Cost Optimization

interface MemoryCostBreakdown {
  storage: {
    vector_storage: {
      cost_per_gb_month: number;
      compression_ratio: number;
      access_patterns: 'hot' | 'warm' | 'cold' | 'archive';
    };
    metadata_storage: {
      transactional_database: number;
      search_indices: number;
      backup_storage: number;
    };
  };
 
  compute: {
    vector_operations: {
      similarity_search: number; // cost per 1M operations
      indexing: number; // cost per 1M vectors
      embedding_generation: number; // cost per 1M tokens
    };
    application_runtime: {
      cpu_hours: number;
      memory_gb_hours: number;
      gpu_hours: number; // for ML workloads
    };
  };
 
  network: {
    data_transfer: {
      inter_region: number; // per GB
      cdn_usage: number; // per GB
      api_calls: number; // per million calls
    };
  };
 
  operational: {
    monitoring_observability: number;
    backup_disaster_recovery: number;
    security_compliance: number;
    development_operations: number; // personnel costs
  };
}

class CostAttributionSystem:
    def __init__(self):
        self.cost_trackers = {
            'storage': StorageCostTracker(),
            'compute': ComputeCostTracker(),
            'network': NetworkCostTracker(),
            'operations': OperationalCostTracker()
        }
 
    def track_memory_operation(self, operation: MemoryOperation):
        """Track costs for individual memory operations"""
 
        cost_breakdown = CostBreakdown()
 
        # Storage costs
        if operation.type in ['store', 'update']:
            storage_cost = self.calculate_storage_cost(
                data_size=operation.data_size,
                storage_tier=operation.storage_tier,
                replication_factor=operation.replication_factor
            )
            cost_breakdown.add_storage_cost(storage_cost)
 
        # Compute costs
        if operation.type in ['search', 'similarity']:
            compute_cost = self.calculate_compute_cost(
                vector_operations=operation.vector_ops,
                cpu_time=operation.cpu_time,
                memory_usage=operation.memory_usage
            )
            cost_breakdown.add_compute_cost(compute_cost)
 
        # Network costs
        if operation.involves_network_transfer:
            network_cost = self.calculate_network_cost(
                bytes_transferred=operation.bytes_transferred,
                regions=operation.regions,
                cdn_usage=operation.uses_cdn
            )
            cost_breakdown.add_network_cost(network_cost)
 
        # Attribute costs to tenant/user
        self.attribute_costs(
            tenant_id=operation.tenant_id,
            user_id=operation.user_id,
            cost_breakdown=cost_breakdown
        )
 
        return cost_breakdown
 
    def generate_cost_report(self, time_period: TimePeriod,
                           granularity: str = 'tenant') -> CostReport:
        """Generate detailed cost reports with optimization recommendations"""
 
        costs_by_dimension = {}
 
        for dimension in ['tenant', 'user', 'feature', 'region']:
            if granularity == 'all' or granularity == dimension:
                costs_by_dimension[dimension] = self.aggregate_costs_by_dimension(
                    time_period, dimension
                )
 
        # Generate optimization recommendations
        recommendations = self.generate_optimization_recommendations(
            costs_by_dimension, time_period
        )
 
        return CostReport(
            time_period=time_period,
            total_cost=self.calculate_total_cost(costs_by_dimension),
            cost_breakdown=costs_by_dimension,
            recommendations=recommendations,
            trends=self.analyze_cost_trends(time_period)
        )

class IntelligentDataTiering:
    def __init__(self):
        self.tiers = {
            'hot': TierConfig(cost_per_gb=0.25, access_time_ms=1),
            'warm': TierConfig(cost_per_gb=0.10, access_time_ms=10),
            'cold': TierConfig(cost_per_gb=0.04, access_time_ms=100),
            'archive': TierConfig(cost_per_gb=0.01, access_time_ms=1000)
        }
        self.ml_predictor = AccessPatternPredictor()
 
    def optimize_data_placement(self, memory_data: MemoryData) -> TieringDecision:
        """Determine optimal storage tier for memory data"""
 
