class CloudCostAnalyzer:
    def __init__(self):
        self.cost_categories = {
            'compute': {
                'components': ['instances', 'containers', 'serverless'],
                'typical_percentage': '60-70%',
                'optimization_potential': 'High'
            },
            'storage': {
                'components': ['object', 'block', 'file', 'archive'],
                'typical_percentage': '15-25%',
                'optimization_potential': 'Medium'
            },
            'network': {
                'components': ['data_transfer', 'load_balancers', 'vpn'],
                'typical_percentage': '10-15%',
                'optimization_potential': 'Medium'
            },
            'database': {
                'components': ['managed_db', 'data_warehouse', 'cache'],
                'typical_percentage': '10-20%',
                'optimization_potential': 'High'
            },
            'other': {
                'components': ['monitoring', 'security', 'support'],
                'typical_percentage': '5-10%',
                'optimization_potential': 'Low'
            }
        }

    def identify_cost_optimization_opportunities(self, spending_data):
        """Identify top cost optimization opportunities"""

        opportunities = []

        # Analyze spending patterns
        for category, details in self.cost_categories.items():
            if spending_data[category] > details['typical_percentage']:
                opportunities.append({
                    'category': category,
                    'current_spending': spending_data[category],
                    'expected_range': details['typical_percentage'],
                    'optimization_potential': details['optimization_potential'],
                    'recommended_actions': self.get_optimization_actions(category)
                })

        return sorted(opportunities, 
                     key=lambda x: x['optimization_potential'], 
                     reverse=True)

cost_optimization_pillars:
  1_right_sizing:
    description: "Match resources to actual workload needs"
    strategies:
      - analyze_utilization_metrics
      - identify_idle_resources
      - downsize_overprovisioned_resources
      - implement_auto_scaling

  2_pricing_model_optimization:
    description: "Choose the most cost-effective pricing model"
    strategies:
      - reserved_instances_for_steady_workloads
      - spot_instances_for_fault_tolerant_workloads
      - savings_plans_for_flexibility
      - on_demand_only_for_unpredictable_workloads

  3_resource_lifecycle_management:
    description: "Automate resource provisioning and deprovisioning"
    strategies:
      - implement_tagging_strategy
      - automate_shutdown_schedules
      - use_ephemeral_resources
      - implement_data_lifecycle_policies

  4_continuous_monitoring:
    description: "Track and analyze costs continuously"
    strategies:
      - set_up_cost_alerts
      - implement_showback_chargeback
      - regular_cost_reviews
      - anomaly_detection

  5_culture_and_governance:
    description: "Build cost-conscious culture"
    strategies:
      - establish_finops_team
      - implement_cost_accountability
      - regular_training
      - celebrate_wins

class CostVisibilityFramework:
    def __init__(self):
        self.tagging_strategy = TaggingStrategy()
        self.cost_allocation = CostAllocation()

    def implement_tagging_strategy(self):
        """Implement comprehensive tagging strategy"""

        tagging_schema = {
            'mandatory_tags': {
                'Environment': ['Production', 'Staging', 'Development', 'Test'],
                'CostCenter': 'REGEX:[0-9]{6}',
                'Owner': 'EMAIL',
                'Project': 'STRING',
                'Application': 'STRING',
                'CreatedDate': 'DATE',
                'Purpose': 'STRING'
            },

            'optional_tags': {
                'DataClassification': ['Public', 'Internal', 'Confidential', 'Restricted'],
                'Compliance': ['HIPAA', 'PCI', 'SOC2', 'None'],
                'AutoShutdown': ['Yes', 'No'],
                'EndDate': 'DATE'
            },

            'enforcement': {
                'method': 'preventive',
                'tools': ['Cloud Policies', 'CI/CD Integration'],
                'exceptions': 'approval_required'
            }
        }

        return tagging_schema

    def create_cost_dashboards(self):
        """Create comprehensive cost dashboards"""

        dashboards = {
            'executive_dashboard': {
                'metrics': [
                    'total_monthly_spend',
                    'spend_vs_budget',
                    'cost_trend',
                    'top_5_cost_drivers'
                ],
                'refresh': 'daily',
                'audience': 'C-level'
            },

