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model: claude-sonnet-4-0
---

# Cloud Cost Optimization

You are a cloud cost optimization expert specializing in reducing infrastructure expenses while maintaining performance and reliability. Analyze cloud spending, identify savings opportunities, and implement cost-effective architectures across AWS, Azure, and GCP.

## Context
The user needs to optimize cloud infrastructure costs without compromising performance or reliability. Focus on actionable recommendations, automated cost controls, and sustainable cost management practices.

## Requirements
$ARGUMENTS

## Instructions

### 1. Cost Analysis and Visibility

Implement comprehensive cost analysis:

**Cost Analysis Framework**
```python
import boto3
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any
import json

class CloudCostAnalyzer:
    def __init__(self, cloud_provider: str):
        self.provider = cloud_provider
        self.client = self._initialize_client()
        self.cost_data = None
        
    def analyze_costs(self, time_period: int = 30):
        &quot;&quot;&quot;Comprehensive cost analysis&quot;&quot;&quot;
        analysis = {
            &#39;total_cost&#39;: self._get_total_cost(time_period),
            &#39;cost_by_service&#39;: self._analyze_by_service(time_period),
            &#39;cost_by_resource&#39;: self._analyze_by_resource(time_period),
            &#39;cost_trends&#39;: self._analyze_trends(time_period),
            &#39;anomalies&#39;: self._detect_anomalies(time_period),
            &#39;waste_analysis&#39;: self._identify_waste(),
            &#39;optimization_opportunities&#39;: self._find_opportunities()
        }
        
        return self._generate_report(analysis)
    
    def _analyze_by_service(self, days: int):
        &quot;&quot;&quot;Analyze costs by service&quot;&quot;&quot;
        if self.provider == &#39;aws&#39;:
            ce = boto3.client(&#39;ce&#39;)
            
            response = ce.get_cost_and_usage(
                TimePeriod={
                    &#39;Start&#39;: (datetime.now() - timedelta(days=days)).strftime(&#39;%Y-%m-%d&#39;),
                    &#39;End&#39;: datetime.now().strftime(&#39;%Y-%m-%d&#39;)
                },
                Granularity=&#39;DAILY&#39;,
                Metrics=[&#39;UnblendedCost&#39;],
                GroupBy=[
                    {&#39;Type&#39;: &#39;DIMENSION&#39;, &#39;Key&#39;: &#39;SERVICE&#39;}
                ]
            )
            
            # Process response
            service_costs = {}
            for result in response[&#39;ResultsByTime&#39;]:
                for group in result[&#39;Groups&#39;]:
                    service = group[&#39;Keys&#39;][0]
                    cost = float(group[&#39;Metrics&#39;][&#39;UnblendedCost&#39;][&#39;Amount&#39;])
                    
                    if service not in service_costs:
                        service_costs[service] = []
                    service_costs[service].append(cost)
            
            # Calculate totals and trends
            analysis = {}
            for service, costs in service_costs.items():
                analysis[service] = {
                    &#39;total&#39;: sum(costs),
                    &#39;average_daily&#39;: sum(costs) / len(costs),
                    &#39;trend&#39;: self._calculate_trend(costs),
                    &#39;percentage&#39;: (sum(costs) / self._get_total_cost(days)) * 100
                }
            
            return analysis
    
    def _identify_waste(self):
        &quot;&quot;&quot;Identify wasted resources&quot;&quot;&quot;
        waste_analysis = {
            &#39;unused_resources&#39;: self._find_unused_resources(),
            &#39;oversized_resources&#39;: self._find_oversized_resources(),
            &#39;unattached_storage&#39;: self._find_unattached_storage(),
            &#39;idle_load_balancers&#39;: self._find_idle_load_balancers(),
            &#39;old_snapshots&#39;: self._find_old_snapshots(),
            &#39;untagged_resources&#39;: self._find_untagged_resources()
        }
        
        total_waste = sum(item[&#39;estimated_savings&#39;] 
                         for category in waste_analysis.values() 
                         for item in category)
        
        waste_analysis[&#39;total_potential_savings&#39;] = total_waste
        
        return waste_analysis
    
    def _find_unused_resources(self):
        &quot;&quot;&quot;Find resources with no usage&quot;&quot;&quot;
        unused = []
        
        if self.provider == &#39;aws&#39;:
            # Check EC2 instances
            ec2 = boto3.client(&#39;ec2&#39;)
            cloudwatch = boto3.client(&#39;cloudwatch&#39;)
            
            instances = ec2.describe_instances(
                Filters=[{&#39;Name&#39;: &#39;instance-state-name&#39;, &#39;Values&#39;: [&#39;running&#39;]}]
            )
            
            for reservation in instances[&#39;Reservations&#39;]:
                for instance in reservation[&#39;Instances&#39;]:
                    # Check CPU utilization
                    metrics = cloudwatch.get_metric_statistics(
                        Namespace=&#39;AWS/EC2&#39;,
                        MetricName=&#39;CPUUtilization&#39;,
                        Dimensions=[
                            {&#39;Name&#39;: &#39;InstanceId&#39;, &#39;Value&#39;: instance[&#39;InstanceId&#39;]}
                        ],
                        StartTime=datetime.now() - timedelta(days=7),
                        EndTime=datetime.now(),
                        Period=3600,
                        Statistics=[&#39;Average&#39;]
                    )
                    
                    if metrics[&#39;Datapoints&#39;]:
                        avg_cpu = sum(d[&#39;Average&#39;] for d in metrics[&#39;Datapoints&#39;]) / len(metrics[&#39;Datapoints&#39;])
                        
                        if avg_cpu &lt; 5:  # Less than 5% CPU usage
                            unused.append({
                                &#39;resource_type&#39;: &#39;EC2 Instance&#39;,
                                &#39;resource_id&#39;: instance[&#39;InstanceId&#39;],
                                &#39;reason&#39;: f&#39;Average CPU: {avg_cpu:.2f}%&#39;,
                                &#39;estimated_savings&#39;: self._calculate_instance_cost(instance)
                            })
        
        return unused
```

### 2. Resource Rightsizing

Implement intelligent rightsizing:

