Google Cloud AI Pricing Strategies

Google Cloud offers powerful AI services including TPUs, AI Platform, and specialized ML services. This guide covers strategies to optimize Google Cloud AI costs by 25-50% while leveraging Google’s AI expertise.

Understanding Google Cloud AI Cost Structure

Google Cloud AI Services Cost Breakdown

Google Cloud AI Cost Distribution:
├── Compute Services (50-65%)
│   ├── Compute Engine (GPU/CPU)
│   ├── AI Platform training/inference
│   ├── TPU instances
│   └── Cloud Run for ML
├── Storage Services (20-30%)
│   ├── Cloud Storage
│   ├── Persistent Disks
│   ├── Filestore
│   └── BigQuery ML
├── AI Services (15-25%)
│   ├── Vision AI
│   ├── Natural Language AI
│   ├── Translation AI
│   ├── Speech-to-Text
│   └── AutoML
└── Network & Data Transfer (5-10%)
    ├── Data transfer costs
    ├── API calls
    └── Cross-region traffic

Key Cost Drivers

• TPU vs GPU: TPUs offer specialized AI performance but different pricing
• Preemptible Instances: Significant cost savings for fault-tolerant workloads
• Committed Use Discounts: Long-term commitments for steady workloads
• AI Platform Pricing: Pay-per-use vs managed service costs
• Storage Classes: Different Cloud Storage tiers for cost optimization

Compute Engine Optimization

1. Instance Type Selection

GPU Instance Cost Analysis

# Google Cloud GPU instance cost analysis
gcp_gpu_instances = {
    'n1-standard-4': {
        'vCPUs': 4,
        'Memory': '15 GB',
        'hourly_cost': 0.19,
        'monthly_cost': 136.80,
        'best_for': ['Light ML workloads', 'Development']
    },
    'n1-standard-8': {
        'vCPUs': 8,
        'Memory': '30 GB',
        'hourly_cost': 0.38,
        'monthly_cost': 273.60,
        'best_for': ['Medium ML workloads', 'Training']
    },
    'n1-standard-16': {
        'vCPUs': 16,
        'Memory': '60 GB',
        'hourly_cost': 0.76,
        'monthly_cost': 547.20,
        'best_for': ['Large ML workloads', 'Distributed training']
    },
    'n1-highmem-8': {
        'vCPUs': 8,
        'Memory': '52 GB',
        'hourly_cost': 0.47,
        'monthly_cost': 338.40,
        'best_for': ['Memory-intensive ML', 'Large datasets']
    }
}

# GPU attachments
gcp_gpu_options = {
    'nvidia-tesla-k80': {
        'hourly_cost': 0.45,
        'memory': '12 GB',
        'best_for': ['General ML training', 'Cost-effective']
    },
    'nvidia-tesla-p100': {
        'hourly_cost': 1.46,
        'memory': '16 GB',
        'best_for': ['Deep learning', 'High performance']
    },
    'nvidia-tesla-v100': {
        'hourly_cost': 2.48,
        'memory': '16 GB',
        'best_for': ['Advanced ML', 'Research']
    },
    'nvidia-tesla-t4': {
        'hourly_cost': 0.35,
        'memory': '16 GB',
        'best_for': ['Inference', 'Cost-effective inference']
    }
}

def select_optimal_gcp_instance(workload_type, budget, performance_requirements):
    """Select optimal GCP instance based on requirements"""
    if workload_type == "training" and budget > 1000:
        return "n1-standard-16"  # High-performance training
    elif workload_type == "inference" and budget < 500:
        return "n1-standard-4"   # Cost-effective inference
    elif workload_type == "memory_intensive":
        return "n1-highmem-8"    # Memory-optimized
    else:
        return "n1-standard-8"   # Balanced option

def calculate_gpu_cost(instance_type, gpu_type, gpu_count=1):
    """Calculate total cost for GPU-enabled instance"""
    instance_cost = gcp_gpu_instances[instance_type]['monthly_cost']
    gpu_cost = gcp_gpu_options[gpu_type]['hourly_cost'] * 730 * gpu_count
    
    return {
        'instance_cost': instance_cost,
        'gpu_cost': gpu_cost,
        'total_cost': instance_cost + gpu_cost,
        'cost_per_gpu': gpu_cost / gpu_count
    }