            'departmental_dashboard': {
                'metrics': [
                    'department_spend',
                    'project_breakdown',
                    'resource_utilization',
                    'cost_per_unit'
                ],
                'refresh': 'hourly',
                'audience': 'Department heads'
            },

            'engineering_dashboard': {
                'metrics': [
                    'resource_efficiency',
                    'idle_resources',
                    'optimization_opportunities',
                    'anomaly_alerts'
                ],
                'refresh': 'real-time',
                'audience': 'DevOps teams'
            }
        }

        return dashboards

-- Top spending resources
WITH resource_costs AS (
    SELECT 
        resource_id,
        resource_type,
        tags,
        SUM(cost) as total_cost,
        AVG(cost) as avg_daily_cost
    FROM cloud_billing_data
    WHERE date >= DATEADD(day, -30, GETDATE())
    GROUP BY resource_id, resource_type, tags
)
SELECT TOP 20
    resource_id,
    resource_type,
    tags->>'Environment' as environment,
    tags->>'Owner' as owner,
    total_cost,
    avg_daily_cost,
    total_cost / SUM(total_cost) OVER () * 100 as percentage_of_total
FROM resource_costs
ORDER BY total_cost DESC;

-- Cost trends by service
SELECT 
    service_name,
    DATE_TRUNC('day', usage_date) as date,
    SUM(cost) as daily_cost,
    AVG(SUM(cost)) OVER (
        PARTITION BY service_name 
        ORDER BY DATE_TRUNC('day', usage_date) 
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) as seven_day_avg
FROM cloud_billing_data
WHERE usage_date >= DATEADD(day, -90, GETDATE())
GROUP BY service_name, DATE_TRUNC('day', usage_date)
ORDER BY service_name, date;

class ComputeOptimizer:
    def __init__(self):
        self.metrics_analyzer = MetricsAnalyzer()
        self.recommendation_engine = RecommendationEngine()

    def analyze_instance_utilization(self, instance_id, period_days=14):
        """Analyze instance utilization and provide recommendations"""

        metrics = self.metrics_analyzer.get_metrics(
            instance_id=instance_id,
            metrics=['cpu', 'memory', 'network', 'disk'],
            period_days=period_days
        )

        analysis = {
            'instance_id': instance_id,
            'current_type': metrics['instance_type'],
            'utilization': {
                'cpu': {
                    'avg': metrics['cpu_avg'],
                    'p95': metrics['cpu_p95'],
                    'max': metrics['cpu_max']
                },
                'memory': {
                    'avg': metrics['memory_avg'],
                    'p95': metrics['memory_p95'],
                    'max': metrics['memory_max']
                }
            }
        }

        # Right-sizing logic
        if metrics['cpu_p95'] < 20 and metrics['memory_p95'] < 40:
            analysis['recommendation'] = 'Downsize'
            analysis['recommended_type'] = self.get_smaller_instance_type(
                metrics['instance_type']
            )
            analysis['potential_savings'] = self.calculate_savings(
                metrics['instance_type'],
                analysis['recommended_type']
            )
        elif metrics['cpu_p95'] > 80 or metrics['memory_p95'] > 85:
            analysis['recommendation'] = 'Upsize'
            analysis['recommended_type'] = self.get_larger_instance_type(
                metrics['instance_type']
            )
        else:
            analysis['recommendation'] = 'Optimal'
            analysis['recommended_type'] = metrics['instance_type']

        return analysis

    def implement_auto_scaling(self):
        """Implement auto-scaling configuration"""

        auto_scaling_config = {
            'scaling_policies': [
                {
                    'name': 'scale-out-high-cpu',
                    'metric': 'CPUUtilization',
                    'threshold': 70,
                    'comparison': 'GreaterThanThreshold',
                    'scaling_adjustment': 2,
                    'cooldown': 300
                },
                {
                    'name': 'scale-in-low-cpu',
                    'metric': 'CPUUtilization',
                    'threshold': 30,
                    'comparison': 'LessThanThreshold',
                    'scaling_adjustment': -1,
                    'cooldown': 300
                }
            ],

            'predictive_scaling': {
                'enabled': True,
                'metric_type': 'ASGAverageCPUUtilization',
                'target_value': 50,
                'mode': 'ForecastAndScale'
            },