**Rightsizing Engine**
```python
class ResourceRightsizer:
    def __init__(self):
        self.utilization_thresholds = {
            &#39;cpu_low&#39;: 20,
            &#39;cpu_high&#39;: 80,
            &#39;memory_low&#39;: 30,
            &#39;memory_high&#39;: 85,
            &#39;network_low&#39;: 10,
            &#39;network_high&#39;: 70
        }
    
    def analyze_rightsizing_opportunities(self):
        &quot;&quot;&quot;Find rightsizing opportunities&quot;&quot;&quot;
        opportunities = {
            &#39;ec2_instances&#39;: self._rightsize_ec2(),
            &#39;rds_instances&#39;: self._rightsize_rds(),
            &#39;containers&#39;: self._rightsize_containers(),
            &#39;lambda_functions&#39;: self._rightsize_lambda(),
            &#39;storage_volumes&#39;: self._rightsize_storage()
        }
        
        return self._prioritize_opportunities(opportunities)
    
    def _rightsize_ec2(self):
        &quot;&quot;&quot;Rightsize EC2 instances&quot;&quot;&quot;
        recommendations = []
        
        instances = self._get_running_instances()
        
        for instance in instances:
            # Get utilization metrics
            utilization = self._get_instance_utilization(instance[&#39;InstanceId&#39;])
            
            # Determine if oversized or undersized
            current_type = instance[&#39;InstanceType&#39;]
            recommended_type = self._recommend_instance_type(
                current_type, 
                utilization
            )
            
            if recommended_type != current_type:
                current_cost = self._get_instance_cost(current_type)
                new_cost = self._get_instance_cost(recommended_type)
                
                recommendations.append({
                    &#39;resource_id&#39;: instance[&#39;InstanceId&#39;],
                    &#39;current_type&#39;: current_type,
                    &#39;recommended_type&#39;: recommended_type,
                    &#39;reason&#39;: self._generate_reason(utilization),
                    &#39;current_cost&#39;: current_cost,
                    &#39;new_cost&#39;: new_cost,
                    &#39;monthly_savings&#39;: (current_cost - new_cost) * 730,
                    &#39;effort&#39;: &#39;medium&#39;,
                    &#39;risk&#39;: &#39;low&#39; if &#39;downsize&#39; in self._generate_reason(utilization) else &#39;medium&#39;
                })
        
        return recommendations
    
    def _recommend_instance_type(self, current_type: str, utilization: Dict):
        &quot;&quot;&quot;Recommend optimal instance type&quot;&quot;&quot;
        # Parse current instance family and size
        family, size = self._parse_instance_type(current_type)
        
        # Calculate required resources
        required_cpu = self._calculate_required_cpu(utilization[&#39;cpu&#39;])
        required_memory = self._calculate_required_memory(utilization[&#39;memory&#39;])
        
        # Find best matching instance
        instance_catalog = self._get_instance_catalog()
        
        candidates = []
        for instance_type, specs in instance_catalog.items():
            if (specs[&#39;vcpu&#39;] &gt;= required_cpu and 
                specs[&#39;memory&#39;] &gt;= required_memory):
                candidates.append({
                    &#39;type&#39;: instance_type,
                    &#39;cost&#39;: specs[&#39;cost&#39;],
                    &#39;vcpu&#39;: specs[&#39;vcpu&#39;],
                    &#39;memory&#39;: specs[&#39;memory&#39;],
                    &#39;efficiency_score&#39;: self._calculate_efficiency_score(
                        specs, required_cpu, required_memory
                    )
                })
        
        # Select best candidate
        if candidates:
            best = sorted(candidates, 
                         key=lambda x: (x[&#39;efficiency_score&#39;], x[&#39;cost&#39;]))[0]
            return best[&#39;type&#39;]
        
        return current_type
    
    def create_rightsizing_automation(self):
        &quot;&quot;&quot;Automated rightsizing implementation&quot;&quot;&quot;
        return &#39;&#39;&#39;
import boto3
from datetime import datetime
import logging

class AutomatedRightsizer:
    def __init__(self):
        self.ec2 = boto3.client(&#39;ec2&#39;)
        self.cloudwatch = boto3.client(&#39;cloudwatch&#39;)
        self.logger = logging.getLogger(__name__)
        
    def execute_rightsizing(self, recommendations: List[Dict], dry_run: bool = True):
        &quot;&quot;&quot;Execute rightsizing recommendations&quot;&quot;&quot;
        results = []
        
        for recommendation in recommendations:
            try:
                if recommendation[&#39;risk&#39;] == &#39;low&#39; or self._get_approval(recommendation):
                    result = self._resize_instance(
                        recommendation[&#39;resource_id&#39;],
                        recommendation[&#39;recommended_type&#39;],
                        dry_run=dry_run
                    )
                    results.append(result)
            except Exception as e:
                self.logger.error(f&quot;Failed to resize {recommendation[&#39;resource_id&#39;]}: {e}&quot;)
                
        return results
    
    def _resize_instance(self, instance_id: str, new_type: str, dry_run: bool):
        &quot;&quot;&quot;Resize an EC2 instance&quot;&quot;&quot;
        # Create snapshot for rollback
        snapshot_id = self._create_snapshot(instance_id)
        
        try:
            # Stop instance
            if not dry_run:
                self.ec2.stop_instances(InstanceIds=[instance_id])
                self._wait_for_state(instance_id, &#39;stopped&#39;)
            
            # Change instance type
            self.ec2.modify_instance_attribute(
                InstanceId=instance_id,
                InstanceType={&#39;Value&#39;: new_type},
                DryRun=dry_run
            )
            
            # Start instance
            if not dry_run:
                self.ec2.start_instances(InstanceIds=[instance_id])
                self._wait_for_state(instance_id, &#39;running&#39;)
            
            return {
                &#39;instance_id&#39;: instance_id,
                &#39;status&#39;: &#39;success&#39;,
                &#39;new_type&#39;: new_type,
                &#39;snapshot_id&#39;: snapshot_id
            }
            
        except Exception as e:
            # Rollback on failure
            if not dry_run:
                self._rollback_instance(instance_id, snapshot_id)
            raise
&#39;&#39;&#39;
```

### 3. Reserved Instances and Savings Plans

Optimize commitment-based discounts:

**Reservation Optimizer**
```python
class ReservationOptimizer:
    def __init__(self):
        self.usage_history = None
        self.existing_reservations = None
        
    def analyze_reservation_opportunities(self):
        &quot;&quot;&quot;Analyze opportunities for reservations&quot;&quot;&quot;
        analysis = {
            &#39;current_coverage&#39;: self._analyze_current_coverage(),
            &#39;usage_patterns&#39;: self._analyze_usage_patterns(),
            &#39;recommendations&#39;: self._generate_recommendations(),
            &#39;roi_analysis&#39;: self._calculate_roi(),
            &#39;risk_assessment&#39;: self._assess_commitment_risk()
        }
        
        return analysis
    
    def _analyze_usage_patterns(self):
        &quot;&quot;&quot;Analyze historical usage patterns&quot;&quot;&quot;
        # Get 12 months of usage data
        usage_data = self._get_historical_usage(months=12)
        
        patterns = {
            &#39;stable_workloads&#39;: [],
            &#39;variable_workloads&#39;: [],
            &#39;seasonal_patterns&#39;: [],
            &#39;growth_trends&#39;: []
        }
        
        # Analyze each instance family
        for family in self._get_instance_families(usage_data):
            family_usage = self._filter_by_family(usage_data, family)
            
            # Calculate stability metrics
            stability = self._calculate_stability(family_usage)
            
            if stability[&#39;coefficient_of_variation&#39;] &lt; 0.1:
                patterns[&#39;stable_workloads&#39;].append({
                    &#39;family&#39;: family,
                    &#39;average_usage&#39;: stability[&#39;mean&#39;],
                    &#39;min_usage&#39;: stability[&#39;min&#39;],
                    &#39;recommendation&#39;: &#39;reserved_instance&#39;,
                    &#39;term&#39;: &#39;3_year&#39;,
                    &#39;payment&#39;: &#39;all_upfront&#39;
                })
            elif stability[&#39;coefficient_of_variation&#39;] &lt; 0.3:
                patterns[&#39;variable_workloads&#39;].append({
                    &#39;family&#39;: family,
                    &#39;average_usage&#39;: stability[&#39;mean&#39;],
                    &#39;baseline&#39;: stability[&#39;percentile_25&#39;],
                    &#39;recommendation&#39;: &#39;savings_plan&#39;,
                    &#39;commitment&#39;: stability[&#39;percentile_25&#39;]
                })
            
            # Check for seasonal patterns
            if self._has_seasonal_pattern(family_usage):
                patterns[&#39;seasonal_patterns&#39;].append({
                    &#39;family&#39;: family,
                    &#39;pattern&#39;: self._identify_seasonal_pattern(family_usage),
                    &#39;recommendation&#39;: &#39;spot_with_savings_plan_baseline&#39;
                })
        
        return patterns
    
    def _generate_recommendations(self):
        &quot;&quot;&quot;Generate reservation recommendations&quot;&quot;&quot;
        recommendations = []
        
        patterns = self._analyze_usage_patterns()
        current_costs = self._calculate_current_costs()
        
        # Reserved Instance recommendations
        for workload in patterns[&#39;stable_workloads&#39;]:
            ri_options = self._calculate_ri_options(workload)
            
            for option in ri_options:
                savings = current_costs[workload[&#39;family&#39;]] - option[&#39;total_cost&#39;]
                
                if savings &gt; 0:
                    recommendations.append({
                        &#39;type&#39;: &#39;reserved_instance&#39;,
                        &#39;family&#39;: workload[&#39;family&#39;],
                        &#39;quantity&#39;: option[&#39;quantity&#39;],
                        &#39;term&#39;: option[&#39;term&#39;],
                        &#39;payment&#39;: option[&#39;payment_option&#39;],
                        &#39;upfront_cost&#39;: option[&#39;upfront_cost&#39;],
                        &#39;monthly_cost&#39;: option[&#39;monthly_cost&#39;],
                        &#39;total_savings&#39;: savings,
                        &#39;break_even_months&#39;: option[&#39;upfront_cost&#39;] / (savings / 36),
                        &#39;confidence&#39;: &#39;high&#39;
                    })
        
        # Savings Plan recommendations
        for workload in patterns[&#39;variable_workloads&#39;]:
            sp_options = self._calculate_savings_plan_options(workload)
            
            for option in sp_options:
                recommendations.append({
                    &#39;type&#39;: &#39;savings_plan&#39;,
                    &#39;commitment_type&#39;: option[&#39;type&#39;],
                    &#39;hourly_commitment&#39;: option[&#39;commitment&#39;],
                    &#39;term&#39;: option[&#39;term&#39;],
                    &#39;estimated_savings&#39;: option[&#39;savings&#39;],
                    &#39;flexibility&#39;: option[&#39;flexibility_score&#39;],
                    &#39;confidence&#39;: &#39;medium&#39;
                })
        
        return sorted(recommendations, key=lambda x: x.get(&#39;total_savings&#39;, 0), reverse=True)
    
    def create_reservation_dashboard(self):
        &quot;&quot;&quot;Create reservation tracking dashboard&quot;&quot;&quot;
        return &#39;&#39;&#39;
&lt;!DOCTYPE html&gt;
&lt;html&gt;
&lt;head&gt;
    &lt;title&gt;Reservation &amp; Savings Dashboard&lt;/title&gt;
    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/chart.js&quot;&gt;&lt;/script&gt;
&lt;/head&gt;
&lt;body&gt;
    &lt;div class=&quot;dashboard&quot;&gt;
        &lt;div class=&quot;summary-cards&quot;&gt;
            &lt;div class=&quot;card&quot;&gt;
                &lt;h3&gt;Current Coverage&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;{coverage_percentage}%&lt;/div&gt;
                &lt;div class=&quot;sub-metric&quot;&gt;On-Demand: ${on_demand_cost}&lt;/div&gt;
                &lt;div class=&quot;sub-metric&quot;&gt;Reserved: ${reserved_cost}&lt;/div&gt;
            &lt;/div&gt;
            
            &lt;div class=&quot;card&quot;&gt;
                &lt;h3&gt;Potential Savings&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;${potential_savings}/month&lt;/div&gt;
                &lt;div class=&quot;sub-metric&quot;&gt;{recommendations_count} opportunities&lt;/div&gt;
            &lt;/div&gt;
            
            &lt;div class=&quot;card&quot;&gt;
                &lt;h3&gt;Expiring Soon&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;{expiring_count} RIs&lt;/div&gt;
                &lt;div class=&quot;sub-metric&quot;&gt;Next 30 days&lt;/div&gt;
            &lt;/div&gt;
        &lt;/div&gt;
        
        &lt;div class=&quot;charts&quot;&gt;
            &lt;canvas id=&quot;coverageChart&quot;&gt;&lt;/canvas&gt;
            &lt;canvas id=&quot;savingsChart&quot;&gt;&lt;/canvas&gt;
        &lt;/div&gt;
        
        &lt;div class=&quot;recommendations-table&quot;&gt;
            &lt;h3&gt;Top Recommendations&lt;/h3&gt;
            &lt;table&gt;
                &lt;tr&gt;
                    &lt;th&gt;Type&lt;/th&gt;
                    &lt;th&gt;Resource&lt;/th&gt;
                    &lt;th&gt;Term&lt;/th&gt;
                    &lt;th&gt;Upfront&lt;/th&gt;
                    &lt;th&gt;Monthly Savings&lt;/th&gt;
                    &lt;th&gt;ROI&lt;/th&gt;
                    &lt;th&gt;Action&lt;/th&gt;
                &lt;/tr&gt;
                {recommendation_rows}
            &lt;/table&gt;
        &lt;/div&gt;
    &lt;/div&gt;
&lt;/body&gt;
&lt;/html&gt;
&#39;&#39;&#39;
```

### 4. Spot Instance Optimization

Leverage spot instances effectively:

**Spot Instance Manager**
```python
class SpotInstanceOptimizer:
    def __init__(self):
        self.spot_advisor = self._init_spot_advisor()
        self.interruption_handler = None
        
    def identify_spot_opportunities(self):
        &quot;&quot;&quot;Identify workloads suitable for spot&quot;&quot;&quot;
        workloads = self._analyze_workloads()
        
        spot_candidates = {
            &#39;batch_processing&#39;: [],
            &#39;dev_test&#39;: [],
            &#39;stateless_apps&#39;: [],
            &#39;ci_cd&#39;: [],
            &#39;data_processing&#39;: []
        }
        
        for workload in workloads:
            suitability = self._assess_spot_suitability(workload)
            
            if suitability[&#39;score&#39;] &gt; 0.7:
                spot_candidates[workload[&#39;type&#39;]].append({
                    &#39;workload&#39;: workload[&#39;name&#39;],
                    &#39;current_cost&#39;: workload[&#39;cost&#39;],
                    &#39;spot_savings&#39;: workload[&#39;cost&#39;] * 0.7,  # ~70% savings
                    &#39;interruption_tolerance&#39;: suitability[&#39;interruption_tolerance&#39;],
                    &#39;recommended_strategy&#39;: self._recommend_spot_strategy(workload)
                })
        
        return spot_candidates
    
    def _recommend_spot_strategy(self, workload):
        &quot;&quot;&quot;Recommend spot instance strategy&quot;&quot;&quot;
        if workload[&#39;interruption_tolerance&#39;] == &#39;high&#39;:
            return {
                &#39;strategy&#39;: &#39;spot_fleet_diverse&#39;,
                &#39;instance_pools&#39;: 10,
                &#39;allocation_strategy&#39;: &#39;capacity-optimized&#39;,
                &#39;on_demand_base&#39;: 0,
                &#39;spot_percentage&#39;: 100
            }
        elif workload[&#39;interruption_tolerance&#39;] == &#39;medium&#39;:
            return {
                &#39;strategy&#39;: &#39;mixed_instances&#39;,
                &#39;on_demand_base&#39;: 25,
                &#39;spot_percentage&#39;: 75,
                &#39;spot_allocation&#39;: &#39;lowest-price&#39;
            }
        else:
            return {
                &#39;strategy&#39;: &#39;spot_with_fallback&#39;,
                &#39;primary&#39;: &#39;spot&#39;,
                &#39;fallback&#39;: &#39;on-demand&#39;,
                &#39;checkpointing&#39;: True
            }
    
    def create_spot_configuration(self):
        &quot;&quot;&quot;Create spot instance configuration&quot;&quot;&quot;
        return &#39;&#39;&#39;
# Terraform configuration for Spot instances
resource &quot;aws_spot_fleet_request&quot; &quot;processing_fleet&quot; {
  iam_fleet_role = aws_iam_role.spot_fleet.arn
  
  allocation_strategy = &quot;diversified&quot;
  target_capacity     = 100
  valid_until        = timeadd(timestamp(), &quot;168h&quot;)
  
  # Define multiple launch specifications for diversity
  dynamic &quot;launch_specification&quot; {
    for_each = var.spot_instance_types
    
    content {
      instance_type     = launch_specification.value
      ami              = var.ami_id
      key_name         = var.key_name
      subnet_id        = var.subnet_ids[launch_specification.key % length(var.subnet_ids)]
      
      weighted_capacity = var.instance_weights[launch_specification.value]
      spot_price       = var.max_spot_prices[launch_specification.value]
      
      user_data = base64encode(templatefile(&quot;${path.module}/spot-init.sh&quot;, {
        interruption_handler = true
        checkpoint_s3_bucket = var.checkpoint_bucket
      }))
      
      tags = {
        Name = &quot;spot-processing-${launch_specification.key}&quot;
        Type = &quot;spot&quot;
      }
    }
  }
  
  # Interruption handling
  lifecycle {
    create_before_destroy = true
  }
}

# Spot interruption handler
resource &quot;aws_lambda_function&quot; &quot;spot_interruption_handler&quot; {
  filename         = &quot;spot-handler.zip&quot;
  function_name    = &quot;spot-interruption-handler&quot;
  role            = aws_iam_role.lambda_role.arn
  handler         = &quot;handler.main&quot;
  runtime         = &quot;python3.9&quot;
  
  environment {
    variables = {
      CHECKPOINT_BUCKET = var.checkpoint_bucket
      SNS_TOPIC_ARN    = aws_sns_topic.spot_interruptions.arn
    }
  }
}
&#39;&#39;&#39;
```

### 5. Storage Optimization

Optimize storage costs:

**Storage Optimizer**
```python
class StorageOptimizer:
    def analyze_storage_costs(self):
        &quot;&quot;&quot;Comprehensive storage analysis&quot;&quot;&quot;
        analysis = {
            &#39;ebs_volumes&#39;: self._analyze_ebs_volumes(),
            &#39;s3_buckets&#39;: self._analyze_s3_buckets(),
            &#39;snapshots&#39;: self._analyze_snapshots(),
            &#39;lifecycle_opportunities&#39;: self._find_lifecycle_opportunities(),
            &#39;compression_opportunities&#39;: self._find_compression_opportunities()
        }
        
        return analysis
    
    def _analyze_s3_buckets(self):
        &quot;&quot;&quot;Analyze S3 bucket costs and optimization&quot;&quot;&quot;
        s3 = boto3.client(&#39;s3&#39;)
        cloudwatch = boto3.client(&#39;cloudwatch&#39;)
        
        buckets = s3.list_buckets()[&#39;Buckets&#39;]
        bucket_analysis = []
        
        for bucket in buckets:
            bucket_name = bucket[&#39;Name&#39;]
            
            # Get storage metrics
            metrics = self._get_s3_metrics(bucket_name)
            
            # Analyze storage classes
            storage_class_distribution = self._get_storage_class_distribution(bucket_name)
            
            # Calculate optimization potential
            optimization = self._calculate_s3_optimization(
                bucket_name,
                metrics,
                storage_class_distribution
            )
            
            bucket_analysis.append({
                &#39;bucket_name&#39;: bucket_name,
                &#39;total_size_gb&#39;: metrics[&#39;size_gb&#39;],
                &#39;total_objects&#39;: metrics[&#39;object_count&#39;],
                &#39;current_cost&#39;: metrics[&#39;monthly_cost&#39;],
                &#39;storage_classes&#39;: storage_class_distribution,
                &#39;optimization_recommendations&#39;: optimization[&#39;recommendations&#39;],
                &#39;potential_savings&#39;: optimization[&#39;savings&#39;]
            })
        
        return bucket_analysis
    
    def create_lifecycle_policies(self):
        &quot;&quot;&quot;Create S3 lifecycle policies&quot;&quot;&quot;
        return &#39;&#39;&#39;
import boto3
from datetime import datetime

class S3LifecycleManager:
    def __init__(self):
        self.s3 = boto3.client(&#39;s3&#39;)
        
    def create_intelligent_lifecycle(self, bucket_name: str, access_patterns: Dict):
        &quot;&quot;&quot;Create lifecycle policy based on access patterns&quot;&quot;&quot;
        
        rules = []
        
        # Intelligent tiering for unknown access patterns
        if access_patterns.get(&#39;unpredictable&#39;):
            rules.append({
                &#39;ID&#39;: &#39;intelligent-tiering&#39;,
                &#39;Status&#39;: &#39;Enabled&#39;,
                &#39;Transitions&#39;: [{
                    &#39;Days&#39;: 1,
                    &#39;StorageClass&#39;: &#39;INTELLIGENT_TIERING&#39;
                }]
            })
        
        # Standard lifecycle for predictable patterns
        if access_patterns.get(&#39;predictable&#39;):
            rules.append({
                &#39;ID&#39;: &#39;standard-lifecycle&#39;,
                &#39;Status&#39;: &#39;Enabled&#39;,
                &#39;Transitions&#39;: [
                    {
                        &#39;Days&#39;: 30,
                        &#39;StorageClass&#39;: &#39;STANDARD_IA&#39;
                    },
                    {
                        &#39;Days&#39;: 90,
                        &#39;StorageClass&#39;: &#39;GLACIER&#39;
                    },
                    {
                        &#39;Days&#39;: 180,
                        &#39;StorageClass&#39;: &#39;DEEP_ARCHIVE&#39;
                    }
                ]
            })
        
        # Delete old versions
        rules.append({
            &#39;ID&#39;: &#39;delete-old-versions&#39;,
            &#39;Status&#39;: &#39;Enabled&#39;,
            &#39;NoncurrentVersionTransitions&#39;: [
                {
                    &#39;NoncurrentDays&#39;: 30,
                    &#39;StorageClass&#39;: &#39;GLACIER&#39;
                }
            ],
            &#39;NoncurrentVersionExpiration&#39;: {
                &#39;NoncurrentDays&#39;: 90
            }
        })
        
        # Apply lifecycle configuration
        self.s3.put_bucket_lifecycle_configuration(
            Bucket=bucket_name,
            LifecycleConfiguration={&#39;Rules&#39;: rules}
        )
        
        return rules
    
    def optimize_ebs_volumes(self):
        &quot;&quot;&quot;Optimize EBS volume types and sizes&quot;&quot;&quot;
        ec2 = boto3.client(&#39;ec2&#39;)
        
        volumes = ec2.describe_volumes()[&#39;Volumes&#39;]
        optimizations = []
        
        for volume in volumes:
            # Analyze volume metrics
            iops_usage = self._get_volume_iops_usage(volume[&#39;VolumeId&#39;])
            throughput_usage = self._get_volume_throughput_usage(volume[&#39;VolumeId&#39;])
            
            current_type = volume[&#39;VolumeType&#39;]
            recommended_type = self._recommend_volume_type(
                iops_usage,
                throughput_usage,
                volume[&#39;Size&#39;]
            )
            
            if recommended_type != current_type:
                optimizations.append({
                    &#39;volume_id&#39;: volume[&#39;VolumeId&#39;],
                    &#39;current_type&#39;: current_type,
                    &#39;recommended_type&#39;: recommended_type,
                    &#39;reason&#39;: self._get_optimization_reason(
                        current_type,
                        recommended_type,
                        iops_usage,
                        throughput_usage
                    ),
                    &#39;monthly_savings&#39;: self._calculate_volume_savings(
                        volume,
                        recommended_type
                    )
                })
        
        return optimizations
&#39;&#39;&#39;
```