        # Analyze access patterns
        access_pattern = self.analyze_access_pattern(memory_data.access_history)
 
        # Predict future access probability
        future_access_probability = self.ml_predictor.predict_access(
            memory_data.features,
            time_horizon=30  # 30 days
        )
 
        # Calculate cost-benefit for each tier
        tier_costs = {}
        for tier_name, tier_config in self.tiers.items():
            cost = self.calculate_tier_cost(
                data_size=memory_data.size,
                tier_config=tier_config,
                access_pattern=access_pattern,
                future_access_prob=future_access_probability
            )
            tier_costs[tier_name] = cost
 
        # Select optimal tier
        optimal_tier = min(tier_costs.items(), key=lambda x: x[1])
 
        return TieringDecision(
            recommended_tier=optimal_tier[0],
            cost_savings=tier_costs['hot'] - optimal_tier[1],
            confidence_score=self.calculate_confidence(memory_data),
            migration_cost=self.estimate_migration_cost(memory_data, optimal_tier[0])
        )
 
    def calculate_tier_cost(self, data_size: int, tier_config: TierConfig,
                           access_pattern: AccessPattern,
                           future_access_prob: float) -> float:
        """Calculate total cost of ownership for storing data in a tier"""
 
        # Storage cost
        monthly_storage_cost = data_size * tier_config.cost_per_gb / (1024**3)
 
        # Access cost (retrieval + latency penalties)
        expected_accesses = access_pattern.monthly_accesses * future_access_prob
        access_cost = expected_accesses * tier_config.retrieval_cost
 
        # Performance penalty cost (business impact of slower access)
        if tier_config.access_time_ms > access_pattern.sla_requirement_ms:
            penalty_cost = self.calculate_performance_penalty(
                tier_config.access_time_ms - access_pattern.sla_requirement_ms,
                expected_accesses
            )
        else:
            penalty_cost = 0
 
        return monthly_storage_cost + access_cost + penalty_cost

class CompressionOptimizer:
    def __init__(self):
        self.algorithms = {
            'gzip': {'ratio': 0.7, 'cpu_cost': 0.1},
            'lz4': {'ratio': 0.8, 'cpu_cost': 0.05},
            'zstd': {'ratio': 0.65, 'cpu_cost': 0.08},
            'brotli': {'ratio': 0.6, 'cpu_cost': 0.15}
        }
 
    def optimize_compression(self, memory_dataset: MemoryDataset) -> CompressionStrategy:
        """Determine optimal compression strategy based on cost-benefit analysis"""
 
        # Analyze data characteristics
        data_analysis = self.analyze_data_characteristics(memory_dataset)
 
        optimal_strategies = {}
        for data_type, data_subset in memory_dataset.group_by_type():
 
            # Calculate compression benefits for each algorithm
            algorithm_scores = {}
            for algo_name, algo_config in self.algorithms.items():
 
                # Estimate compression ratio for this data type
                estimated_ratio = self.estimate_compression_ratio(
                    data_subset, algo_name, data_analysis[data_type]
                )
 
                # Calculate storage savings
                storage_savings = (
                    data_subset.size * (1 - estimated_ratio) *
                    self.storage_cost_per_gb
                )
 
                # Calculate CPU cost increase
                cpu_cost_increase = (
                    data_subset.access_frequency * algo_config['cpu_cost'] *
                    self.cpu_cost_per_hour
                )
 
                # Net benefit
                net_benefit = storage_savings - cpu_cost_increase
 
                algorithm_scores[algo_name] = {
                    'net_benefit': net_benefit,
                    'storage_savings': storage_savings,
                    'cpu_overhead': cpu_cost_increase,
                    'compression_ratio': estimated_ratio
                }
 
            # Select best algorithm for this data type
            best_algo = max(algorithm_scores.items(),
                          key=lambda x: x[1]['net_benefit'])
 
            optimal_strategies[data_type] = CompressionConfig(
                algorithm=best_algo[0],
                **best_algo[1]
            )
 
        return CompressionStrategy(strategies=optimal_strategies)
 
    def implement_deduplication(self, memory_store: MemoryStore) -> DeduplicationResult:
        """Implement content-aware deduplication to reduce storage costs"""
 