            'schedule_based_scaling': [
                {
                    'name': 'business-hours',
                    'schedule': '0 8 * * MON-FRI',
                    'min_size': 4,
                    'max_size': 20,
                    'desired_capacity': 8
                },
                {
                    'name': 'after-hours',
                    'schedule': '0 18 * * MON-FRI',
                    'min_size': 2,
                    'max_size': 10,
                    'desired_capacity': 2
                }
            ]
        }

        return auto_scaling_config

spot_instance_strategy:
  suitable_workloads:
    - batch_processing
    - big_data_analytics
    - ci_cd_pipelines
    - stateless_web_applications
    - containerized_microservices

  implementation:
    diversification:
      instance_types: ["m5.large", "m5a.large", "m4.large", "c5.large"]
      availability_zones: ["us-east-1a", "us-east-1b", "us-east-1c"]

    interruption_handling:
      notice_handler:
        enabled: true
        grace_period: "120 seconds"
        actions:
          - drain_connections
          - checkpoint_state
          - graceful_shutdown

      replacement_strategy:
        method: "capacity_optimized"
        fallback: "on_demand"

    cost_tracking:
      savings_calculation: true
      compare_to_on_demand: true
      monthly_reports: true

class StorageOptimizer:
    def __init__(self):
        self.storage_analyzer = StorageAnalyzer()

    def implement_storage_lifecycle(self):
        """Implement storage lifecycle management"""

        lifecycle_rules = {
            's3_lifecycle': [
                {
                    'name': 'transition-to-ia',
                    'prefix': 'logs/',
                    'transitions': [
                        {
                            'days': 30,
                            'storage_class': 'STANDARD_IA'
                        },
                        {
                            'days': 90,
                            'storage_class': 'GLACIER'
                        },
                        {
                            'days': 365,
                            'storage_class': 'DEEP_ARCHIVE'
                        }
                    ],
                    'expiration': {
                        'days': 2555  # 7 years
                    }
                },
                {
                    'name': 'delete-incomplete-uploads',
                    'abort_incomplete_multipart_upload': {
                        'days_after_initiation': 7
                    }
                }
            ],

            'intelligent_tiering': {
                'enabled': True,
                'access_tiers': [
                    {
                        'name': 'frequent_access',
                        'days': 0
                    },
                    {
                        'name': 'infrequent_access',
                        'days': 30
                    },
                    {
                        'name': 'archive_instant',
                        'days': 90
                    },
                    {
                        'name': 'archive_access',
                        'days': 180
                    }
                ]
            }
        }

        return lifecycle_rules

    def optimize_block_storage(self):
        """Optimize block storage usage"""

        optimization_strategies = {
            'volume_type_optimization': {
                'analysis_period': '30 days',
                'metrics': ['iops', 'throughput', 'latency'],
                'recommendations': self.analyze_volume_performance()
            },

            'snapshot_management': {
                'retention_policy': {
                    'daily': 7,
                    'weekly': 4,
                    'monthly': 12,
                    'yearly': 7
                },
                'automated_deletion': True,
                'cross_region_copies': 'critical_only'
            },

            'unused_volume_detection': {
                'criteria': {
                    'unattached_days': 7,
                    'zero_io_days': 30
                },
                'action': 'snapshot_and_delete',
                'notification': 'owner'
            }
        }

        return optimization_strategies

def implement_data_optimization():
    """Implement data compression and deduplication"""

    optimization_config = {
        'compression': {
            'file_types': {
                'logs': {
                    'algorithm': 'gzip',
                    'level': 9,
                    'expected_ratio': '10:1'
                },
                'databases': {
                    'algorithm': 'lz4',
                    'level': 'fast',
                    'expected_ratio': '3:1'
                },
                'archives': {
                    'algorithm': 'zstd',
                    'level': 19,
                    'expected_ratio': '20:1'
                }
            }
        },

        'deduplication': {
            'enabled': True,
            'block_size': '4KB',
            'algorithm': 'SHA-256',
            'scope': 'global',
            'expected_savings': '30-50%'
        },

        'intelligent_sync': {
            'enabled': True,
            'sync_only_changes': True,
            'compression_in_transit': True
        }
    }

    return optimization_config

network_optimization:
  data_transfer_optimization:
    strategies:
      - use_private_endpoints:
          description: "Avoid internet gateway charges"
          savings: "100% of egress charges"

