                ))
        
        return zombies
    
    def create_cleanup_plan(self, zombie_report: ZombieReport) -> CleanupPlan:
        """Create a safe cleanup plan with rollback capabilities."""
        plan = CleanupPlan()
        
        # Sort by confidence and cost impact
        prioritized_zombies = sorted(
            zombie_report.all_zombies,
            key=lambda z: z.confidence * z.cost_per_month,
            reverse=True
        )
        
        for zombie in prioritized_zombies:
            if zombie.confidence > 0.8:
                plan.add_immediate_cleanup(zombie)
            elif zombie.confidence > 0.6:
                plan.add_staged_cleanup(zombie, days_delay=7)
            else:
                plan.add_manual_review(zombie)
        
        return plan
 
# Automated cleanup execution
async def execute_zombie_cleanup():
    hunter = ZombieResourceHunter()
    zombie_report = await hunter.hunt_zombies()
    cleanup_plan = hunter.create_cleanup_plan(zombie_report)
    
    print(f"Found {len(zombie_report.all_zombies)} zombie resources")
    print(f"Potential monthly savings: ${zombie_report.total_monthly_cost:,.2f}")
    
    # Execute cleanup with confirmation
    await cleanup_plan.execute_with_confirmation()

# AWS Config rules for automated cleanup
UnusedSecurityGroupsRule:
  Type: AWS::Config::ConfigRule
  Properties:
    ConfigRuleName: unused-security-groups
    Source:
      Owner: AWS
      SourceIdentifier: EC2_SECURITY_GROUP_ATTACHED_TO_ENI
    
UnusedEIPsRule:
  Type: AWS::Config::ConfigRule
  Properties:
    ConfigRuleName: unused-elastic-ips
    Source:
      Owner: AWS
      SourceIdentifier: EIP_ATTACHED
 
# Lambda function for automated remediation
ZombieCleanupFunction:
  Type: AWS::Lambda::Function
  Properties:
    FunctionName: zombie-resource-cleanup
    Runtime: python3.9
    Handler: cleanup.lambda_handler
    Code:
      ZipFile: |
        import boto3
        import json
        
        def lambda_handler(event, context):
            # Automated cleanup logic
            cleanup_results = perform_zombie_cleanup(event)
            return {
                'statusCode': 200,
                'body': json.dumps(cleanup_results)
            }
    
    Environment:
      Variables:
        CONFIDENCE_THRESHOLD: "0.8"
        DRY_RUN: "false"

interface CloudCostAnalyzer {
  analyzeWorkloadFit(workload: Workload): Promise<CloudFitAnalysis>;
  calculateArbitrageOpportunities(): Promise<ArbitrageReport>;
  recommendOptimalPlacement(): Promise<PlacementStrategy>;
}
 
class MultiCloudOptimizer implements CloudCostAnalyzer {
  async analyzeWorkloadFit(workload: Workload): Promise<CloudFitAnalysis> {
    const providers = ['aws', 'azure', 'gcp'];
    const analyses = {};
    
    for (const provider of providers) {
      const cost = await this.calculateProviderCost(workload, provider);
      const performance = await this.estimatePerformance(workload, provider);
      const features = await this.analyzeFeatureFit(workload, provider);
      
      analyses[provider] = {
        monthlyCost: cost,
        performanceScore: performance,
        featureCompatibility: features,
        migrationComplexity: this.assessMigrationComplexity(workload, provider)
      };
    }
    
    return new CloudFitAnalysis(workload, analyses);
  }
  
  async calculateArbitrageOpportunities(): Promise<ArbitrageReport> {
    const workloads = await this.identifyPortableWorkloads();
    const opportunities = [];
    
    for (const workload of workloads) {
      const analysis = await this.analyzeWorkloadFit(workload);
      const currentCost = analysis.getCurrentProviderCost();
      const optimalProvider = analysis.getOptimalProvider();
      const potentialSavings = currentCost - analysis.getProviderCost(optimalProvider);
      
      if (potentialSavings > 1000) { // Minimum $1000/month savings
        opportunities.push({
          workload: workload.id,
          currentProvider: workload.provider,
          optimalProvider: optimalProvider,
          monthlySavings: potentialSavings,
          migrationCost: analysis.getMigrationCost(optimalProvider),
          paybackPeriod: analysis.getMigrationCost(optimalProvider) / potentialSavings,
          riskLevel: analysis.getMigrationRisk(optimalProvider)
        });
      }
    }
    