2. Preemptible Instance Strategy

Preemptible Instance Implementation

# Preemptible instance cost optimization
from google.cloud import compute_v1
import time

class PreemptibleInstanceManager:
    def __init__(self, project_id, zone):
        self.project_id = project_id
        self.zone = zone
        self.compute_client = compute_v1.InstancesClient()
    
    def create_preemptible_instance(self, instance_name, machine_type, gpu_type=None):
        """Create preemptible instance for cost savings"""
        instance_config = {
            'name': instance_name,
            'machine_type': f'zones/{self.zone}/machineTypes/{machine_type}',
            'disks': [{
                'boot': True,
                'auto_delete': True,
                'initialize_params': {
                    'source_image': 'projects/deeplearning-platform-release/global/images/family/tf-latest-gpu'
                }
            }],
            'scheduling': {
                'preemptible': True,
                'automatic_restart': False,
                'on_host_maintenance': 'TERMINATE'
            }
        }
        
        if gpu_type:
            instance_config['guest_accelerators'] = [{
                'accelerator_count': 1,
                'accelerator_type': gpu_type
            }]
        
        return instance_config
    
    def calculate_preemptible_savings(self, instance_type, gpu_type=None):
        """Calculate savings from using preemptible instances"""
        # Preemptible instances are 60-91% cheaper than regular instances
        regular_cost = self.get_regular_instance_cost(instance_type, gpu_type)
        preemptible_cost = regular_cost * 0.3  # 70% savings
        
        return {
            'regular_cost': regular_cost,
            'preemptible_cost': preemptible_cost,
            'savings': regular_cost - preemptible_cost,
            'savings_percentage': 70
        }
    
    def implement_fault_tolerance(self, workload_config):
        """Implement fault tolerance for preemptible instances"""
        fault_tolerance_config = {
            'checkpointing': True,
            'checkpoint_interval': 300,  # 5 minutes
            'auto_restart': True,
            'backup_instances': 2,
            'data_persistence': 'cloud_storage'
        }
        
        return fault_tolerance_config

# Preemptible instance cost comparison
preemptible_savings_example = {
    'n1-standard-8_tesla-v100': {
        'regular_monthly': 2147.20,
        'preemptible_monthly': 644.16,
        'savings_percentage': 70,
        'monthly_savings': 1503.04
    },
    'n1-standard-4_tesla-t4': {
        'regular_monthly': 571.20,
        'preemptible_monthly': 171.36,
        'savings_percentage': 70,
        'monthly_savings': 399.84
    }
}

3. Committed Use Discounts

Committed Use Strategy

# Committed use discount optimization
class CommittedUseOptimizer:
    def __init__(self):
        self.commitment_types = {
            '1_year': {'discount': 0.30, 'commitment': '1 year'},
            '3_year': {'discount': 0.55, 'commitment': '3 years'},
            '1_year_cud': {'discount': 0.25, 'commitment': '1 year', 'flexible': True},
            '3_year_cud': {'discount': 0.50, 'commitment': '3 years', 'flexible': True}
        }
    
    def calculate_committed_savings(self, instance_type, usage_hours, commitment_type='1_year'):
        """Calculate savings from committed use discounts"""
        regular_cost = self.get_regular_instance_cost(instance_type, usage_hours)
        discount = self.commitment_types[commitment_type]['discount']
        committed_cost = regular_cost * (1 - discount)
        
        return {
            'regular_cost': regular_cost,
            'committed_cost': committed_cost,
            'savings': regular_cost - committed_cost,
            'savings_percentage': discount * 100,
            'commitment_period': self.commitment_types[commitment_type]['commitment']
        }
    
    def get_regular_instance_cost(self, instance_type, hours):
        """Get regular instance cost"""
        hourly_rates = {
            'n1-standard-4': 0.19,
            'n1-standard-8': 0.38,
            'n1-standard-16': 0.76,
            'n1-highmem-8': 0.47
        }
        return hourly_rates.get(instance_type, 0) * hours

# Committed use discount example
committed_use_example = {
    'n1-standard-8_1year': {
        'regular_monthly': 273.60,
        'committed_monthly': 191.52,
        'savings_percentage': 30,
        'annual_savings': 984.96
    },
    'n1-standard-16_3year': {
        'regular_monthly': 547.20,
        'committed_monthly': 246.24,
        'savings_percentage': 55,
        'annual_savings': 3611.52
    }
}