      - implement_caching:
          cdn_usage: true
          edge_locations: "global"
          cache_hit_ratio_target: ">80%"

      - optimize_regions:
          principle: "process_data_where_stored"
          cross_region_transfer: "minimize"

      - compress_data:
          in_transit_compression: true
          algorithms: ["gzip", "brotli"]

  architectural_patterns:
    - name: "Hub-and-Spoke"
      benefits:
        - centralized_egress
        - reduced_nat_gateways
        - shared_services

    - name: "Service Mesh"
      benefits:
        - optimized_internal_routing
        - reduced_cross_az_traffic
        - intelligent_load_balancing

  monitoring:
    vpc_flow_logs:
      enabled: true
      analysis: "identify_top_talkers"

    cost_allocation:
      by_service: true
      by_az: true
      by_endpoint: true

class CDNOptimizer:
    def __init__(self):
        self.cdn_analyzer = CDNAnalyzer()

    def optimize_cdn_configuration(self):
        """Optimize CDN configuration for cost and performance"""

        cdn_config = {
            'origin_configuration': {
                'origin_shield': {
                    'enabled': True,
                    'region': 'us-east-1',
                    'expected_savings': '30-50% origin requests'
                },
                'connection_attempts': 3,
                'connection_timeout': 10,
                'keep_alive_timeout': 5
            },

            'caching_behavior': {
                'default': {
                    'ttl': {
                        'default': 86400,
                        'max': 31536000,
                        'min': 0
                    },
                    'compress': True,
                    'cache_policy': 'cache_everything'
                },
                'static_content': {
                    'path_pattern': '/static/*',
                    'ttl': {'default': 31536000},
                    'compress': True
                },
                'dynamic_content': {
                    'path_pattern': '/api/*',
                    'ttl': {'default': 0},
                    'cache_policy': 'cache_nothing',
                    'origin_request_policy': 'all_headers'
                }
            },

            'cost_optimization': {
                'price_class': 'PriceClass_100',  # Use only least expensive edges
                'unused_distribution_cleanup': True,
                'log_analysis': {
                    'enabled': True,
                    'identify_uncached_content': True,
                    'optimize_cache_keys': True
                }
            }
        }

        return cdn_config

class DatabaseOptimizer:
    def __init__(self):
        self.db_analyzer = DatabaseAnalyzer()

    def optimize_database_resources(self):
        """Optimize database resources for cost and performance"""

        optimization_strategies = {
            'instance_optimization': {
                'right_sizing': {
                    'metrics': ['cpu', 'memory', 'connections', 'iops'],
                    'analysis_period': '30 days',
                    'recommendation_threshold': {
                        'downsize_if_below': 40,
                        'upsize_if_above': 80
                    }
                },

                'reserved_instances': {
                    'analysis': self.analyze_db_usage_patterns(),
                    'recommendation': 'purchase_ri_for_steady_workloads',
                    'term': '1 year',
                    'payment': 'all_upfront'
                }
            },

            'storage_optimization': {
                'auto_scaling': {
                    'enabled': True,
                    'min_capacity': 100,  # GB
                    'max_capacity': 1000,  # GB
                    'target_utilization': 80
                },

                'backup_optimization': {
                    'retention_period': 7,  # days
                    'backup_window': '03:00-04:00',
                    'snapshot_management': {
                        'automated_cleanup': True,
                        'keep_final_snapshot': True
                    }
                }
            },

            'read_replica_optimization': {
                'auto_scaling_replicas': {
                    'enabled': True,
                    'min_replicas': 1,
                    'max_replicas': 5,
                    'target_cpu': 70
                },

                'cross_region_replicas': {
                    'evaluate_necessity': True,
                    'latency_threshold': 100  # ms
                }
            }
        }

        return optimization_strategies

    def implement_serverless_databases(self):
        """Implement serverless database configurations"""

        serverless_config = {
            'aurora_serverless': {
                'scaling_configuration': {
                    'min_capacity': 0.5,  # ACUs
                    'max_capacity': 16,   # ACUs
                    'auto_pause': True,
                    'seconds_until_auto_pause': 300
                },
                'benefits': {
                    'cost_savings': 'up to 90% for variable workloads',
                    'automatic_scaling': True,
                    'pay_per_second': True
                }
            },

