AWS AI Cost Optimization

AWS provides a comprehensive suite of AI services, but costs can quickly escalate without proper optimization. This guide covers strategies to reduce AWS AI costs by 30-60% while maintaining performance and reliability.

Understanding AWS AI Cost Structure

AWS AI Services Cost Breakdown

AWS AI Cost Distribution:
├── Compute Services (60-70%)
│   ├── EC2 instances (GPU/CPU)
│   ├── SageMaker training/inference
│   └── Batch processing
├── Storage Services (15-25%)
│   ├── S3 data storage
│   ├── EBS volumes
│   └── Glacier archival
├── AI Services (10-20%)
│   ├── Rekognition
│   ├── Comprehend
│   ├── Translate
│   └── Personalize
└── Network & Data Transfer (5-10%)
    ├── Data transfer costs
    ├── API calls
    └── Cross-region traffic

Key Cost Drivers

• Instance Types: GPU instances are significantly more expensive
• Storage Classes: S3 storage costs vary by access frequency
• Data Transfer: Cross-region and outbound data transfer costs
• API Calls: Pay-per-use AI services can accumulate quickly
• Idle Resources: Unused instances and storage continue to incur costs

EC2 Instance Optimization

1. Instance Type Selection

GPU Instance Cost Comparison

# AWS GPU instance cost analysis
aws_gpu_instances = {
    'p3.2xlarge': {
        'vCPUs': 8,
        'GPUs': 1,
        'Memory': '61 GiB',
        'GPU_Memory': '16 GiB',
        'hourly_cost': 3.06,
        'monthly_cost': 2196.00,
        'best_for': ['Deep learning training', 'Large model inference']
    },
    'p3.8xlarge': {
        'vCPUs': 32,
        'GPUs': 4,
        'Memory': '244 GiB',
        'GPU_Memory': '64 GiB',
        'hourly_cost': 12.24,
        'monthly_cost': 8784.00,
        'best_for': ['Distributed training', 'Multi-GPU workloads']
    },
    'g4dn.xlarge': {
        'vCPUs': 4,
        'GPUs': 1,
        'Memory': '16 GiB',
        'GPU_Memory': '16 GiB',
        'hourly_cost': 0.526,
        'monthly_cost': 378.72,
        'best_for': ['Inference workloads', 'Cost-sensitive training']
    },
    'g4dn.2xlarge': {
        'vCPUs': 8,
        'GPUs': 1,
        'Memory': '32 GiB',
        'GPU_Memory': '16 GiB',
        'hourly_cost': 0.752,
        'monthly_cost': 541.44,
        'best_for': ['Balanced training/inference', 'Medium workloads']
    }
}

def select_optimal_gpu_instance(workload_type, budget, performance_requirements):
    """Select optimal GPU instance based on requirements"""
    if workload_type == "training" and budget > 2000:
        return "p3.2xlarge"  # High-performance training
    elif workload_type == "inference" and budget < 500:
        return "g4dn.xlarge"  # Cost-effective inference
    elif workload_type == "balanced":
        return "g4dn.2xlarge"  # Balanced option
    else:
        return "g4dn.xlarge"  # Default cost-effective choice

2. Spot Instance Strategy

Spot Instance Implementation

# Spot instance cost optimization
import boto3
from botocore.exceptions import ClientError

class SpotInstanceManager:
    def __init__(self, region='us-east-1'):
        self.ec2 = boto3.client('ec2', region_name=region)
        self.spot_prices = {}
    
    def get_spot_price(self, instance_type, availability_zone):
        """Get current spot price for instance type"""
        try:
            response = self.ec2.describe_spot_price_history(
                InstanceTypes=[instance_type],
                AvailabilityZone=availability_zone,
                MaxResults=1
            )
            return response['SpotPriceHistory'][0]['SpotPrice']
        except ClientError as e:
            print(f"Error getting spot price: {e}")
            return None
    