### 6. Network Cost Optimization

Reduce network transfer costs:

**Network Cost Optimizer**
```python
class NetworkCostOptimizer:
    def analyze_network_costs(self):
        &quot;&quot;&quot;Analyze network transfer costs&quot;&quot;&quot;
        analysis = {
            &#39;data_transfer_costs&#39;: self._analyze_data_transfer(),
            &#39;nat_gateway_costs&#39;: self._analyze_nat_gateways(),
            &#39;load_balancer_costs&#39;: self._analyze_load_balancers(),
            &#39;vpc_endpoint_opportunities&#39;: self._find_vpc_endpoint_opportunities(),
            &#39;cdn_optimization&#39;: self._analyze_cdn_usage()
        }
        
        return analysis
    
    def _analyze_data_transfer(self):
        &quot;&quot;&quot;Analyze data transfer patterns and costs&quot;&quot;&quot;
        transfers = {
            &#39;inter_region&#39;: self._get_inter_region_transfers(),
            &#39;internet_egress&#39;: self._get_internet_egress(),
            &#39;inter_az&#39;: self._get_inter_az_transfers(),
            &#39;vpc_peering&#39;: self._get_vpc_peering_transfers()
        }
        
        recommendations = []
        
        # Analyze inter-region transfers
        if transfers[&#39;inter_region&#39;][&#39;monthly_gb&#39;] &gt; 1000:
            recommendations.append({
                &#39;type&#39;: &#39;region_consolidation&#39;,
                &#39;description&#39;: &#39;Consider consolidating resources in fewer regions&#39;,
                &#39;current_cost&#39;: transfers[&#39;inter_region&#39;][&#39;monthly_cost&#39;],
                &#39;potential_savings&#39;: transfers[&#39;inter_region&#39;][&#39;monthly_cost&#39;] * 0.8
            })
        
        # Analyze internet egress
        if transfers[&#39;internet_egress&#39;][&#39;monthly_gb&#39;] &gt; 10000:
            recommendations.append({
                &#39;type&#39;: &#39;cdn_implementation&#39;,
                &#39;description&#39;: &#39;Implement CDN to reduce origin egress&#39;,
                &#39;current_cost&#39;: transfers[&#39;internet_egress&#39;][&#39;monthly_cost&#39;],
                &#39;potential_savings&#39;: transfers[&#39;internet_egress&#39;][&#39;monthly_cost&#39;] * 0.6
            })
        
        return {
            &#39;current_costs&#39;: transfers,
            &#39;recommendations&#39;: recommendations
        }
    
    def create_network_optimization_script(self):
        &quot;&quot;&quot;Script to implement network optimizations&quot;&quot;&quot;
        return &#39;&#39;&#39;
#!/usr/bin/env python3
import boto3
from collections import defaultdict

class NetworkOptimizer:
    def __init__(self):
        self.ec2 = boto3.client(&#39;ec2&#39;)
        self.cloudwatch = boto3.client(&#39;cloudwatch&#39;)
        
    def optimize_nat_gateways(self):
        &quot;&quot;&quot;Consolidate and optimize NAT gateways&quot;&quot;&quot;
        # Get all NAT gateways
        nat_gateways = self.ec2.describe_nat_gateways()[&#39;NatGateways&#39;]
        
        # Group by VPC
        vpc_nat_gateways = defaultdict(list)
        for nat in nat_gateways:
            if nat[&#39;State&#39;] == &#39;available&#39;:
                vpc_nat_gateways[nat[&#39;VpcId&#39;]].append(nat)
        
        optimizations = []
        
        for vpc_id, nats in vpc_nat_gateways.items():
            if len(nats) &gt; 1:
                # Check if consolidation is possible
                traffic_analysis = self._analyze_nat_traffic(nats)
                
                if traffic_analysis[&#39;can_consolidate&#39;]:
                    optimizations.append({
                        &#39;vpc_id&#39;: vpc_id,
                        &#39;action&#39;: &#39;consolidate_nat&#39;,
                        &#39;current_count&#39;: len(nats),
                        &#39;recommended_count&#39;: traffic_analysis[&#39;recommended_count&#39;],
                        &#39;monthly_savings&#39;: (len(nats) - traffic_analysis[&#39;recommended_count&#39;]) * 45
                    })
        
        return optimizations
    
    def implement_vpc_endpoints(self):
        &quot;&quot;&quot;Implement VPC endpoints for AWS services&quot;&quot;&quot;
        services_to_check = [&#39;s3&#39;, &#39;dynamodb&#39;, &#39;ec2&#39;, &#39;sns&#39;, &#39;sqs&#39;]
        vpc_list = self.ec2.describe_vpcs()[&#39;Vpcs&#39;]
        
        implementations = []
        
        for vpc in vpc_list:
            vpc_id = vpc[&#39;VpcId&#39;]
            
            # Check existing endpoints
            existing = self._get_existing_endpoints(vpc_id)
            
            for service in services_to_check:
                if service not in existing:
                    # Check if service is being used
                    if self._is_service_used(vpc_id, service):
                        # Create VPC endpoint
                        endpoint = self._create_vpc_endpoint(vpc_id, service)
                        
                        implementations.append({
                            &#39;vpc_id&#39;: vpc_id,
                            &#39;service&#39;: service,
                            &#39;endpoint_id&#39;: endpoint[&#39;VpcEndpointId&#39;],
                            &#39;estimated_savings&#39;: self._estimate_endpoint_savings(vpc_id, service)
                        })
        
        return implementations
    
    def optimize_cloudfront_distribution(self):
        &quot;&quot;&quot;Optimize CloudFront for cost reduction&quot;&quot;&quot;
        cloudfront = boto3.client(&#39;cloudfront&#39;)
        
        distributions = cloudfront.list_distributions()
        optimizations = []
        
        for dist in distributions.get(&#39;DistributionList&#39;, {}).get(&#39;Items&#39;, []):
            # Analyze distribution patterns
            analysis = self._analyze_distribution(dist[&#39;Id&#39;])
            
            if analysis[&#39;optimization_potential&#39;]:
                optimizations.append({
                    &#39;distribution_id&#39;: dist[&#39;Id&#39;],
                    &#39;recommendations&#39;: [
                        {
                            &#39;action&#39;: &#39;adjust_price_class&#39;,
                            &#39;current&#39;: dist[&#39;PriceClass&#39;],
                            &#39;recommended&#39;: analysis[&#39;recommended_price_class&#39;],
                            &#39;savings&#39;: analysis[&#39;price_class_savings&#39;]
                        },
                        {
                            &#39;action&#39;: &#39;optimize_cache_behaviors&#39;,
                            &#39;cache_improvements&#39;: analysis[&#39;cache_improvements&#39;],
                            &#39;savings&#39;: analysis[&#39;cache_savings&#39;]
                        }
                    ]
                })
        
        return optimizations
&#39;&#39;&#39;
```