            'dynamodb_on_demand': {
                'benefits': {
                    'no_capacity_planning': True,
                    'instant_scaling': True,
                    'pay_per_request': True
                },
                'cost_comparison': {
                    'break_even_point': '1.4M requests/month',
                    'use_when': 'unpredictable or spiky workloads'
                }
            }
        }

        return serverless_config

-- Identify expensive queries
WITH query_stats AS (
    SELECT 
        query_id,
        query_text,
        execution_count,
        total_time,
        mean_time,
        rows_processed,
        bytes_scanned,
        estimated_cost
    FROM query_performance_insights
    WHERE timestamp >= DATEADD(day, -7, GETDATE())
)
SELECT 
    query_id,
    LEFT(query_text, 100) as query_preview,
    execution_count,
    ROUND(mean_time, 2) as avg_time_ms,
    ROUND(total_time / 1000, 2) as total_time_sec,
    rows_processed,
    ROUND(bytes_scanned / 1024 / 1024 / 1024, 2) as gb_scanned,
    ROUND(estimated_cost * execution_count, 2) as total_cost,
    ROUND(estimated_cost, 4) as cost_per_query
FROM query_stats
ORDER BY total_cost DESC
LIMIT 20;

-- Recommend indexes for cost reduction
WITH missing_indexes AS (
    SELECT 
        table_name,
        suggested_index,
        estimated_improvement_percent,
        avg_user_impact,
        user_scans + user_seeks as total_accesses
    FROM sys.dm_db_missing_index_details
    WHERE estimated_improvement_percent > 20
)
SELECT 
    table_name,
    suggested_index,
    estimated_improvement_percent,
    CASE 
        WHEN estimated_improvement_percent > 80 THEN 'Critical'
        WHEN estimated_improvement_percent > 50 THEN 'High'
        WHEN estimated_improvement_percent > 30 THEN 'Medium'
        ELSE 'Low'
    END as priority,
    total_accesses
FROM missing_indexes
ORDER BY estimated_improvement_percent DESC;

class CostAutomation:
    def __init__(self):
        self.scheduler = Scheduler()
        self.policy_engine = PolicyEngine()

    def implement_automated_cost_controls(self):
        """Implement automated cost control mechanisms"""

        automation_config = {
            'scheduled_resources': {
                'development_environments': {
                    'schedule': {
                        'start': '08:00 weekdays',
                        'stop': '18:00 weekdays',
                        'timezone': 'America/New_York'
                    },
                    'resources': ['ec2', 'rds', 'eks'],
                    'exceptions': ['critical-dev-server'],
                    'estimated_savings': '70%'
                },

                'batch_processing': {
                    'schedule': {
                        'start': '22:00 daily',
                        'stop': '06:00 daily',
                        'timezone': 'UTC'
                    },
                    'auto_scaling': {
                        'min': 0,
                        'max': 100,
                        'target_completion_time': '8 hours'
                    }
                }
            },

            'automated_cleanup': {
                'unattached_volumes': {
                    'age_days': 7,
                    'action': 'snapshot_and_delete',
                    'notification': True
                },

                'old_snapshots': {
                    'retention_rules': {
                        'daily': 7,
                        'weekly': 4,
                        'monthly': 12
                    },
                    'action': 'delete',
                    'exclude_tags': ['keep-forever']
                },

                'unused_elastic_ips': {
                    'age_hours': 1,
                    'action': 'release',
                    'notification': True
                },

                'empty_s3_buckets': {
                    'age_days': 30,
                    'action': 'delete',
                    'require_approval': True
                }
            },

            'budget_enforcement': {
                'actions': [
                    {
                        'threshold': 80,
                        'action': 'notify',
                        'recipients': ['team-lead', 'finance']
                    },
                    {
                        'threshold': 90,
                        'action': 'restrict_new_resources',
                        'approval_required': True
                    },
                    {
                        'threshold': 100,
                        'action': 'stop_non_critical_resources',
                        'exclude_tags': ['production', 'critical']
                    }
                ]
            }
        }

        return automation_config

    def create_cost_anomaly_detection(self):
        """Create cost anomaly detection rules"""

        anomaly_rules = {
            'detection_methods': [
                {
                    'name': 'statistical_baseline',
                    'method': 'standard_deviation',
                    'threshold': 2.5,
                    'lookback_period': '30 days'
                },
                {
                    'name': 'ml_based',
                    'algorithm': 'isolation_forest',
                    'features': ['service', 'region', 'tags', 'time_of_day'],
                    'training_period': '90 days'
                }
            ],