TPU Optimization

1. TPU vs GPU Cost Analysis

TPU Cost Comparison

# TPU vs GPU cost analysis
class TPUvsGPUCostAnalyzer:
    def __init__(self):
        self.tpu_pricing = {
            'v2-8': {
                'hourly_cost': 4.50,
                'monthly_cost': 3240.00,
                'performance': 'high',
                'best_for': ['Training', 'Large models']
            },
            'v3-8': {
                'hourly_cost': 8.00,
                'monthly_cost': 5760.00,
                'performance': 'very_high',
                'best_for': ['Advanced training', 'Research']
            },
            'v2-32': {
                'hourly_cost': 18.00,
                'monthly_cost': 12960.00,
                'performance': 'very_high',
                'best_for': ['Distributed training', 'Large scale']
            },
            'v3-32': {
                'hourly_cost': 32.00,
                'monthly_cost': 23040.00,
                'performance': 'extreme',
                'best_for': ['Massive scale', 'Research']
            }
        }
        
        self.gpu_pricing = {
            'tesla-v100': {
                'hourly_cost': 2.48,
                'monthly_cost': 1785.60,
                'performance': 'high',
                'best_for': ['General ML', 'Flexible workloads']
            },
            'tesla-p100': {
                'hourly_cost': 1.46,
                'monthly_cost': 1051.20,
                'performance': 'medium',
                'best_for': ['Cost-effective', 'Medium workloads']
            }
        }
    
    def compare_tpu_gpu_costs(self, workload_type, model_size, training_time_hours):
        """Compare TPU vs GPU costs for specific workload"""
        if workload_type == "training" and model_size == "large":
            # TPU is often more cost-effective for large models
            tpu_cost = self.tpu_pricing['v2-8']['hourly_cost'] * training_time_hours
            gpu_cost = self.gpu_pricing['tesla-v100']['hourly_cost'] * training_time_hours * 4  # 4 GPUs needed
            
            return {
                'tpu_cost': tpu_cost,
                'gpu_cost': gpu_cost,
                'recommendation': 'TPU' if tpu_cost < gpu_cost else 'GPU',
                'savings': abs(tpu_cost - gpu_cost),
                'savings_percentage': ((max(tpu_cost, gpu_cost) - min(tpu_cost, gpu_cost)) / max(tpu_cost, gpu_cost)) * 100
            }
        else:
            # GPU is often more cost-effective for smaller models
            return {
                'tpu_cost': self.tpu_pricing['v2-8']['hourly_cost'] * training_time_hours,
                'gpu_cost': self.gpu_pricing['tesla-p100']['hourly_cost'] * training_time_hours,
                'recommendation': 'GPU',
                'reason': 'GPU more cost-effective for smaller workloads'
            }
    
    def select_optimal_tpu(self, model_size, training_time, budget):
        """Select optimal TPU configuration"""
        if model_size == "small" and budget < 2000:
            return None  # Use GPU instead
        elif model_size == "medium" and budget < 5000:
            return "v2-8"
        elif model_size == "large" and budget < 15000:
            return "v3-8"
        else:
            return "v2-32"

# TPU vs GPU cost comparison
tpu_gpu_comparison = {
    'large_model_training': {
        'tpu_v2-8_cost': 3240.00,
        'gpu_4x_v100_cost': 7142.40,
        'tpu_savings': 3902.40,
        'savings_percentage': 54.6
    },
    'medium_model_training': {
        'tpu_v2-8_cost': 3240.00,
        'gpu_2x_p100_cost': 2102.40,
        'gpu_savings': 1137.60,
        'savings_percentage': 35.1
    }
}

2. TPU Preemptible Instances

TPU Preemptible Strategy

# TPU preemptible instance optimization
class TPUPreemptibleOptimizer:
    def __init__(self):
        self.preemptible_tpu_pricing = {
            'v2-8': {
                'hourly_cost': 1.35,  # 70% discount
                'monthly_cost': 972.00,
                'availability': 'variable'
            },
            'v3-8': {
                'hourly_cost': 2.40,  # 70% discount
                'monthly_cost': 1728.00,
                'availability': 'variable'
            }
        }
    
    def calculate_preemptible_tpu_savings(self, tpu_type):
        """Calculate savings from preemptible TPUs"""
        regular_cost = self.get_regular_tpu_cost(tpu_type)
        preemptible_cost = self.preemptible_tpu_pricing[tpu_type]['monthly_cost']
        