    def calculate_spot_savings(self, instance_type, on_demand_price):
        """Calculate potential spot instance savings"""
        spot_price = self.get_spot_price(instance_type, 'us-east-1a')
        if spot_price:
            savings_percentage = ((on_demand_price - float(spot_price)) / on_demand_price) * 100
            return {
                'spot_price': spot_price,
                'savings_percentage': savings_percentage,
                'monthly_savings': (on_demand_price - float(spot_price)) * 730
            }
        return None
    
    def create_spot_fleet(self, instance_types, target_capacity):
        """Create spot fleet for cost optimization"""
        fleet_config = {
            'SpotFleetRequestConfig': {
                'TargetCapacity': target_capacity,
                'IamFleetRole': 'arn:aws:iam::123456789012:role/spot-fleet-role',
                'LaunchSpecifications': []
            }
        }
        
        for instance_type in instance_types:
            spec = {
                'InstanceType': instance_type,
                'ImageId': 'ami-12345678',
                'SubnetId': 'subnet-12345678',
                'WeightedCapacity': 1,
                'SpotPrice': '0.50'  # Maximum bid price
            }
            fleet_config['SpotFleetRequestConfig']['LaunchSpecifications'].append(spec)
        
        return fleet_config

# Spot instance cost comparison
spot_savings_example = {
    'p3.2xlarge': {
        'on_demand_price': 3.06,
        'spot_price': 1.20,
        'savings_percentage': 61,
        'monthly_savings': 1357.80
    },
    'g4dn.xlarge': {
        'on_demand_price': 0.526,
        'spot_price': 0.15,
        'savings_percentage': 71,
        'monthly_savings': 274.88
    }
}

3. Reserved Instance Planning

Reserved Instance Strategy

# Reserved instance cost optimization
class ReservedInstanceOptimizer:
    def __init__(self):
        self.reservation_types = {
            'standard': {'discount': 0.30, 'commitment': '1-3 years'},
            'convertible': {'discount': 0.25, 'commitment': '1-3 years', 'flexible': True},
            'scheduled': {'discount': 0.20, 'commitment': '1 year', 'scheduled': True}
        }
    
    def calculate_reserved_savings(self, instance_type, usage_hours, reservation_type='standard'):
        """Calculate savings from reserved instances"""
        on_demand_cost = self.get_on_demand_cost(instance_type, usage_hours)
        reserved_cost = on_demand_cost * (1 - self.reservation_types[reservation_type]['discount'])
        
        return {
            'on_demand_cost': on_demand_cost,
            'reserved_cost': reserved_cost,
            'savings': on_demand_cost - reserved_cost,
            'savings_percentage': self.reservation_types[reservation_type]['discount'] * 100
        }
    
    def get_on_demand_cost(self, instance_type, hours):
        """Get on-demand cost for instance type"""
        hourly_rates = {
            'p3.2xlarge': 3.06,
            'g4dn.xlarge': 0.526,
            'c5.2xlarge': 0.34,
            'm5.large': 0.096
        }
        return hourly_rates.get(instance_type, 0) * hours

# Reserved instance example
reserved_instance_example = {
    'p3.2xlarge_1year': {
        'on_demand_monthly': 2196.00,
        'reserved_monthly': 1537.20,
        'savings_percentage': 30,
        'annual_savings': 7905.60
    },
    'g4dn.xlarge_3year': {
        'on_demand_monthly': 378.72,
        'reserved_monthly': 265.10,
        'savings_percentage': 30,
        'annual_savings': 1363.44
    }
}

SageMaker Cost Optimization

1. Training Cost Optimization

SageMaker Training Optimization

# SageMaker training cost optimization
import sagemaker
from sagemaker import get_execution_role

class SageMakerCostOptimizer:
    def __init__(self):
        self.sagemaker_session = sagemaker.Session()
        self.role = get_execution_role()
    
    def optimize_training_job(self, training_config):
        """Optimize SageMaker training job configuration"""
        optimized_config = {
            'instance_type': self.select_optimal_instance(training_config),
            'instance_count': self.calculate_optimal_instances(training_config),
            'volume_size': self.optimize_storage_size(training_config),
            'max_run': self.estimate_training_time(training_config),
            'use_spot_instances': True,
            'max_wait': 3600  # 1 hour max wait for spot instances
        }
        return optimized_config
    