            'alert_rules': [
                {
                    'name': 'daily_spend_spike',
                    'condition': 'daily_cost > average_daily_cost * 1.5',
                    'severity': 'high',
                    'notification': 'immediate'
                },
                {
                    'name': 'new_expensive_resource',
                    'condition': 'new_resource_cost > $100/day',
                    'severity': 'medium',
                    'notification': '1 hour'
                },
                {
                    'name': 'unusual_region_activity',
                    'condition': 'cost_in_new_region > $50',
                    'severity': 'high',
                    'notification': 'immediate'
                }
            ]
        }

        return anomaly_rules

def generate_cost_optimization_report():
    """Generate automated cost optimization report"""

    report_template = {
        'executive_summary': {
            'total_spend': 'calculate_monthly_spend()',
            'month_over_month_change': 'calculate_trend()',
            'budget_variance': 'compare_to_budget()',
            'optimization_opportunities': 'identify_savings()',
            'implemented_savings': 'track_implementations()'
        },

        'detailed_analysis': {
            'by_service': {
                'top_services': 'get_top_spending_services(10)',
                'growth_rate': 'calculate_service_growth()',
                'optimization_potential': 'estimate_service_savings()'
            },

            'by_team': {
                'spending_breakdown': 'allocate_costs_by_tags()',
                'efficiency_metrics': 'calculate_cost_per_unit()',
                'recommendations': 'generate_team_recommendations()'
            },

            'unused_resources': {
                'idle_instances': 'find_idle_resources()',
                'unattached_storage': 'find_orphaned_storage()',
                'over_provisioned': 'identify_oversized_resources()'
            }
        },

        'recommendations': {
            'immediate_actions': [
                'terminate_idle_resources()',
                'rightsize_instances()',
                'purchase_savings_plans()'
            ],

            'short_term': [
                'implement_auto_scaling()',
                'optimize_storage_tiers()',
                'consolidate_accounts()'
            ],

            'long_term': [
                'modernize_architecture()',
                'implement_finops_practices()',
                'automate_cost_governance()'
            ]
        }
    }

    return report_template

finops_operating_model:
  organizational_structure:
    finops_team:
      roles:
        - name: "FinOps Manager"
          responsibilities:
            - strategy_development
            - stakeholder_management
            - process_improvement

        - name: "Cloud Financial Analyst"
          responsibilities:
            - cost_analysis
            - budget_forecasting
            - savings_identification

        - name: "Cloud Engineer"
          responsibilities:
            - technical_optimization
            - automation_implementation
            - architecture_review

        - name: "Business Analyst"
          responsibilities:
            - unit_economics
            - showback_chargeback
            - business_alignment

    stakeholders:
      - engineering_teams
      - finance_department
      - product_management
      - executive_leadership

  maturity_model:
    crawl:
      characteristics:
        - basic_cost_visibility
        - manual_optimization
        - reactive_approach
      duration: "3-6 months"

    walk:
      characteristics:
        - automated_reporting
        - proactive_optimization
        - team_accountability
      duration: "6-12 months"

    run:
      characteristics:
        - predictive_analytics
        - automated_optimization
        - business_value_focus
      duration: "ongoing"

class FinOpsMetrics:
    def __init__(self):
        self.metrics_collector = MetricsCollector()

    def calculate_unit_economics(self):
        """Calculate unit economics for cloud resources"""

        unit_metrics = {
            'cost_per_customer': {
                'formula': 'total_infrastructure_cost / active_customers',
                'target': '<$10',
                'current': self.calculate_cost_per_customer()
            },

            'cost_per_transaction': {
                'formula': 'transaction_processing_cost / total_transactions',
                'target': '<$0.01',
                'current': self.calculate_cost_per_transaction()
            },