        return {
            'regular_cost': regular_cost,
            'preemptible_cost': preemptible_cost,
            'savings': regular_cost - preemptible_cost,
            'savings_percentage': ((regular_cost - preemptible_cost) / regular_cost) * 100
        }
    
    def get_regular_tpu_cost(self, tpu_type):
        """Get regular TPU cost"""
        regular_pricing = {
            'v2-8': 3240.00,
            'v3-8': 5760.00
        }
        return regular_pricing.get(tpu_type, 0)
    
    def implement_tpu_fault_tolerance(self):
        """Implement fault tolerance for preemptible TPUs"""
        return {
            'checkpointing': True,
            'checkpoint_interval': 600,  # 10 minutes
            'model_saving': 'cloud_storage',
            'auto_restart': True,
            'backup_strategy': 'multiple_regions'
        }

# TPU preemptible savings
tpu_preemptible_savings = {
    'v2-8': {
        'regular_monthly': 3240.00,
        'preemptible_monthly': 972.00,
        'savings_percentage': 70,
        'monthly_savings': 2268.00
    },
    'v3-8': {
        'regular_monthly': 5760.00,
        'preemptible_monthly': 1728.00,
        'savings_percentage': 70,
        'monthly_savings': 4032.00
    }
}

AI Platform Optimization

1. AI Platform Training Optimization

AI Platform Cost Analysis

# AI Platform training cost optimization
from google.cloud import aiplatform

class AIPlatformCostOptimizer:
    def __init__(self):
        self.ai_platform_pricing = {
            'training': {
                'ml_training': {
                    'basic_tier': 0.49,  # per hour
                    'scale_tier': 'BASIC'
                },
                'ml_training_advanced': {
                    'standard_tier': 0.49,  # per hour
                    'scale_tier': 'STANDARD_1'
                },
                'ml_training_custom': {
                    'custom_tier': 'custom_pricing',
                    'scale_tier': 'CUSTOM'
                }
            },
            'prediction': {
                'ml_prediction': {
                    'per_prediction': 0.0001,
                    'per_node_hour': 0.54
                }
            }
        }
    
    def optimize_training_config(self, training_config):
        """Optimize AI Platform training configuration"""
        optimized_config = {
            'scale_tier': self.select_optimal_scale_tier(training_config),
            'master_type': self.select_optimal_master_type(training_config),
            'worker_type': self.select_optimal_worker_type(training_config),
            'worker_count': self.calculate_optimal_workers(training_config),
            'use_preemptible': True,
            'max_running_time': self.estimate_training_time(training_config)
        }
        return optimized_config
    
    def select_optimal_scale_tier(self, config):
        """Select optimal scale tier"""
        data_size = config.get('data_size_gb', 0)
        model_complexity = config.get('model_complexity', 'medium')
        
        if data_size < 1 and model_complexity == 'simple':
            return 'BASIC'
        elif data_size < 10:
            return 'STANDARD_1'
        else:
            return 'CUSTOM'
    
    def calculate_training_costs(self, scale_tier, training_hours, worker_count=0):
        """Calculate AI Platform training costs"""
        if scale_tier == 'BASIC':
            return 0.49 * training_hours
        elif scale_tier == 'STANDARD_1':
            return 0.49 * training_hours
        else:
            # Custom pricing based on instance types
            master_cost = 0.49 * training_hours
            worker_cost = 0.49 * training_hours * worker_count
            return master_cost + worker_cost

# AI Platform cost comparison
ai_platform_costs = {
    'basic_training_8h': {
        'cost': 3.92,
        'scale_tier': 'BASIC',
        'best_for': ['Small models', 'Prototyping']
    },
    'standard_training_8h': {
        'cost': 3.92,
        'scale_tier': 'STANDARD_1',
        'best_for': ['Medium models', 'Production training']
    },
    'custom_training_8h_4workers': {
        'cost': 19.60,
        'scale_tier': 'CUSTOM',
        'best_for': ['Large models', 'Distributed training']
    }
}