    def select_optimal_instance(self, config):
        """Select optimal instance type for training"""
        data_size = config.get('data_size_gb', 0)
        model_complexity = config.get('model_complexity', 'medium')
        
        if data_size > 100 and model_complexity == 'high':
            return 'ml.p3.2xlarge'
        elif data_size > 50:
            return 'ml.g4dn.xlarge'
        else:
            return 'ml.c5.2xlarge'
    
    def calculate_optimal_instances(self, config):
        """Calculate optimal number of instances"""
        data_size = config.get('data_size_gb', 0)
        time_constraint = config.get('time_constraint_hours', 24)
        
        # Simple heuristic: more data = more instances
        if data_size > 500:
            return 4
        elif data_size > 100:
            return 2
        else:
            return 1

# SageMaker training cost comparison
sagemaker_training_costs = {
    'ml.p3.2xlarge': {
        'hourly_rate': 3.06,
        'training_time_8h': 24.48,
        'with_spot': 12.24,  # 50% savings
        'best_for': ['Large models', 'Fast training']
    },
    'ml.g4dn.xlarge': {
        'hourly_rate': 0.526,
        'training_time_8h': 4.21,
        'with_spot': 2.10,   # 50% savings
        'best_for': ['Medium models', 'Cost-sensitive training']
    },
    'ml.c5.2xlarge': {
        'hourly_rate': 0.34,
        'training_time_8h': 2.72,
        'with_spot': 1.36,   # 50% savings
        'best_for': ['Small models', 'CPU training']
    }
}

2. Inference Cost Optimization

SageMaker Inference Optimization

# SageMaker inference cost optimization
class SageMakerInferenceOptimizer:
    def __init__(self):
        self.inference_configs = {
            'real_time': {
                'instance_type': 'ml.g4dn.xlarge',
                'auto_scaling': True,
                'min_capacity': 1,
                'max_capacity': 10
            },
            'batch': {
                'instance_type': 'ml.c5.2xlarge',
                'auto_scaling': False,
                'instance_count': 2
            },
            'serverless': {
                'max_concurrency': 100,
                'memory_size': 2048
            }
        }
    
    def select_inference_config(self, workload_type, traffic_pattern):
        """Select optimal inference configuration"""
        if workload_type == 'real_time' and traffic_pattern == 'spiky':
            return self.inference_configs['serverless']
        elif workload_type == 'real_time' and traffic_pattern == 'steady':
            return self.inference_configs['real_time']
        else:
            return self.inference_configs['batch']
    
    def calculate_inference_costs(self, config, requests_per_month):
        """Calculate inference costs"""
        if config == self.inference_configs['serverless']:
            # Serverless pricing: $0.0000166667 per 100ms
            cost_per_request = 0.0000166667
            return requests_per_month * cost_per_request
        else:
            # Instance-based pricing
            hourly_rate = self.get_instance_hourly_rate(config['instance_type'])
            hours_per_month = 730
            return hourly_rate * hours_per_month

# Inference cost comparison
inference_cost_comparison = {
    'real_time_g4dn': {
        'monthly_cost': 378.72,
        'requests_per_month': 1000000,
        'cost_per_request': 0.00038,
        'latency': '50ms'
    },
    'serverless': {
        'monthly_cost': 166.67,
        'requests_per_month': 1000000,
        'cost_per_request': 0.00017,
        'latency': '100ms'
    },
    'batch_c5': {
        'monthly_cost': 248.20,
        'requests_per_month': 1000000,
        'cost_per_request': 0.00025,
        'latency': '500ms'
    }
}