    return new ArbitrageReport(opportunities);
  }
}

class CloudCostCalculator:
    def __init__(self):
        self.pricing_apis = {
            'aws': AWSPricingAPI(),
            'azure': AzurePricingAPI(),
            'gcp': GCPPricingAPI()
        }
    
    def calculate_workload_costs(self, workload_spec: WorkloadSpec) -> Dict[str, float]:
        """Calculate costs across all major cloud providers."""
        costs = {}
        
        for provider, api in self.pricing_apis.items():
            compute_cost = api.calculate_compute_cost(workload_spec.compute)
            storage_cost = api.calculate_storage_cost(workload_spec.storage)
            network_cost = api.calculate_network_cost(workload_spec.network)
            
            # Factor in provider-specific discounts
            discount_multiplier = self.get_discount_multiplier(provider, workload_spec)
            
            total_cost = (compute_cost + storage_cost + network_cost) * discount_multiplier
            costs[provider] = total_cost
            
        return costs
    
    def identify_cost_optimization_opportunities(self, current_deployment: Deployment) -> List[Opportunity]:
        """Identify specific cost optimization opportunities."""
        opportunities = []
        
        # Analyze each component
        for component in current_deployment.components:
            # Calculate costs on different providers
            costs = self.calculate_workload_costs(component.spec)
            
            # Find potential savings
            current_cost = costs[current_deployment.provider]
            cheapest_provider = min(costs, key=costs.get)
            potential_savings = current_cost - costs[cheapest_provider]
            
            if potential_savings > 500:  # Minimum $500/month savings
                opportunities.append(Opportunity(
                    component=component.id,
                    current_provider=current_deployment.provider,
                    recommended_provider=cheapest_provider,
                    monthly_savings=potential_savings,
                    migration_complexity=self.assess_migration_complexity(component),
                    business_justification=self.generate_business_case(component, potential_savings)
                ))
        
        return opportunities
 
# Example usage
calculator = CloudCostCalculator()
opportunities = calculator.identify_cost_optimization_opportunities(current_deployment)
 
for opp in opportunities:
    print(f"Component {opp.component}: Save ${opp.monthly_savings}/month")
    print(f"Move from {opp.current_provider} to {opp.recommended_provider}")

interface FinOpsGovernance {
  establishCostAccountability(): Promise<void>;
  implementCostGuardrails(): Promise<void>;
  enableCostTransparency(): Promise<void>;
  createCostOptimizationIncentives(): Promise<void>;
}
 
class FinOpsImplementation implements FinOpsGovernance {
  async establishCostAccountability(): Promise<void> {
    // Implement cost allocation and chargeback
    await this.setupCostAllocation();
    await this.createTeamDashboards();
    await this.establishBudgetAlerts();
  }
  
  async implementCostGuardrails(): Promise<void> {
    // Prevent expensive mistakes before they happen
    const guardrails = [
      new InstanceTypeLimiter(['p4d.24xlarge']), // Prevent accidental expensive instances
      new RegionLimiter([process.env.ALLOWED_REGIONS]),
      new SpendLimiter(10000), // $10K monthly limit for new resources
      new ResourceTagEnforcer(['Owner', 'Project', 'Environment'])
    ];
    
    for (const guardrail of guardrails) {
      await guardrail.implement();
    }
  }
  
  async enableCostTransparency(): Promise<void> {
    // Make costs visible to all stakeholders
    await this.createRealTimeCostDashboard();
    await this.setupWeeklyCostReports();
    await this.implementProjectCostTracking();
  }
}

#!/bin/bash
# Automated cost allocation implementation
 
implement_cost_allocation() {
    # Define mandatory tags
    MANDATORY_TAGS=(
        "Owner"
        "Project" 
        "Environment"
        "CostCenter"
        "Application"
    )
    
    # Create tag enforcement policy
    cat > tag-enforcement-policy.json << EOF
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Deny",
            "Action": [
                "ec2:RunInstances",
                "rds:CreateDBInstance",
                "s3:CreateBucket"
            ],
            "Resource": "*",
            "Condition": {
                "Null": {
                    "aws:RequestedRegion": "false"
                },
                "ForAllValues:StringNotEquals": {
                    "aws:TagKeys": [
                        "Owner",
                        "Project",
                        "Environment",
                        "CostCenter"
                    ]
                }
            }
        }
    ]
}
EOF
    