        # Content-based deduplication
        content_hashes = {}
        duplicate_content = []
 
        for memory_id, memory_item in memory_store.items():
            content_hash = self.calculate_content_hash(memory_item.content)
 
            if content_hash in content_hashes:
                duplicate_content.append({
                    'original_id': content_hashes[content_hash],
                    'duplicate_id': memory_id,
                    'size_saved': memory_item.size
                })
            else:
                content_hashes[content_hash] = memory_id
 
        # Semantic deduplication (using embeddings)
        semantic_duplicates = self.find_semantic_duplicates(
            memory_store, similarity_threshold=0.98
        )
 
        total_size_saved = (
            sum(item['size_saved'] for item in duplicate_content) +
            sum(item['size_saved'] for item in semantic_duplicates)
        )
 
        return DeduplicationResult(
            content_duplicates=duplicate_content,
            semantic_duplicates=semantic_duplicates,
            total_size_saved=total_size_saved,
            cost_savings=total_size_saved * self.storage_cost_per_gb
        )

class QueryOptimizer:
    def __init__(self):
        self.query_cache = QueryCache()
        self.execution_planner = ExecutionPlanner()
        self.cost_model = QueryCostModel()
 
    def optimize_similarity_search(self, query: SimilarityQuery) -> OptimizedQuery:
        """Optimize similarity search queries for cost and performance"""
 
        # Check if query can be satisfied from cache
        cached_result = self.query_cache.get(query.cache_key)
        if cached_result and not cached_result.is_expired():
            return OptimizedQuery(
                execution_plan=CacheRetrievalPlan(),
                estimated_cost=0.001,  # minimal cache access cost
                cached=True
            )
 
        # Generate multiple execution plans
        execution_plans = [
            self.generate_exact_search_plan(query),
            self.generate_approximate_search_plan(query),
            self.generate_hybrid_search_plan(query)
        ]
 
        # Cost each execution plan
        plan_costs = []
        for plan in execution_plans:
            cost = self.cost_model.estimate_execution_cost(plan)
            plan_costs.append((plan, cost))
 
        # Select optimal plan based on cost-quality trade-off
        optimal_plan = self.select_optimal_plan(plan_costs, query.quality_requirements)
 
        return OptimizedQuery(
            execution_plan=optimal_plan[0],
            estimated_cost=optimal_plan[1],
            cached=False
        )
 
    def implement_progressive_search(self, query: SimilarityQuery) -> ProgressiveSearchResult:
        """Implement progressive search to minimize unnecessary compute costs"""
 
        results = []
        total_cost = 0
 
        # Start with cheapest, fastest approximation
        fast_results = self.execute_fast_search(query)
        results.extend(fast_results.top_k(query.initial_k))
        total_cost += fast_results.cost
 
        # Check if results meet quality threshold
        if self.meets_quality_threshold(results, query.quality_threshold):
            return ProgressiveSearchResult(
                results=results,
                total_cost=total_cost,
                search_stages_used=1
            )
 
        # Refine with more expensive but accurate search
        refined_results = self.execute_precise_search(query, exclude=results)
        results.extend(refined_results.top_k(query.target_k - len(results)))
        total_cost += refined_results.cost
 
        return ProgressiveSearchResult(
            results=results,
            total_cost=total_cost,
            search_stages_used=2
        )

class ResourceRightSizing:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.capacity_planner = CapacityPlanner()
 
    def analyze_resource_utilization(self, time_period: TimePeriod) -> UtilizationAnalysis:
        """Analyze resource utilization patterns for right-sizing opportunities"""
 
        # Collect resource metrics
        cpu_metrics = self.metrics_collector.get_cpu_utilization(time_period)
        memory_metrics = self.metrics_collector.get_memory_utilization(time_period)
        storage_metrics = self.metrics_collector.get_storage_utilization(time_period)
        network_metrics = self.metrics_collector.get_network_utilization(time_period)
 