Storage Optimization

1. S3 Storage Optimization

S3 Storage Class Strategy

# S3 storage optimization
import boto3
from botocore.exceptions import ClientError

class S3StorageOptimizer:
    def __init__(self):
        self.s3_client = boto3.client('s3')
        self.storage_classes = {
            'standard': {'cost_per_gb': 0.023, 'access': 'immediate'},
            'intelligent_tiering': {'cost_per_gb': 0.0125, 'access': 'automatic'},
            'standard_ia': {'cost_per_gb': 0.0125, 'access': 'hours'},
            'one_zone_ia': {'cost_per_gb': 0.01, 'access': 'hours'},
            'glacier': {'cost_per_gb': 0.004, 'access': 'hours_days'},
            'deep_archive': {'cost_per_gb': 0.00099, 'access': 'days_hours'}
        }
    
    def optimize_storage_class(self, access_pattern, data_size_gb):
        """Select optimal storage class based on access pattern"""
        if access_pattern['frequency'] == 'daily':
            return 'standard'
        elif access_pattern['frequency'] == 'weekly':
            return 'intelligent_tiering'
        elif access_pattern['frequency'] == 'monthly':
            return 'standard_ia'
        elif access_pattern['frequency'] == 'quarterly':
            return 'one_zone_ia'
        else:
            return 'glacier'
    
    def calculate_storage_savings(self, current_class, optimized_class, data_size_gb):
        """Calculate storage cost savings"""
        current_cost = self.storage_classes[current_class]['cost_per_gb'] * data_size_gb
        optimized_cost = self.storage_classes[optimized_class]['cost_per_gb'] * data_size_gb
        
        return {
            'current_cost': current_cost,
            'optimized_cost': optimized_cost,
            'savings': current_cost - optimized_cost,
            'savings_percentage': ((current_cost - optimized_cost) / current_cost) * 100
        }
    
    def setup_lifecycle_policy(self, bucket_name, prefix):
        """Setup S3 lifecycle policy for automatic optimization"""
        lifecycle_config = {
            'Rules': [
                {
                    'ID': 'AI_Data_Lifecycle',
                    'Status': 'Enabled',
                    'Filter': {'Prefix': prefix},
                    'Transitions': [
                        {
                            'Days': 30,
                            'StorageClass': 'STANDARD_IA'
                        },
                        {
                            'Days': 90,
                            'StorageClass': 'GLACIER'
                        }
                    ]
                }
            ]
        }
        
        try:
            self.s3_client.put_bucket_lifecycle_configuration(
                Bucket=bucket_name,
                LifecycleConfiguration=lifecycle_config
            )
            return True
        except ClientError as e:
            print(f"Error setting lifecycle policy: {e}")
            return False

# S3 storage cost comparison
s3_storage_costs = {
    '1TB_standard': {
        'monthly_cost': 23.00,
        'access': 'immediate'
    },
    '1TB_intelligent_tiering': {
        'monthly_cost': 12.50,
        'access': 'automatic',
        'savings': 45.7
    },
    '1TB_glacier': {
        'monthly_cost': 4.00,
        'access': 'hours_days',
        'savings': 82.6
    }
}

2. EBS Volume Optimization

EBS Volume Strategy

# EBS volume optimization
class EBSVolumeOptimizer:
    def __init__(self):
        self.volume_types = {
            'gp3': {'cost_per_gb': 0.08, 'iops': 3000, 'throughput': 125},
            'gp2': {'cost_per_gb': 0.10, 'iops': 'baseline', 'throughput': 'baseline'},
            'io1': {'cost_per_gb': 0.125, 'iops': 'provisioned', 'throughput': 'provisioned'},
            'st1': {'cost_per_gb': 0.045, 'iops': 'baseline', 'throughput': 500},
            'sc1': {'cost_per_gb': 0.015, 'iops': 'baseline', 'throughput': 250}
        }
    
    def select_optimal_volume_type(self, workload_type, size_gb, iops_required):
        """Select optimal EBS volume type"""
        if workload_type == 'general_purpose':
            return 'gp3'  # Best price-performance
        elif workload_type == 'high_iops':
            if iops_required > 16000:
                return 'io1'
            else:
                return 'gp3'
        elif workload_type == 'throughput_optimized':
            return 'st1'
        elif workload_type == 'cold_storage':
            return 'sc1'
        else:
            return 'gp3'
    
    def calculate_volume_costs(self, volume_type, size_gb, iops=None):
        """Calculate EBS volume costs"""
        base_cost = self.volume_types[volume_type]['cost_per_gb'] * size_gb
        