2. AI Platform Prediction Optimization

Prediction Cost Optimization

# AI Platform prediction cost optimization
class AIPlatformPredictionOptimizer:
    def __init__(self):
        self.prediction_pricing = {
            'per_prediction': 0.0001,
            'per_node_hour': 0.54,
            'min_nodes': 1,
            'max_nodes': 10
        }
    
    def optimize_prediction_config(self, traffic_pattern, latency_requirements):
        """Optimize prediction configuration"""
        if traffic_pattern == 'spiky' and latency_requirements == 'low':
            return {
                'min_nodes': 2,
                'max_nodes': 10,
                'auto_scaling': True,
                'prediction_type': 'online'
            }
        elif traffic_pattern == 'steady':
            return {
                'min_nodes': 1,
                'max_nodes': 3,
                'auto_scaling': False,
                'prediction_type': 'batch'
            }
        else:
            return {
                'min_nodes': 1,
                'max_nodes': 5,
                'auto_scaling': True,
                'prediction_type': 'online'
            }
    
    def calculate_prediction_costs(self, requests_per_month, avg_nodes=1):
        """Calculate prediction costs"""
        prediction_cost = requests_per_month * self.prediction_pricing['per_prediction']
        node_cost = avg_nodes * self.prediction_pricing['per_node_hour'] * 730  # hours per month
        
        return {
            'prediction_cost': prediction_cost,
            'node_cost': node_cost,
            'total_cost': prediction_cost + node_cost,
            'cost_per_request': (prediction_cost + node_cost) / requests_per_month
        }
    
    def compare_with_managed_services(self, requests_per_month):
        """Compare with other managed ML services"""
        ai_platform_cost = self.calculate_prediction_costs(requests_per_month)
        
        # Compare with Cloud Run for ML
        cloud_run_cost = requests_per_month * 0.0000004  # $0.0000004 per request
        
        return {
            'ai_platform': ai_platform_cost,
            'cloud_run': cloud_run_cost,
            'recommendation': 'Cloud Run' if cloud_run_cost < ai_platform_cost['total_cost'] else 'AI Platform'
        }

# Prediction cost comparison
prediction_cost_comparison = {
    '1M_requests_ai_platform': {
        'prediction_cost': 100.00,
        'node_cost': 394.20,
        'total_cost': 494.20,
        'cost_per_request': 0.00049
    },
    '1M_requests_cloud_run': {
        'total_cost': 0.40,
        'cost_per_request': 0.0000004,
        'savings': 493.80,
        'savings_percentage': 99.9
    }
}

Storage Optimization

1. Cloud Storage Optimization

Cloud Storage Cost Analysis

# Cloud Storage cost optimization
from google.cloud import storage

class CloudStorageOptimizer:
    def __init__(self):
        self.storage_classes = {
            'standard': {
                'cost_per_gb': 0.020,
                'access': 'immediate',
                'use_case': 'Frequently accessed data'
            },
            'nearline': {
                'cost_per_gb': 0.010,
                'access': 'hours',
                'use_case': 'Accessed less than once per month'
            },
            'coldline': {
                'cost_per_gb': 0.004,
                'access': 'hours',
                'use_case': 'Accessed less than once per quarter'
            },
            'archive': {
                'cost_per_gb': 0.0012,
                'access': 'days',
                'use_case': 'Accessed less than once per year'
            }
        }
    
    def optimize_storage_class(self, access_pattern, data_size_gb):
        """Select optimal storage class"""
        if access_pattern['frequency'] == 'daily':
            return 'standard'
        elif access_pattern['frequency'] == 'monthly':
            return 'nearline'
        elif access_pattern['frequency'] == 'quarterly':
            return 'coldline'
        else:
            return 'archive'
    
    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):
        """Setup Cloud Storage lifecycle policy"""
        lifecycle_config = {
            'rule': [
                {
                    'action': {
                        'type': 'SetStorageClass',
                        'storageClass': 'NEARLINE'
                    },
                    'condition': {
                        'age': 30,
                        'matchesStorageClass': ['STANDARD']
                    }
                },
                {
                    'action': {
                        'type': 'SetStorageClass',
                        'storageClass': 'COLDLINE'
                    },
                    'condition': {
                        'age': 90,
                        'matchesStorageClass': ['NEARLINE']
                    }
                },
                {
                    'action': {
                        'type': 'SetStorageClass',
                        'storageClass': 'ARCHIVE'
                    },
                    'condition': {
                        'age': 365,
                        'matchesStorageClass': ['COLDLINE']
                    }
                }
            ]
        }
        
        return lifecycle_config

# Cloud Storage cost comparison
cloud_storage_costs = {
    '1TB_standard': {
        'monthly_cost': 20.00,
        'access': 'immediate'
    },
    '1TB_nearline': {
        'monthly_cost': 10.00,
        'access': 'hours',
        'savings': 50.0
    },
    '1TB_coldline': {
        'monthly_cost': 4.00,
        'access': 'hours',
        'savings': 80.0
    },
    '1TB_archive': {
        'monthly_cost': 1.20,
        'access': 'days',
        'savings': 94.0
    }
}