### 7. Container Cost Optimization

Optimize container workloads:

**Container Cost Optimizer**
```python
class ContainerCostOptimizer:
    def optimize_ecs_costs(self):
        &quot;&quot;&quot;Optimize ECS/Fargate costs&quot;&quot;&quot;
        return {
            &#39;cluster_optimization&#39;: self._optimize_clusters(),
            &#39;task_rightsizing&#39;: self._rightsize_tasks(),
            &#39;scheduling_optimization&#39;: self._optimize_scheduling(),
            &#39;fargate_spot&#39;: self._implement_fargate_spot()
        }
    
    def _rightsize_tasks(self):
        &quot;&quot;&quot;Rightsize ECS tasks&quot;&quot;&quot;
        ecs = boto3.client(&#39;ecs&#39;)
        cloudwatch = boto3.client(&#39;cloudwatch&#39;)
        
        clusters = ecs.list_clusters()[&#39;clusterArns&#39;]
        recommendations = []
        
        for cluster in clusters:
            # Get services
            services = ecs.list_services(cluster=cluster)[&#39;serviceArns&#39;]
            
            for service in services:
                # Get task definition
                service_detail = ecs.describe_services(
                    cluster=cluster,
                    services=[service]
                )[&#39;services&#39;][0]
                
                task_def = service_detail[&#39;taskDefinition&#39;]
                
                # Analyze resource utilization
                utilization = self._analyze_task_utilization(cluster, service)
                
                # Generate recommendations
                if utilization[&#39;cpu&#39;][&#39;average&#39;] &lt; 30 or utilization[&#39;memory&#39;][&#39;average&#39;] &lt; 40:
                    recommendations.append({
                        &#39;cluster&#39;: cluster,
                        &#39;service&#39;: service,
                        &#39;current_cpu&#39;: service_detail[&#39;cpu&#39;],
                        &#39;current_memory&#39;: service_detail[&#39;memory&#39;],
                        &#39;recommended_cpu&#39;: int(service_detail[&#39;cpu&#39;] * 0.7),
                        &#39;recommended_memory&#39;: int(service_detail[&#39;memory&#39;] * 0.8),
                        &#39;monthly_savings&#39;: self._calculate_task_savings(
                            service_detail,
                            utilization
                        )
                    })
        
        return recommendations
    
    def create_k8s_cost_optimization(self):
        &quot;&quot;&quot;Kubernetes cost optimization&quot;&quot;&quot;
        return &#39;&#39;&#39;
apiVersion: v1
kind: ConfigMap
metadata:
  name: cost-optimization-config
data:
  vertical-pod-autoscaler.yaml: |
    apiVersion: autoscaling.k8s.io/v1
    kind: VerticalPodAutoscaler
    metadata:
      name: app-vpa
    spec:
      targetRef:
        apiVersion: apps/v1
        kind: Deployment
        name: app-deployment
      updatePolicy:
        updateMode: &quot;Auto&quot;
      resourcePolicy:
        containerPolicies:
        - containerName: app
          minAllowed:
            cpu: 100m
            memory: 128Mi
          maxAllowed:
            cpu: 2
            memory: 2Gi
  
  cluster-autoscaler-config.yaml: |
    apiVersion: apps/v1
    kind: Deployment
    metadata:
      name: cluster-autoscaler
    spec:
      template:
        spec:
          containers:
          - image: k8s.gcr.io/autoscaling/cluster-autoscaler:v1.21.0
            name: cluster-autoscaler
            command:
            - ./cluster-autoscaler
            - --v=4
            - --stderrthreshold=info
            - --cloud-provider=aws
            - --skip-nodes-with-local-storage=false
            - --expander=priority
            - --node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/cluster-name
            - --scale-down-enabled=true
            - --scale-down-unneeded-time=10m
            - --scale-down-utilization-threshold=0.5
  
  spot-instance-handler.yaml: |
    apiVersion: apps/v1
    kind: DaemonSet
    metadata:
      name: aws-node-termination-handler
    spec:
      selector:
        matchLabels:
          app: aws-node-termination-handler
      template:
        spec:
          containers:
          - name: aws-node-termination-handler
            image: amazon/aws-node-termination-handler:v1.13.0
            env:
            - name: NODE_NAME
              valueFrom:
                fieldRef:
                  fieldPath: spec.nodeName
            - name: ENABLE_SPOT_INTERRUPTION_DRAINING
              value: &quot;true&quot;
            - name: ENABLE_SCHEDULED_EVENT_DRAINING
              value: &quot;true&quot;
&#39;&#39;&#39;
```

### 8. Serverless Cost Optimization

Optimize serverless workloads:

**Serverless Optimizer**
```python
class ServerlessOptimizer:
    def optimize_lambda_costs(self):
        &quot;&quot;&quot;Optimize Lambda function costs&quot;&quot;&quot;
        lambda_client = boto3.client(&#39;lambda&#39;)
        cloudwatch = boto3.client(&#39;cloudwatch&#39;)
        
        functions = lambda_client.list_functions()[&#39;Functions&#39;]
        optimizations = []
        
        for function in functions:
            # Analyze function performance
            analysis = self._analyze_lambda_function(function)
            
            # Memory optimization
            if analysis[&#39;memory_optimization_possible&#39;]:
                optimizations.append({
                    &#39;function_name&#39;: function[&#39;FunctionName&#39;],
                    &#39;type&#39;: &#39;memory_optimization&#39;,
                    &#39;current_memory&#39;: function[&#39;MemorySize&#39;],
                    &#39;recommended_memory&#39;: analysis[&#39;optimal_memory&#39;],
                    &#39;estimated_savings&#39;: analysis[&#39;memory_savings&#39;]
                })
            
            # Timeout optimization
            if analysis[&#39;timeout_optimization_possible&#39;]:
                optimizations.append({
                    &#39;function_name&#39;: function[&#39;FunctionName&#39;],
                    &#39;type&#39;: &#39;timeout_optimization&#39;,
                    &#39;current_timeout&#39;: function[&#39;Timeout&#39;],
                    &#39;recommended_timeout&#39;: analysis[&#39;optimal_timeout&#39;],
                    &#39;risk_reduction&#39;: &#39;prevents unnecessary charges from hanging functions&#39;
                })
        
        return optimizations
    
    def implement_lambda_cost_controls(self):
        &quot;&quot;&quot;Implement Lambda cost controls&quot;&quot;&quot;
        return &#39;&#39;&#39;
import json
import boto3
from datetime import datetime

def lambda_cost_controller(event, context):
    &quot;&quot;&quot;Lambda function to monitor and control Lambda costs&quot;&quot;&quot;
    
    cloudwatch = boto3.client(&#39;cloudwatch&#39;)
    lambda_client = boto3.client(&#39;lambda&#39;)
    
    # Get current month costs
    costs = get_current_month_lambda_costs()
    
    # Check against budget
    budget_limit = float(os.environ.get(&#39;MONTHLY_BUDGET&#39;, &#39;1000&#39;))
    
    if costs &gt; budget_limit * 0.8:  # 80% of budget
        # Implement cost controls
        high_cost_functions = identify_high_cost_functions()
        
        for func in high_cost_functions:
            # Reduce concurrency
            lambda_client.put_function_concurrency(
                FunctionName=func[&#39;FunctionName&#39;],
                ReservedConcurrentExecutions=max(
                    1, 
                    int(func[&#39;CurrentConcurrency&#39;] * 0.5)
                )
            )
            
            # Alert
            send_cost_alert(func, costs, budget_limit)
    
    # Implement provisioned concurrency optimization
    optimize_provisioned_concurrency()
    
    return {
        &#39;statusCode&#39;: 200,
        &#39;body&#39;: json.dumps({
            &#39;current_costs&#39;: costs,
            &#39;budget_limit&#39;: budget_limit,
            &#39;actions_taken&#39;: len(high_cost_functions)
        })
    }

def optimize_provisioned_concurrency():
    &quot;&quot;&quot;Optimize provisioned concurrency based on usage patterns&quot;&quot;&quot;
    functions = get_functions_with_provisioned_concurrency()
    
    for func in functions:
        # Analyze invocation patterns
        patterns = analyze_invocation_patterns(func[&#39;FunctionName&#39;])
        
        if patterns[&#39;predictable&#39;]:
            # Schedule provisioned concurrency
            create_scheduled_scaling(
                func[&#39;FunctionName&#39;],
                patterns[&#39;peak_hours&#39;],
                patterns[&#39;peak_concurrency&#39;]
            )
        else:
            # Consider removing provisioned concurrency
            if patterns[&#39;avg_cold_starts&#39;] &lt; 10:  # per minute
                remove_provisioned_concurrency(func[&#39;FunctionName&#39;])
&#39;&#39;&#39;
```

### 9. Cost Allocation and Tagging

Implement cost allocation strategies:

**Cost Allocation Manager**
```python
class CostAllocationManager:
    def implement_tagging_strategy(self):
        &quot;&quot;&quot;Implement comprehensive tagging strategy&quot;&quot;&quot;
        return {
            &#39;required_tags&#39;: [
                {&#39;key&#39;: &#39;Environment&#39;, &#39;values&#39;: [&#39;prod&#39;, &#39;staging&#39;, &#39;dev&#39;, &#39;test&#39;]},
                {&#39;key&#39;: &#39;CostCenter&#39;, &#39;values&#39;: &#39;dynamic&#39;},
                {&#39;key&#39;: &#39;Project&#39;, &#39;values&#39;: &#39;dynamic&#39;},
                {&#39;key&#39;: &#39;Owner&#39;, &#39;values&#39;: &#39;dynamic&#39;},
                {&#39;key&#39;: &#39;Department&#39;, &#39;values&#39;: &#39;dynamic&#39;}
            ],
            &#39;automation&#39;: self._create_tagging_automation(),
            &#39;enforcement&#39;: self._create_tag_enforcement(),
            &#39;reporting&#39;: self._create_cost_allocation_reports()
        }
    
    def _create_tagging_automation(self):
        &quot;&quot;&quot;Automate resource tagging&quot;&quot;&quot;
        return &#39;&#39;&#39;
import boto3
from datetime import datetime

class AutoTagger:
    def __init__(self):
        self.tag_policies = self.load_tag_policies()
        
    def auto_tag_resources(self, event, context):
        &quot;&quot;&quot;Auto-tag resources on creation&quot;&quot;&quot;
        
        # Parse CloudTrail event
        detail = event[&#39;detail&#39;]
        event_name = detail[&#39;eventName&#39;]
        
        # Map events to resource types
        if event_name.startswith(&#39;Create&#39;):
            resource_arn = self.extract_resource_arn(detail)
            
            if resource_arn:
                # Determine tags
                tags = self.determine_tags(detail)
                
                # Apply tags
                self.apply_tags(resource_arn, tags)
                
                # Log tagging action
                self.log_tagging(resource_arn, tags)
    
    def determine_tags(self, event_detail):
        &quot;&quot;&quot;Determine tags based on context&quot;&quot;&quot;
        tags = []
        
        # User-based tags
        user_identity = event_detail.get(&#39;userIdentity&#39;, {})
        if &#39;userName&#39; in user_identity:
            tags.append({
                &#39;Key&#39;: &#39;Creator&#39;,
                &#39;Value&#39;: user_identity[&#39;userName&#39;]
            })
        
        # Time-based tags
        tags.append({
            &#39;Key&#39;: &#39;CreatedDate&#39;,
            &#39;Value&#39;: datetime.now().strftime(&#39;%Y-%m-%d&#39;)
        })
        
        # Environment inference
        if &#39;prod&#39; in event_detail.get(&#39;sourceIPAddress&#39;, &#39;&#39;):
            env = &#39;prod&#39;
        elif &#39;dev&#39; in event_detail.get(&#39;sourceIPAddress&#39;, &#39;&#39;):
            env = &#39;dev&#39;
        else:
            env = &#39;unknown&#39;
            
        tags.append({
            &#39;Key&#39;: &#39;Environment&#39;,
            &#39;Value&#39;: env
        })
        
        return tags
    
    def create_cost_allocation_dashboard(self):
        &quot;&quot;&quot;Create cost allocation dashboard&quot;&quot;&quot;
        return &quot;&quot;&quot;
        SELECT 
            tags.environment,
            tags.department,
            tags.project,
            SUM(costs.amount) as total_cost,
            SUM(costs.amount) / SUM(SUM(costs.amount)) OVER () * 100 as percentage
        FROM 
            aws_costs costs
        JOIN 
            resource_tags tags ON costs.resource_id = tags.resource_id
        WHERE 
            costs.date &gt;= DATE_TRUNC(&#39;month&#39;, CURRENT_DATE)
        GROUP BY 
            tags.environment,
            tags.department,
            tags.project
        ORDER BY 
            total_cost DESC
        &quot;&quot;&quot;
&#39;&#39;&#39;
```