        # Analyze utilization patterns
        analysis = UtilizationAnalysis()
 
        # CPU analysis
        cpu_p95 = np.percentile(cpu_metrics, 95)
        cpu_average = np.mean(cpu_metrics)
 
        if cpu_p95 < 60:  # Under-utilized
            analysis.cpu_recommendation = ResourceRecommendation(
                action='downsize',
                current_capacity=self.get_current_cpu_capacity(),
                recommended_capacity=self.calculate_optimal_cpu_capacity(cpu_metrics),
                potential_savings=self.calculate_cpu_savings(cpu_metrics)
            )
        elif cpu_p95 > 85:  # Over-utilized
            analysis.cpu_recommendation = ResourceRecommendation(
                action='upsize',
                reason='performance_risk',
                recommended_capacity=self.calculate_safe_cpu_capacity(cpu_metrics)
            )
 
        # Memory analysis
        memory_p95 = np.percentile(memory_metrics, 95)
        if memory_p95 < 70:
            analysis.memory_recommendation = self.generate_memory_recommendation(memory_metrics)
 
        # Storage analysis
        storage_efficiency = self.analyze_storage_efficiency(storage_metrics)
        analysis.storage_recommendation = self.generate_storage_recommendation(storage_efficiency)
 
        return analysis
 
    def implement_auto_scaling(self, scaling_config: AutoScalingConfig) -> AutoScalingSetup:
        """Implement intelligent auto-scaling to optimize costs"""
 
        scaling_policies = []
 
        # CPU-based scaling
        cpu_policy = ScalingPolicy(
            metric='cpu_utilization',
            scale_up_threshold=scaling_config.cpu_scale_up_threshold,
            scale_down_threshold=scaling_config.cpu_scale_down_threshold,
            scale_up_cooldown=scaling_config.scale_up_cooldown,
            scale_down_cooldown=scaling_config.scale_down_cooldown,
            max_instances=scaling_config.max_instances,
            min_instances=scaling_config.min_instances
        )
        scaling_policies.append(cpu_policy)
 
        # Queue depth-based scaling for batch workloads
        queue_policy = ScalingPolicy(
            metric='queue_depth',
            scale_up_threshold=100,  # scale up if queue > 100 items
            scale_down_threshold=10,  # scale down if queue < 10 items
            evaluation_periods=2,  # wait for 2 evaluation periods
            scaling_adjustment=2  # scale by 2 instances
        )
        scaling_policies.append(queue_policy)
 
        # Predictive scaling based on historical patterns
        predictive_policy = PredictiveScalingPolicy(
            model=self.train_demand_prediction_model(),
            forecast_horizon=3600,  # 1 hour
            confidence_threshold=0.8
        )
        scaling_policies.append(predictive_policy)
 
        return AutoScalingSetup(policies=scaling_policies)

class NetworkCostOptimizer:
    def __init__(self):
        self.cdn_providers = ['cloudflare', 'aws_cloudfront', 'azure_cdn']
        self.region_costs = RegionCostMapping()
 
    def optimize_cdn_strategy(self, traffic_patterns: TrafficPatterns) -> CDNStrategy:
        """Optimize CDN usage to minimize network transfer costs"""
 
        # Analyze traffic patterns by region
        regional_analysis = {}
        for region, traffic in traffic_patterns.by_region.items():
            regional_analysis[region] = {
                'volume_gb': traffic.volume_gb,
                'cache_hit_ratio': traffic.cache_hit_ratio,
                'latency_requirements': traffic.latency_requirements,
                'cost_sensitivity': traffic.cost_sensitivity
            }
 