        if volume_type == 'io1' and iops:
            iops_cost = 0.065 * iops  # $0.065 per provisioned IOPS
            return base_cost + iops_cost
        
        return base_cost

# EBS volume cost comparison
ebs_volume_costs = {
    '100GB_gp2': {
        'monthly_cost': 10.00,
        'iops': 300,
        'throughput': 'baseline'
    },
    '100GB_gp3': {
        'monthly_cost': 8.00,
        'iops': 3000,
        'throughput': 125,
        'savings': 20.0
    },
    '100GB_st1': {
        'monthly_cost': 4.50,
        'iops': 'baseline',
        'throughput': 500,
        'savings': 55.0
    }
}

AI Services Cost Optimization

1. Managed AI Services Optimization

AI Services Cost Analysis

# AWS AI services cost optimization
class AWServicesCostOptimizer:
    def __init__(self):
        self.ai_services = {
            'rekognition': {
                'image_analysis': 0.0010,  # per image
                'video_analysis': 0.10,    # per minute
                'text_detection': 0.0015   # per image
            },
            'comprehend': {
                'text_analysis': 0.0001,   # per unit
                'entity_recognition': 0.0001,
                'sentiment_analysis': 0.0001
            },
            'translate': {
                'character': 0.000015,     # per character
                'batch': 0.000012          # per character (batch)
            },
            'personalize': {
                'training': 0.24,          # per hour
                'inference': 0.0001        # per prediction
            }
        }
    
    def optimize_rekognition_usage(self, image_count, video_minutes):
        """Optimize Rekognition usage costs"""
        # Batch processing for images
        batch_size = 1000
        batches = (image_count + batch_size - 1) // batch_size
        
        image_cost = batches * 0.0010 * batch_size
        video_cost = video_minutes * 0.10
        
        return {
            'image_cost': image_cost,
            'video_cost': video_cost,
            'total_cost': image_cost + video_cost,
            'optimization_tips': [
                'Use batch processing for images',
                'Consider video preprocessing to reduce minutes',
                'Use appropriate analysis types'
            ]
        }
    
    def optimize_translate_usage(self, character_count, batch_eligible=True):
        """Optimize Translate service costs"""
        if batch_eligible and character_count > 10000:
            cost = character_count * 0.000012  # Batch pricing
            savings = character_count * (0.000015 - 0.000012)
        else:
            cost = character_count * 0.000015  # Standard pricing
            savings = 0
        
        return {
            'cost': cost,
            'savings': savings,
            'recommendation': 'Use batch processing for large volumes'
        }

# AI services cost comparison
ai_services_costs = {
    'rekognition_1000_images': {
        'standard': 1.00,
        'batch_optimized': 0.80,
        'savings': 20.0
    },
    'translate_1M_characters': {
        'standard': 15.00,
        'batch': 12.00,
        'savings': 20.0
    },
    'comprehend_100k_units': {
        'standard': 10.00,
        'batch': 8.00,
        'savings': 20.0
    }
}

2. API Call Optimization

API Call Cost Optimization

# API call optimization strategies
class APICallOptimizer:
    def __init__(self):
        self.caching_strategies = {
            'redis': {'cost_per_hour': 0.017, 'latency': 'ms'},
            'dynamodb': {'cost_per_request': 0.00025, 'latency': 'ms'},
            's3': {'cost_per_request': 0.0004, 'latency': 'ms'}
        }
    
    def implement_caching(self, api_calls_per_month, cache_hit_rate=0.8):
        """Implement caching to reduce API calls"""
        cached_calls = api_calls_per_month * cache_hit_rate
        api_calls_reduced = api_calls_per_month - cached_calls
        
        # Example: Rekognition API calls
        cost_savings = api_calls_reduced * 0.0010  # $0.001 per call
        
        return {
            'original_calls': api_calls_per_month,
            'cached_calls': cached_calls,
            'api_calls_reduced': api_calls_reduced,
            'cost_savings': cost_savings,
            'cache_cost': self.calculate_cache_cost(cached_calls)
        }
    
    def calculate_cache_cost(self, cached_requests):
        """Calculate cache storage and request costs"""
        # Using Redis for caching
        storage_cost = 0.017 * 730  # $0.017/hour * 730 hours/month
        request_cost = cached_requests * 0.0001  # Estimated cache request cost
        
        return storage_cost + request_cost
    
    def batch_api_calls(self, individual_calls, batch_size=100):
        """Batch API calls to reduce costs"""
        batches = (individual_calls + batch_size - 1) // batch_size
        