2. BigQuery ML Optimization

BigQuery ML Cost Analysis

# BigQuery ML cost optimization
from google.cloud import bigquery

class BigQueryMLOptimizer:
    def __init__(self):
        self.bigquery_pricing = {
            'analysis': 0.0000005,  # per byte processed
            'ml_training': 0.0000005,  # per byte processed
            'ml_prediction': 0.0000005,  # per byte processed
            'storage': 0.020,  # per GB per month
            'long_term_storage': 0.010  # per GB per month (after 90 days)
        }
    
    def calculate_ml_training_cost(self, data_size_gb, model_complexity):
        """Calculate BigQuery ML training costs"""
        # BigQuery ML charges for data processed, not compute time
        bytes_processed = data_size_gb * (1024**3)  # Convert to bytes
        
        # Model complexity affects processing multiplier
        complexity_multipliers = {
            'simple': 1.0,
            'medium': 2.0,
            'complex': 5.0
        }
        
        multiplier = complexity_multipliers.get(model_complexity, 1.0)
        total_bytes = bytes_processed * multiplier
        
        training_cost = total_bytes * self.bigquery_pricing['ml_training']
        
        return {
            'data_size_gb': data_size_gb,
            'bytes_processed': total_bytes,
            'training_cost': training_cost,
            'cost_per_gb': training_cost / data_size_gb
        }
    
    def compare_with_ai_platform(self, data_size_gb, training_hours):
        """Compare BigQuery ML with AI Platform costs"""
        bigquery_cost = self.calculate_ml_training_cost(data_size_gb, 'medium')
        ai_platform_cost = 0.49 * training_hours  # AI Platform hourly rate
        
        return {
            'bigquery_ml': bigquery_cost,
            'ai_platform': ai_platform_cost,
            'recommendation': 'BigQuery ML' if bigquery_cost['training_cost'] < ai_platform_cost else 'AI Platform',
            'savings': abs(bigquery_cost['training_cost'] - ai_platform_cost)
        }

# BigQuery ML cost comparison
bigquery_ml_costs = {
    '100GB_training': {
        'bigquery_cost': 0.05,
        'ai_platform_cost': 3.92,  # 8 hours
        'savings': 98.7,
        'savings_percentage': 98.7
    },
    '1TB_training': {
        'bigquery_cost': 0.50,
        'ai_platform_cost': 39.20,  # 80 hours
        'savings': 98.7,
        'savings_percentage': 98.7
    }
}

AI Services Optimization

1. Vision AI Optimization

Vision AI Cost Analysis

# Vision AI cost optimization
class VisionAIOptimizer:
    def __init__(self):
        self.vision_ai_pricing = {
            'label_detection': 0.0015,  # per image
            'text_detection': 0.0015,   # per image
            'face_detection': 0.0015,   # per image
            'landmark_detection': 0.0015,  # per image
            'logo_detection': 0.0015,   # per image
            'object_localization': 0.0015,  # per image
            'web_detection': 0.0015,    # per image
            'crop_hints': 0.0015,       # per image
            'image_properties': 0.0015,  # per image
            'safe_search_detection': 0.0015,  # per image
            'document_text_detection': 0.0015,  # per image
            'batch_annotation': 0.0015  # per image (batch)
        }
    
    def optimize_vision_usage(self, image_count, analysis_types):
        """Optimize Vision AI usage costs"""
        # Batch processing for multiple images
        batch_size = 1000
        batches = (image_count + batch_size - 1) // batch_size
        
        # Calculate cost per analysis type
        total_cost = 0
        for analysis_type in analysis_types:
            cost_per_image = self.vision_ai_pricing.get(analysis_type, 0.0015)
            total_cost += image_count * cost_per_image
        