        # Optimize CDN configuration
        cdn_strategy = CDNStrategy()
 
        for region, analysis in regional_analysis.items():
            if analysis['volume_gb'] > 1000:  # High volume regions
                if analysis['latency_requirements'] == 'strict':
                    # Use premium CDN tier
                    cdn_strategy.add_region_config(region, {
                        'tier': 'premium',
                        'cache_ttl': self.calculate_optimal_ttl(analysis),
                        'compression': True,
                        'image_optimization': True
                    })
                else:
                    # Use standard CDN tier
                    cdn_strategy.add_region_config(region, {
                        'tier': 'standard',
                        'cache_ttl': analysis['volume_gb'] * 0.1,  # Dynamic TTL
                        'compression': True
                    })
            else:
                # Low volume regions - direct origin serving might be cheaper
                direct_cost = self.calculate_direct_serving_cost(region, analysis)
                cdn_cost = self.calculate_cdn_cost(region, analysis)
 
                if direct_cost < cdn_cost:
                    cdn_strategy.add_region_config(region, {
                        'strategy': 'direct_origin'
                    })
 
        return cdn_strategy
 
    def implement_intelligent_prefetching(self, access_patterns: AccessPatterns) -> PrefetchingStrategy:
        """Implement cost-aware prefetching to reduce latency while controlling costs"""
 
        prefetch_candidates = []
 
        # Analyze access patterns to identify prefetch opportunities
        for user_id, user_patterns in access_patterns.by_user.items():
            # Predict next likely accesses
            predicted_accesses = self.predict_next_accesses(user_patterns)
 
            for prediction in predicted_accesses:
                prefetch_cost = self.calculate_prefetch_cost(prediction)
                cache_miss_cost = self.calculate_cache_miss_cost(prediction)
 
                # Only prefetch if expected benefit > cost
                if prediction.probability * cache_miss_cost > prefetch_cost:
                    prefetch_candidates.append(PrefetchCandidate(
                        user_id=user_id,
                        resource=prediction.resource,
                        probability=prediction.probability,
                        cost_benefit=prediction.probability * cache_miss_cost - prefetch_cost
                    ))
 
        # Sort by cost-benefit ratio and select top candidates
        prefetch_candidates.sort(key=lambda x: x.cost_benefit, reverse=True)
 
        # Limit total prefetch bandwidth to budget
        selected_prefetch = []
        total_cost = 0
 
        for candidate in prefetch_candidates:
            if total_cost + candidate.prefetch_cost <= self.prefetch_budget:
                selected_prefetch.append(candidate)
                total_cost += candidate.prefetch_cost
 
        return PrefetchingStrategy(prefetch_list=selected_prefetch)

class CostGovernanceFramework:
    def __init__(self):
        self.budget_manager = BudgetManager()
        self.policy_engine = CostPolicyEngine()
        self.alert_manager = AlertManager()
 
    def implement_cost_controls(self, governance_config: GovernanceConfig) -> CostControls:
        """Implement automated cost governance and controls"""
 
        cost_controls = CostControls()
 
        # Budget-based controls
        for budget_config in governance_config.budgets:
            budget = Budget(
                name=budget_config.name,
                amount=budget_config.amount,
                period=budget_config.period,
                scope=budget_config.scope
            )
 
            # Set up automated actions when budget thresholds are exceeded
            budget.add_threshold_action(
                threshold=0.8,  # 80% of budget
                action=AlertAction(severity='warning',
                                 message='Budget 80% consumed')
            )
 
            budget.add_threshold_action(
                threshold=0.95,  # 95% of budget
                action=ThrottleAction(service='non_critical_operations')
            )
 
            budget.add_threshold_action(
                threshold=1.0,  # 100% of budget
                action=BlockAction(operations=['new_tenant_onboarding'])
            )
 
            cost_controls.add_budget(budget)
 
        # Resource quotas and limits
        for quota_config in governance_config.quotas:
            quota = ResourceQuota(
                resource_type=quota_config.resource_type,
                limit=quota_config.limit,
                scope=quota_config.scope
            )
 
            cost_controls.add_quota(quota)
 
        # Cost optimization policies
        optimization_policies = [
            # Automatic storage tiering
            AutoTieringPolicy(
                trigger_conditions=['storage_cost_increase > 20%'],
                actions=['evaluate_tiering_opportunities']
            ),
 
            # Idle resource cleanup
            IdleResourceCleanupPolicy(
                idle_threshold=timedelta(hours=24),
                resource_types=['compute_instances', 'storage_volumes'],
                exceptions=['production_tagged_resources']
            ),
 