        # Batch processing often has volume discounts
        individual_cost = individual_calls * 0.0010
        batch_cost = batches * batch_size * 0.0008  # 20% discount
        
        return {
            'individual_cost': individual_cost,
            'batch_cost': batch_cost,
            'savings': individual_cost - batch_cost,
            'savings_percentage': ((individual_cost - batch_cost) / individual_cost) * 100
        }

# API optimization example
api_optimization_example = {
    '100k_rekognition_calls': {
        'without_caching': 100.00,
        'with_caching': 20.00,
        'savings': 80.0
    },
    '1M_translate_calls': {
        'individual': 15.00,
        'batched': 12.00,
        'savings': 20.0
    }
}

Monitoring and Cost Tracking

1. AWS Cost Monitoring

Cost Monitoring Implementation

# AWS cost monitoring and alerting
import boto3
from datetime import datetime, timedelta

class AWSCostMonitor:
    def __init__(self):
        self.ce_client = boto3.client('ce')
        self.cloudwatch = boto3.client('cloudwatch')
    
    def get_current_month_cost(self):
        """Get current month's AWS costs"""
        end_date = datetime.now()
        start_date = end_date.replace(day=1)
        
        response = self.ce_client.get_cost_and_usage(
            TimePeriod={
                'Start': start_date.strftime('%Y-%m-%d'),
                'End': end_date.strftime('%Y-%m-%d')
            },
            Granularity='MONTHLY',
            Metrics=['UnblendedCost'],
            GroupBy=[
                {'Type': 'DIMENSION', 'Key': 'SERVICE'},
                {'Type': 'DIMENSION', 'Key': 'USAGE_TYPE'}
            ]
        )
        
        return response['ResultsByTime'][0]['Groups']
    
    def analyze_ai_costs(self, cost_data):
        """Analyze AI-specific costs"""
        ai_services = ['Amazon SageMaker', 'Amazon Rekognition', 'Amazon Comprehend', 'Amazon Translate']
        ai_costs = {}
        
        for group in cost_data:
            service = group['Keys'][0]
            if service in ai_services:
                cost = float(group['Metrics']['UnblendedCost']['Amount'])
                ai_costs[service] = cost
        
        return ai_costs
    
    def set_cost_alerts(self, threshold_amount):
        """Set up cost alerts"""
        alarm_name = 'AI-Cost-Alert'
        
        self.cloudwatch.put_metric_alarm(
            AlarmName=alarm_name,
            ComparisonOperator='GreaterThanThreshold',
            EvaluationPeriods=1,
            MetricName='EstimatedCharges',
            Namespace='AWS/Billing',
            Period=86400,  # 24 hours
            Statistic='Maximum',
            Threshold=threshold_amount,
            ActionsEnabled=True,
            AlarmDescription='Alert when AI costs exceed threshold'
        )
    
    def get_cost_recommendations(self, cost_data):
        """Generate cost optimization recommendations"""
        recommendations = []
        
        # Check for underutilized resources
        if self.detect_underutilized_instances():
            recommendations.append({
                'type': 'right_size',
                'description': 'Consider downsizing underutilized instances',
                'potential_savings': '20-40%'
            })
        
        # Check for spot instance opportunities
        if self.detect_spot_opportunities():
            recommendations.append({
                'type': 'spot_instances',
                'description': 'Use spot instances for non-critical workloads',
                'potential_savings': '50-90%'
            })
        