### 10. Cost Monitoring and Alerts

Implement proactive cost monitoring:

**Cost Monitoring System**
```python
class CostMonitoringSystem:
    def setup_cost_alerts(self):
        &quot;&quot;&quot;Setup comprehensive cost alerting&quot;&quot;&quot;
        alerts = []
        
        # Budget alerts
        alerts.extend(self._create_budget_alerts())
        
        # Anomaly detection
        alerts.extend(self._create_anomaly_alerts())
        
        # Threshold alerts
        alerts.extend(self._create_threshold_alerts())
        
        # Forecast alerts
        alerts.extend(self._create_forecast_alerts())
        
        return alerts
    
    def _create_anomaly_alerts(self):
        &quot;&quot;&quot;Create anomaly detection alerts&quot;&quot;&quot;
        ce = boto3.client(&#39;ce&#39;)
        
        # Create anomaly monitor
        monitor = ce.create_anomaly_monitor(
            AnomalyMonitor={
                &#39;MonitorName&#39;: &#39;ServiceCostMonitor&#39;,
                &#39;MonitorType&#39;: &#39;DIMENSIONAL&#39;,
                &#39;MonitorDimension&#39;: &#39;SERVICE&#39;
            }
        )
        
        # Create anomaly subscription
        subscription = ce.create_anomaly_subscription(
            AnomalySubscription={
                &#39;SubscriptionName&#39;: &#39;CostAnomalyAlerts&#39;,
                &#39;Threshold&#39;: 100.0,  # Alert on anomalies &gt; $100
                &#39;Frequency&#39;: &#39;DAILY&#39;,
                &#39;MonitorArnList&#39;: [monitor[&#39;MonitorArn&#39;]],
                &#39;Subscribers&#39;: [
                    {
                        &#39;Type&#39;: &#39;EMAIL&#39;,
                        &#39;Address&#39;: &#39;team@company.com&#39;
                    },
                    {
                        &#39;Type&#39;: &#39;SNS&#39;,
                        &#39;Address&#39;: &#39;arn:aws:sns:us-east-1:123456789012:cost-alerts&#39;
                    }
                ]
            }
        )
        
        return [monitor, subscription]
    
    def create_cost_dashboard(self):
        &quot;&quot;&quot;Create executive cost dashboard&quot;&quot;&quot;
        return &#39;&#39;&#39;
&lt;!DOCTYPE html&gt;
&lt;html&gt;
&lt;head&gt;
    &lt;title&gt;Cloud Cost Dashboard&lt;/title&gt;
    &lt;script src=&quot;https://d3js.org/d3.v7.min.js&quot;&gt;&lt;/script&gt;
    &lt;style&gt;
        .metric-card {
            background: #f5f5f5;
            padding: 20px;
            margin: 10px;
            border-radius: 8px;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
        }
        .alert { color: #d32f2f; }
        .warning { color: #f57c00; }
        .success { color: #388e3c; }
    &lt;/style&gt;
&lt;/head&gt;
&lt;body&gt;
    &lt;div id=&quot;dashboard&quot;&gt;
        &lt;h1&gt;Cloud Cost Optimization Dashboard&lt;/h1&gt;
        
        &lt;div class=&quot;summary-row&quot;&gt;
            &lt;div class=&quot;metric-card&quot;&gt;
                &lt;h3&gt;Current Month Spend&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;${current_spend}&lt;/div&gt;
                &lt;div class=&quot;trend ${spend_trend_class}&quot;&gt;${spend_trend}% vs last month&lt;/div&gt;
            &lt;/div&gt;
            
            &lt;div class=&quot;metric-card&quot;&gt;
                &lt;h3&gt;Projected Month End&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;${projected_spend}&lt;/div&gt;
                &lt;div class=&quot;budget-status&quot;&gt;Budget: ${budget}&lt;/div&gt;
            &lt;/div&gt;
            
            &lt;div class=&quot;metric-card&quot;&gt;
                &lt;h3&gt;Optimization Opportunities&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;${total_savings_identified}&lt;/div&gt;
                &lt;div class=&quot;count&quot;&gt;{opportunity_count} recommendations&lt;/div&gt;
            &lt;/div&gt;
            
            &lt;div class=&quot;metric-card&quot;&gt;
                &lt;h3&gt;Realized Savings&lt;/h3&gt;
                &lt;div class=&quot;metric&quot;&gt;${realized_savings_mtd}&lt;/div&gt;
                &lt;div class=&quot;count&quot;&gt;YTD: ${realized_savings_ytd}&lt;/div&gt;
            &lt;/div&gt;
        &lt;/div&gt;
        
        &lt;div class=&quot;charts-row&quot;&gt;
            &lt;div id=&quot;spend-trend-chart&quot;&gt;&lt;/div&gt;
            &lt;div id=&quot;service-breakdown-chart&quot;&gt;&lt;/div&gt;
            &lt;div id=&quot;optimization-progress-chart&quot;&gt;&lt;/div&gt;
        &lt;/div&gt;
        
        &lt;div class=&quot;recommendations-section&quot;&gt;
            &lt;h2&gt;Top Optimization Recommendations&lt;/h2&gt;
            &lt;table id=&quot;recommendations-table&quot;&gt;
                &lt;thead&gt;
                    &lt;tr&gt;
                        &lt;th&gt;Priority&lt;/th&gt;
                        &lt;th&gt;Service&lt;/th&gt;
                        &lt;th&gt;Recommendation&lt;/th&gt;
                        &lt;th&gt;Monthly Savings&lt;/th&gt;
                        &lt;th&gt;Effort&lt;/th&gt;
                        &lt;th&gt;Action&lt;/th&gt;
                    &lt;/tr&gt;
                &lt;/thead&gt;
                &lt;tbody&gt;
                    ${recommendation_rows}
                &lt;/tbody&gt;
            &lt;/table&gt;
        &lt;/div&gt;
    &lt;/div&gt;
    
    &lt;script&gt;
        // Real-time updates
        setInterval(updateDashboard, 60000);
        
        // Initialize charts
        initializeCharts();
    &lt;/script&gt;
&lt;/body&gt;
&lt;/html&gt;
&#39;&#39;&#39;
```

## Output Format

1. **Cost Analysis Report**: Comprehensive breakdown of current cloud costs
2. **Optimization Recommendations**: Prioritized list of cost-saving opportunities
3. **Implementation Scripts**: Automated scripts for implementing optimizations
4. **Monitoring Dashboards**: Real-time cost tracking and alerting
5. **ROI Calculations**: Detailed savings projections and payback periods
6. **Risk Assessment**: Analysis of risks associated with each optimization
7. **Implementation Roadmap**: Phased approach to cost optimization
8. **Best Practices Guide**: Long-term cost management strategies

Focus on delivering immediate cost savings while establishing sustainable cost optimization practices that maintain performance and reliability standards.</pre>
                  </div>
                </div>
              </div>
          </div>

        </div>
      </div>
    </div>
  </div>
</div>

</template></turbo-stream>