            'revenue_per_compute_dollar': {
                'formula': 'monthly_revenue / monthly_compute_cost',
                'target': '>10x',
                'current': self.calculate_revenue_efficiency()
            },

            'infrastructure_margin': {
                'formula': '(revenue - infrastructure_cost) / revenue',
                'target': '>80%',
                'current': self.calculate_infrastructure_margin()
            }
        }

        return unit_metrics

    def track_optimization_metrics(self):
        """Track FinOps optimization metrics"""

        optimization_kpis = {
            'coverage_metrics': {
                'reserved_instance_coverage': {
                    'target': 80,
                    'current': self.calculate_ri_coverage(),
                    'trend': 'improving'
                },
                'savings_plan_coverage': {
                    'target': 70,
                    'current': self.calculate_sp_coverage(),
                    'trend': 'stable'
                },
                'spot_instance_usage': {
                    'target': 30,
                    'current': self.calculate_spot_usage(),
                    'trend': 'improving'
                }
            },

            'efficiency_metrics': {
                'resource_utilization': {
                    'compute': 75,
                    'storage': 80,
                    'database': 70
                },
                'waste_reduction': {
                    'monthly_savings': self.calculate_waste_reduction(),
                    'yoy_improvement': '25%'
                },
                'automation_rate': {
                    'automated_actions': 85,
                    'manual_interventions': 15
                }
            },

            'business_metrics': {
                'forecast_accuracy': {
                    'target': '±5%',
                    'current': self.calculate_forecast_accuracy()
                },
                'budget_adherence': {
                    'target': '95-105%',
                    'current': self.calculate_budget_adherence()
                },
                'time_to_optimization': {
                    'target': '<24 hours',
                    'current': self.calculate_optimization_time()
                }
            }
        }

        return optimization_kpis

def aws_specific_optimizations():
    """AWS-specific cost optimization strategies"""

    aws_optimizations = {
        'compute_savings': {
            'savings_plans': {
                'compute_savings_plan': {
                    'flexibility': 'any instance family, size, OS, region',
                    'discount': 'up to 66%',
                    'commitment': '1 or 3 years'
                },
                'ec2_instance_savings_plan': {
                    'flexibility': 'instance family within region',
                    'discount': 'up to 72%',
                    'commitment': '1 or 3 years'
                }
            },

            'spot_fleet': {
                'diversification': 'multiple instance types',
                'allocation_strategy': 'capacity-optimized',
                'price_protection': 'on-demand price cap'
            }
        },

        'storage_savings': {
            's3_intelligent_tiering': {
                'automatic_optimization': True,
                'no_retrieval_fees': True,
                'monitoring_fee': '$0.0025 per 1,000 objects'
            },

            'ebs_optimization': {
                'gp3_migration': 'save 20% over gp2',
                'snapshot_lifecycle': 'automated deletion',
                'unused_volume_cleanup': 'weekly scan'
            }
        },

        'data_transfer_savings': {
            'vpc_endpoints': 'eliminate NAT gateway costs',
            'cloudfront_origin_shield': 'reduce origin requests',
            'direct_connect': 'reduce data transfer costs'
        }
    }

    return aws_optimizations

azure_cost_optimization:
  compute_optimization:
    azure_hybrid_benefit:
      windows_server: "save up to 85%"
      sql_server: "save up to 55%"
      red_hat: "save up to 49%"
      suse: "save up to 49%"

    reserved_instances:
      vm_reserved_instances:
        term: ["1 year", "3 year"]
        payment: ["monthly", "upfront"]
        flexibility: "instance size within family"

      capacity_reservations:
        guaranteed_capacity: true
        combine_with_ri: true

    spot_vms:
      savings: "up to 90%"
      eviction_policy: ["deallocate", "delete"]
      max_price: ["variable", "fixed"]

  storage_optimization:
    blob_storage_tiers:
      - hot: "frequent access"
      - cool: "infrequent access (30+ days)"
      - archive: "rare access (180+ days)"

    lifecycle_management:
      automatic_tiering: true
      deletion_rules: true

    reserved_capacity:
      commitment: ["100TB", "1PB", "10PB"]
      term: ["1 year", "3 year"]
      savings: "up to 38%"

  paas_optimization:
    azure_sql_database:
      serverless: "auto-pause capability"
      elastic_pools: "share resources"
      reserved_capacity: "up to 80% savings"

    app_service:
      reserved_instances: "up to 55% savings"
      auto_scaling: true
      density_optimization: "multiple apps per plan"

def gcp_cost_optimization():
    """Google Cloud specific cost optimizations"""

    gcp_optimizations = {
        'committed_use_discounts': {
            'compute': {
                'discount': 'up to 57%',
                'commitment': '1 or 3 years',
                'flexibility': 'machine type changes allowed'
            },
            'memory_optimized': {
                'discount': 'up to 70%',
                'use_case': 'SAP, databases'
            }
        },