            # Right-sizing enforcement
            RightSizingPolicy(
                evaluation_frequency='weekly',
                utilization_threshold=0.6,
                auto_resize=True,
                approval_required=True
            )
        ]
 
        cost_controls.add_policies(optimization_policies)
 
        return cost_controls
 
    def generate_cost_optimization_recommendations(self,
                                                  cost_data: CostData) -> OptimizationRecommendations:
        """Generate AI-driven cost optimization recommendations"""
 
        recommendations = OptimizationRecommendations()
 
        # Analyze spending patterns
        spending_analysis = self.analyze_spending_patterns(cost_data)
 
        # Storage optimization recommendations
        storage_recommendations = self.analyze_storage_optimization(cost_data.storage_costs)
        recommendations.extend(storage_recommendations)
 
        # Compute optimization recommendations
        compute_recommendations = self.analyze_compute_optimization(cost_data.compute_costs)
        recommendations.extend(compute_recommendations)
 
        # Network optimization recommendations
        network_recommendations = self.analyze_network_optimization(cost_data.network_costs)
        recommendations.extend(network_recommendations)
 
        # License and subscription optimization
        license_recommendations = self.analyze_license_optimization(cost_data.license_costs)
        recommendations.extend(license_recommendations)
 
        # Prioritize recommendations by potential impact
        recommendations.sort_by_impact()
 
        return recommendations

class UsageBasedBillingOptimizer:
    def __init__(self):
        self.pricing_model = DynamicPricingModel()
        self.usage_analyzer = UsageAnalyzer()
 
    def optimize_pricing_tiers(self, usage_data: UsageData) -> PricingOptimization:
        """Optimize pricing tiers based on actual usage patterns"""
 
        # Analyze usage distribution
        usage_distribution = self.usage_analyzer.analyze_distribution(usage_data)
 
        # Current tier analysis
        current_tiers = self.pricing_model.get_current_tiers()
        tier_utilization = {}
 
        for tier in current_tiers:
            users_in_tier = usage_data.get_users_in_tier(tier.name)
            utilization = self.calculate_tier_utilization(users_in_tier, tier)
            tier_utilization[tier.name] = utilization
 
        # Identify optimization opportunities
        optimization_opportunities = []
 
        # Find users paying for unused capacity
        for tier_name, utilization in tier_utilization.items():
            if utilization.average_utilization < 0.5:  # Less than 50% utilization
                opportunity = OptimizationOpportunity(
                    type='tier_downgrade',
                    affected_users=utilization.underutilized_users,
                    potential_savings=self.calculate_tier_savings(
                        utilization.underutilized_users, tier_name
                    )
                )
                optimization_opportunities.append(opportunity)
 
        # Find users exceeding tier limits frequently
        for tier_name, utilization in tier_utilization.items():
            if utilization.overage_frequency > 0.2:  # More than 20% overage
                opportunity = OptimizationOpportunity(
                    type='tier_upgrade',
                    affected_users=utilization.frequent_overage_users,
                    potential_savings=self.calculate_overage_savings(
                        utilization.frequent_overage_users, tier_name
                    )
                )
                optimization_opportunities.append(opportunity)
 
        # Recommend new tier structures
        recommended_tiers = self.design_optimal_tiers(usage_distribution)
 
        return PricingOptimization(
            current_efficiency=self.calculate_pricing_efficiency(tier_utilization),
            optimization_opportunities=optimization_opportunities,
            recommended_tier_structure=recommended_tiers,
            projected_revenue_impact=self.calculate_revenue_impact(
                optimization_opportunities, recommended_tiers
            )
        )
 
    def implement_dynamic_pricing(self, market_conditions: MarketConditions) -> DynamicPricingStrategy:
        """Implement dynamic pricing based on demand and capacity"""
 
        pricing_strategy = DynamicPricingStrategy()
 