        # Batch processing discount (if applicable)
        if batches > 1:
            total_cost *= 0.9  # 10% discount for batch processing
        
        return {
            'image_count': image_count,
            'analysis_types': analysis_types,
            'total_cost': total_cost,
            'cost_per_image': total_cost / image_count,
            'optimization_tips': [
                'Use batch processing for multiple images',
                'Combine multiple analysis types in single request',
                'Cache results for repeated images'
            ]
        }
    
    def implement_caching_strategy(self, image_hash, analysis_results):
        """Implement caching for Vision AI results"""
        cache_config = {
            'storage': 'cloud_storage',
            'ttl': 86400,  # 24 hours
            'key_format': 'vision_ai_{hash}_{analysis_type}',
            'compression': True
        }
        
        return cache_config

# Vision AI cost comparison
vision_ai_costs = {
    '1000_images_single_analysis': {
        'cost': 1.50,
        'cost_per_image': 0.0015
    },
    '1000_images_batch_analysis': {
        'cost': 1.35,
        'cost_per_image': 0.00135,
        'savings': 10.0
    },
    '1000_images_cached_results': {
        'cost': 0.15,  # 90% cache hit rate
        'cost_per_image': 0.00015,
        'savings': 90.0
    }
}

2. Natural Language AI Optimization

Natural Language AI Cost Analysis

# Natural Language AI cost optimization
class NaturalLanguageAIOptimizer:
    def __init__(self):
        self.nlp_pricing = {
            'analyze_sentiment': 0.0001,  # per unit
            'analyze_entities': 0.0001,   # per unit
            'analyze_syntax': 0.0001,     # per unit
            'analyze_entity_sentiment': 0.0001,  # per unit
            'classify_text': 0.0001,      # per unit
            'annotate_text': 0.0001       # per unit (includes all features)
        }
    
    def optimize_nlp_usage(self, text_volume, analysis_types):
        """Optimize Natural Language AI usage"""
        # Use annotate_text for multiple analysis types
        if len(analysis_types) > 2:
            # annotate_text includes all features for same price
            cost_per_unit = self.nlp_pricing['annotate_text']
            recommendation = 'Use annotate_text for multiple analyses'
        else:
            # Calculate cost for individual analyses
            cost_per_unit = sum(self.nlp_pricing[analysis_type] for analysis_type in analysis_types)
            recommendation = 'Use individual analysis methods'
        
        total_cost = text_volume * cost_per_unit
        
        return {
            'text_volume': text_volume,
            'analysis_types': analysis_types,
            'total_cost': total_cost,
            'cost_per_unit': cost_per_unit,
            'recommendation': recommendation,
            'optimization_tips': [
                'Use annotate_text for multiple analyses',
                'Batch process large text volumes',
                'Cache results for repeated content'
            ]
        }

# Natural Language AI cost comparison
nlp_costs = {
    '100k_units_individual': {
        'cost': 10.00,
        'analysis_types': ['sentiment', 'entities', 'syntax']
    },
    '100k_units_annotate': {
        'cost': 10.00,
        'analysis_types': 'all',
        'savings': 0.0  # Same cost, more features
    },
    '100k_units_cached': {
        'cost': 1.00,  # 90% cache hit rate
        'savings': 90.0
    }
}

Monitoring and Cost Tracking

1. Google Cloud Cost Monitoring

Cost Monitoring Implementation

# Google Cloud cost monitoring
from google.cloud import billing_v1
from google.cloud import monitoring_v3

class GoogleCloudCostMonitor:
    def __init__(self, project_id):
        self.project_id = project_id
        self.billing_client = billing_v1.CloudBillingClient()
        self.monitoring_client = monitoring_v3.MetricServiceClient()
    
    def get_current_month_cost(self):
        """Get current month's Google Cloud costs"""
        # Implementation would use Cloud Billing API
        # This is a simplified example
        return {
            'total_cost': 0,
            'ai_services_cost': 0,
            'compute_cost': 0,
            'storage_cost': 0,
            'network_cost': 0
        }
    
    def analyze_ai_costs(self, cost_data):
        """Analyze AI-specific costs"""
        ai_services = [
            'AI Platform',
            'Vision AI',
            'Natural Language AI',
            'Translation AI',
            'Speech-to-Text',
            'AutoML'
        ]
        
        ai_costs = {}
        for service in ai_services:
            if service in cost_data:
                ai_costs[service] = cost_data[service]
        
        return ai_costs
    
    def set_cost_alerts(self, threshold_amount):
        """Set up cost alerts using Cloud Monitoring"""
        alert_policy = {
            'display_name': 'AI Cost Alert',
            'conditions': [{
                'display_name': 'Cost threshold exceeded',
                'condition_threshold': {
                    'filter': 'metric.type="billing.googleapis.com/billing/amount"',
                    'comparison': 'COMPARISON_GREATER_THAN',
                    'threshold_value': threshold_amount
                }
            }]
        }
        
        return alert_policy
    
    def get_cost_recommendations(self, cost_data):
        """Generate cost optimization recommendations"""
        recommendations = []
        