        'sustained_use_discounts': {
            'automatic': True,
            'no_commitment': True,
            'discount_tiers': {
                '25%_usage': '10% discount',
                '50%_usage': '20% discount',
                '75%_usage': '25% discount',
                '100%_usage': '30% discount'
            }
        },

        'preemptible_vms': {
            'savings': 'up to 91%',
            'max_duration': '24 hours',
            'use_cases': [
                'batch processing',
                'fault-tolerant workloads',
                'ci/cd pipelines'
            ]
        },

        'storage_optimization': {
            'autoclass': {
                'automatic_transitions': True,
                'no_early_deletion_fees': True,
                'no_retrieval_fees': True
            },
            'lifecycle_rules': {
                'delete_old_versions': True,
                'transition_to_nearline': '30 days',
                'transition_to_coldline': '90 days',
                'transition_to_archive': '365 days'
            }
        },

        'bigquery_optimization': {
            'slot_commitments': {
                'flex_slots': 'cancel anytime after 60 seconds',
                'monthly': '1 month commitment',
                'annual': 'up to 40% discount'
            },
            'query_optimization': {
                'partition_tables': 'reduce data scanned',
                'cluster_tables': 'improve performance',
                'materialized_views': 'pre-compute results'
            }
        }
    }

    return gcp_optimizations

emergency_cost_reduction:
  immediate_actions:  # Day 1
    - stop_all_development_environments:
        savings: "20-30%"
        impact: "development delays"

    - terminate_unused_resources:
        savings: "5-10%"
        impact: "minimal"

    - disable_non_critical_services:
        savings: "10-15%"
        impact: "feature limitations"

  short_term_actions:  # Week 1
    - downsize_overprovisioned_resources:
        savings: "15-25%"
        impact: "potential performance impact"

    - consolidate_accounts_and_services:
        savings: "5-10%"
        impact: "operational changes"

    - renegotiate_commitments:
        savings: "10-20%"
        impact: "lock-in period"

  medium_term_actions:  # Month 1
    - re_architect_expensive_components:
        savings: "20-40%"
        impact: "development effort"

    - implement_aggressive_auto_scaling:
        savings: "20-30%"
        impact: "cold start latency"

    - migrate_to_serverless:
        savings: "30-70%"
        impact: "architecture changes"

def continuous_optimization_cycle():
    """Implement continuous cost optimization cycle"""

    optimization_cycle = {
        'weekly_tasks': [
            {
                'task': 'review_unused_resources',
                'automation': 'scripted',
                'time_required': '1 hour',
                'potential_savings': '2-5%'
            },
            {
                'task': 'analyze_cost_anomalies',
                'automation': 'alerts',
                'time_required': '30 minutes',
                'potential_savings': '1-3%'
            }
        ],

        'monthly_tasks': [
            {
                'task': 'rightsize_analysis',
                'automation': 'recommendations',
                'time_required': '4 hours',
                'potential_savings': '10-20%'
            },
            {
                'task': 'commitment_planning',
                'automation': 'forecasting',
                'time_required': '2 hours',
                'potential_savings': '20-30%'
            },
            {
                'task': 'architectural_review',
                'automation': 'manual',
                'time_required': '8 hours',
                'potential_savings': '15-40%'
            }
        ],

        'quarterly_tasks': [
            {
                'task': 'contract_negotiations',
                'automation': 'manual',
                'time_required': '40 hours',
                'potential_savings': '10-25%'
            },
            {
                'task': 'technology_evaluation',
                'automation': 'manual',
                'time_required': '80 hours',
                'potential_savings': '20-50%'
            }
        ]
    }

    return optimization_cycle