        # Demand-based pricing
        for service in self.pricing_model.services:
            current_demand = market_conditions.get_service_demand(service.name)
            current_capacity = self.get_service_capacity(service.name)
 
            utilization_ratio = current_demand / current_capacity
 
            if utilization_ratio > 0.9:  # High utilization
                # Increase prices to reduce demand and increase revenue
                price_adjustment = PriceAdjustment(
                    service=service.name,
                    multiplier=1.2,  # 20% increase
                    duration=timedelta(hours=6),
                    reason='high_demand'
                )
                pricing_strategy.add_adjustment(price_adjustment)
 
            elif utilization_ratio < 0.3:  # Low utilization
                # Decrease prices to stimulate demand
                price_adjustment = PriceAdjustment(
                    service=service.name,
                    multiplier=0.85,  # 15% decrease
                    duration=timedelta(hours=12),
                    reason='excess_capacity'
                )
                pricing_strategy.add_adjustment(price_adjustment)
 
        # Time-based pricing
        for time_period in ['peak', 'off_peak', 'weekend']:
            period_config = market_conditions.get_period_config(time_period)
 
            pricing_strategy.add_time_based_rule(
                time_period=time_period,
                price_multiplier=period_config.price_multiplier,
                eligible_services=period_config.eligible_services
            )
 
        return pricing_strategy

class PredictiveCostModel:
    def __init__(self):
        self.historical_data = CostHistoryDatabase()
        self.ml_models = {
            'usage_forecast': UsageForecastModel(),
            'cost_driver_analysis': CostDriverAnalysisModel(),
            'anomaly_detection': CostAnomalyDetectionModel()
        }
 
    def forecast_costs(self, forecast_horizon: timedelta,
                      scenario_config: ScenarioConfig) -> CostForecast:
        """Generate detailed cost forecasts with scenario analysis"""
 
        # Base forecast using historical trends
        historical_trends = self.analyze_historical_trends()
        base_forecast = self.ml_models['usage_forecast'].predict(
            horizon=forecast_horizon,
            trends=historical_trends
        )
 
        # Apply scenario adjustments
        adjusted_forecasts = {}
 
        for scenario_name, scenario in scenario_config.scenarios.items():
            scenario_forecast = self.apply_scenario_adjustments(
                base_forecast, scenario
            )
            adjusted_forecasts[scenario_name] = scenario_forecast
 
        # Identify cost drivers and sensitivities
        cost_drivers = self.ml_models['cost_driver_analysis'].analyze(
            base_forecast, scenario_config
        )
 
        # Generate confidence intervals
        confidence_intervals = self.calculate_confidence_intervals(
            adjusted_forecasts, confidence_levels=[0.8, 0.9, 0.95]
        )
 
        return CostForecast(
            base_forecast=base_forecast,
            scenario_forecasts=adjusted_forecasts,
            cost_drivers=cost_drivers,
            confidence_intervals=confidence_intervals,
            forecast_accuracy=self.calculate_forecast_accuracy()
        )
 
    def detect_cost_anomalies(self, cost_data: CostData) -> AnomalyReport:
        """Detect and analyze cost anomalies for proactive optimization"""
 
        anomalies = self.ml_models['anomaly_detection'].detect(cost_data)
 
        # Categorize anomalies by type and severity
        categorized_anomalies = {
            'spending_spikes': [],
            'efficiency_drops': [],
            'billing_errors': [],
            'usage_anomalies': []
        }
 
        for anomaly in anomalies:
            category = self.categorize_anomaly(anomaly)
            categorized_anomalies[category].append(anomaly)
 
        # Generate investigation recommendations
        investigation_recommendations = []
        for category, category_anomalies in categorized_anomalies.items():
            if category_anomalies:
                recommendation = self.generate_investigation_recommendation(
                    category, category_anomalies
                )
                investigation_recommendations.append(recommendation)
 
        return AnomalyReport(
            anomalies_by_category=categorized_anomalies,
            severity_distribution=self.calculate_severity_distribution(anomalies),
            investigation_recommendations=investigation_recommendations,
            potential_cost_impact=self.estimate_anomaly_cost_impact(anomalies)
        )