        # Check for preemptible opportunities
        if cost_data.get('compute_cost', 0) > 1000:
            recommendations.append({
                'type': 'preemptible_instances',
                'description': 'Use preemptible instances for fault-tolerant workloads',
                'potential_savings': '60-91%'
            })
        
        # Check for committed use opportunities
        if cost_data.get('compute_cost', 0) > 500:
            recommendations.append({
                'type': 'committed_use',
                'description': 'Purchase committed use discounts for steady workloads',
                'potential_savings': '30-55%'
            })
        
        # Check for storage optimization
        if cost_data.get('storage_cost', 0) > 100:
            recommendations.append({
                'type': 'storage_optimization',
                'description': 'Optimize storage classes based on access patterns',
                'potential_savings': '50-94%'
            })
        
        return recommendations

# Cost monitoring dashboard
cost_monitoring_dashboard = {
    'current_monthly_cost': 0,
    'ai_services_cost': 0,
    'compute_cost': 0,
    'storage_cost': 0,
    'network_cost': 0,
    'cost_trend': 'stable',
    'budget_utilization': 0,
    'top_cost_drivers': []
}

2. Cost Optimization Dashboard

Dashboard Implementation

# Cost optimization dashboard
class GoogleCloudCostDashboard:
    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.update(cost_data)
    
    def calculate_savings_potential(self):
        """Calculate potential savings from optimization"""
        potential_savings = {
            'preemptible_instances': self.metrics['compute_cost'] * 0.7,  # 70% savings
            'committed_use': self.metrics['compute_cost'] * 0.4,  # 40% savings
            'storage_optimization': self.metrics['storage_cost'] * 0.6,  # 60% savings
            'ai_services_optimization': self.metrics['ai_services_cost'] * 0.3  # 30% 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': [
                'Use preemptible instances for fault-tolerant workloads',
                'Purchase committed use discounts for steady workloads',
                'Optimize storage classes based on access patterns',
                'Use TPUs for large-scale training workloads',
                'Implement caching for AI services',
                'Use BigQuery ML for simple ML workloads'
            ],
            'implementation_priority': [
                'High: Preemptible instances (quick wins)',
                'High: Storage optimization (significant savings)',
                'Medium: Committed use discounts (long-term planning)',
                'Medium: TPU optimization (performance improvement)'
            ]
        }
        
        return report

# Dashboard example
dashboard_example = {
    'current_monthly_cost': 4000,
    'ai_services_cost': 1200,
    'compute_cost': 2000,
    'storage_cost': 600,
    'network_cost': 200,
    'potential_savings': 1800,
    'savings_percentage': 45
}

Best Practices Summary

Google Cloud AI Cost Optimization Principles

1. Use Preemptible Instances: Leverage preemptible instances for fault-tolerant workloads
2. Purchase Committed Use Discounts: Plan for steady-state workloads
3. Optimize Storage Classes: Use appropriate Cloud Storage tiers
4. Choose TPUs Wisely: Use TPUs for large-scale training, GPUs for flexibility
5. Monitor and Alert: Set up cost monitoring and alerts
6. Use BigQuery ML: Leverage BigQuery ML for simple ML workloads
7. Implement Caching: Cache AI service results to reduce API calls

Implementation Checklist

• Analyze current Google Cloud AI costs
• Implement preemptible instance strategy
• Purchase committed use discounts for steady workloads
• Optimize Cloud Storage classes
• Evaluate TPU vs GPU usage
• Set up AI Platform cost optimization
• Optimize AI services usage
• Implement caching strategies
• Set up cost monitoring and alerts
• Regular cost optimization reviews

Conclusion

Google Cloud AI cost optimization requires understanding the unique pricing models of TPUs, preemptible instances, and committed use discounts. By implementing these strategies, organizations can achieve significant cost savings while leveraging Google’s AI expertise.

The key is to start with preemptible instances for quick wins, then move to strategic optimizations like committed use discounts and TPU utilization. Regular cost reviews and optimization adjustments ensure continued cost efficiency as workloads evolve.

Remember that Google Cloud’s AI services are designed to work together efficiently. Focus on using the right tool for the job: TPUs for large-scale training, BigQuery ML for simple workloads, and preemptible instances for cost-sensitive operations.