    # Apply policy to all development roles
    aws iam attach-role-policy \
        --role-name DeveloperRole \
        --policy-arn arn:aws:iam::account:policy/TagEnforcementPolicy
    
    # Set up cost allocation tags
    aws ce create-cost-category-definition \
        --name "Project-Based-Allocation" \
        --rules file://cost-allocation-rules.json
}

class CostReportingEngine:
    def __init__(self):
        self.ce_client = boto3.client('ce')
        self.ses_client = boto3.client('ses')
        
    def generate_weekly_cost_report(self) -> WeeklyCostReport:
        """Generate comprehensive weekly cost report."""
        
        # Get cost data for the past week
        end_date = datetime.now().date()
        start_date = end_date - timedelta(days=7)
        
        cost_data = self.ce_client.get_cost_and_usage(
            TimePeriod={
                'Start': start_date.isoformat(),
                'End': end_date.isoformat()
            },
            Granularity='DAILY',
            Metrics=['UnblendedCost'],
            GroupBy=[
                {'Type': 'DIMENSION', 'Key': 'SERVICE'},
                {'Type': 'TAG', 'Key': 'Project'}
            ]
        )
        
        # Analyze cost trends
        report = WeeklyCostReport(
            total_spend=self.calculate_total_spend(cost_data),
            top_services=self.identify_top_services(cost_data),
            cost_trends=self.analyze_cost_trends(cost_data),
            anomalies=self.detect_cost_anomalies(cost_data),
            recommendations=self.generate_cost_recommendations(cost_data)
        )
        
        return report
    
    def send_cost_alerts(self, report: WeeklyCostReport) -> None:
        """Send targeted cost alerts to stakeholders."""
        
        # Executive summary for leadership
        executive_summary = self.create_executive_summary(report)
        self.send_email(
            recipients=['cto@company.com', 'cfo@company.com'],
            subject='Weekly Cloud Cost Summary',
            body=executive_summary
        )
        
        # Detailed reports for team leads
        for team in report.team_breakdowns:
            team_report = self.create_team_specific_report(team, report)
            self.send_email(
                recipients=[team.lead_email],
                subject=f'Your Team\'s Cloud Costs - {team.name}',
                body=team_report
            )

interface CostOptimizationKPIs {
  // Cost efficiency metrics
  costPerTransaction: number;
  costPerUser: number;
  infrastructureCostRatio: number; // Infrastructure cost as % of revenue
  
  // Optimization metrics
  monthlyWasteReduction: number;
  rightsizingAdoptionRate: number;
  reservedInstanceUtilization: number;
  
  // Operational metrics
  timeToOptimize: number; // Days from identification to implementation
  automationCoverage: number; // % of optimizations automated
  teamEngagement: number; // % of teams actively managing costs
}
 
class KPITracker {
  calculateMonthlyKPIs(): CostOptimizationKPIs {
    return {
      costPerTransaction: this.calculateCostPerTransaction(),
      costPerUser: this.calculateCostPerUser(),
      infrastructureCostRatio: this.calculateInfrastructureCostRatio(),
      monthlyWasteReduction: this.calculateWasteReduction(),
      rightsizingAdoptionRate: this.calculateRightsizingAdoption(),
      reservedInstanceUtilization: this.calculateRIUtilization(),
      timeToOptimize: this.calculateOptimizationVelocity(),
      automationCoverage: this.calculateAutomationCoverage(),
      teamEngagement: this.calculateTeamEngagement()
    };
  }
}

optimization_timeline = {
    'month_1': {
        'cost_reduction': '15%',
        'focus': 'Low-hanging fruit',
        'key_activities': ['Zombie cleanup', 'Basic rightsizing', 'Storage optimization']
    },
    'month_2': {
        'cost_reduction': '28%',
        'focus': 'Automation and scaling',
        'key_activities': ['Auto-scaling', 'RI optimization', 'Network optimization']
    },
    'month_3': {
        'cost_reduction': '40%',
        'focus': 'Advanced optimization',
        'key_activities': ['Predictive scaling', 'Multi-cloud arbitrage', 'FinOps culture']
    },
    'ongoing': {
        'cost_reduction': '40-50%',
        'focus': 'Continuous optimization',
        'key_activities': ['Automated monitoring', 'Proactive optimization', 'Cost innovation']
    }
}