        # Check for storage optimization
        if self.detect_storage_optimization():
            recommendations.append({
                'type': 'storage_tiering',
                'description': 'Move infrequently accessed data to cheaper storage',
                'potential_savings': '30-80%'
            })
        
        return recommendations

# Cost monitoring dashboard metrics
cost_monitoring_metrics = {
    'daily_spend': 0,
    'monthly_spend': 0,
    'ai_services_spend': 0,
    'cost_trend': 'increasing',
    'budget_utilization': 0,
    'top_cost_drivers': []
}

2. Cost Optimization Dashboard

Dashboard Implementation

# Cost optimization dashboard
class CostOptimizationDashboard:
    def __init__(self):
        self.metrics = {
            'total_cost': 0,
            'ai_services_cost': 0,
            'compute_cost': 0,
            'storage_cost': 0,
            'network_cost': 0,
            'savings_achieved': 0,
            'savings_potential': 0
        }
    
    def update_metrics(self, cost_data):
        """Update dashboard metrics"""
        self.metrics['total_cost'] = cost_data.get('total', 0)
        self.metrics['ai_services_cost'] = cost_data.get('ai_services', 0)
        self.metrics['compute_cost'] = cost_data.get('compute', 0)
        self.metrics['storage_cost'] = cost_data.get('storage', 0)
        self.metrics['network_cost'] = cost_data.get('network', 0)
    
    def calculate_savings_potential(self):
        """Calculate potential savings from optimization"""
        potential_savings = {
            'spot_instances': self.metrics['compute_cost'] * 0.6,  # 60% savings
            'reserved_instances': self.metrics['compute_cost'] * 0.3,  # 30% savings
            'storage_optimization': self.metrics['storage_cost'] * 0.5,  # 50% savings
            'api_optimization': self.metrics['ai_services_cost'] * 0.2  # 20% savings
        }
        
        self.metrics['savings_potential'] = sum(potential_savings.values())
        return potential_savings
    
    def generate_optimization_report(self):
        """Generate comprehensive optimization report"""
        report = {
            'current_costs': self.metrics,
            'savings_potential': self.calculate_savings_potential(),
            'recommendations': [
                'Implement spot instances for non-critical workloads',
                'Purchase reserved instances for steady-state workloads',
                'Optimize storage classes based on access patterns',
                'Implement caching for frequently used AI services',
                'Use batch processing for large-scale operations'
            ],
            'implementation_priority': [
                'High: Storage optimization (quick wins)',
                'High: Spot instances (significant savings)',
                'Medium: Reserved instances (long-term planning)',
                'Medium: API optimization (ongoing improvement)'
            ]
        }
        
        return report

# Dashboard example
dashboard_example = {
    'current_monthly_cost': 5000,
    'ai_services_cost': 1500,
    'compute_cost': 2500,
    'storage_cost': 800,
    'network_cost': 200,
    'potential_savings': 2250,
    'savings_percentage': 45
}

Best Practices Summary

AWS AI Cost Optimization Principles

1. Right-Size Resources: Match instance types to workload requirements
2. Use Spot Instances: Leverage spot instances for non-critical workloads
3. Implement Reserved Instances: Plan for steady-state workloads
4. Optimize Storage: Use appropriate storage classes and lifecycle policies
5. Monitor and Alert: Set up cost monitoring and alerts
6. Batch Operations: Group operations to reduce API call costs
7. Implement Caching: Cache frequently accessed data and API responses

Implementation Checklist

• Analyze current AWS AI costs
• Implement spot instance strategy
• Purchase reserved instances for steady workloads
• Optimize S3 storage classes
• Implement EBS volume optimization
• Set up SageMaker cost optimization
• Optimize AI services usage
• Implement caching strategies
• Set up cost monitoring and alerts
• Regular cost optimization reviews

Conclusion

AWS AI cost optimization requires a comprehensive approach that addresses compute, storage, and AI services costs. By implementing these strategies, organizations can achieve significant cost savings while maintaining or improving performance.

The key is to start with quick wins like storage optimization and spot instances, then move to more strategic optimizations like reserved instances and comprehensive monitoring. Regular cost reviews and optimization adjustments ensure continued cost efficiency as workloads evolve.

Remember that the most expensive AWS resource is the one that’s not being used effectively. Focus on utilization optimization first, then work on cost reduction through more efficient resource types and configurations.