Microservices vs Monoliths: A 2025 Perspective - The Architecture Decision That Can Make or Break Your Business

// A well-structured monolith for a SaaS product
@SpringBootApplication
@EnableModularity // Custom annotation for modular monolith
public class SaasApplication {
    public static void main(String[] args) {
        SpringApplication.run(SaasApplication.class, args);
    }
}

// Modular structure within the monolith
saas-app/
├── modules/
│   ├── authentication/
│   │   ├── api/           // Public interfaces
│   │   ├── internal/      // Implementation
│   │   └── module-info.java
│   ├── billing/
│   ├── notifications/
│   └── reporting/
├── shared/
│   ├── domain/           // Shared domain objects
│   └── infrastructure/   // Common utilities
└── app/                  // Application layer

// Domain boundaries within a monolith
@Configuration
@ComponentScan(basePackages = "com.company.billing")
@EnableJpaRepositories(basePackages = "com.company.billing.repository")
public class BillingModuleConfig {
    
    @Bean
    @ConditionalOnProperty(name = "modules.billing.enabled", havingValue = "true")
    public BillingService billingService(
            PaymentGateway gateway,
            InvoiceRepository invoiceRepo,
            EventPublisher eventPublisher) {
        return new BillingService(gateway, invoiceRepo, eventPublisher);
    }
    
    // Module-specific transaction management
    @Bean
    public PlatformTransactionManager billingTransactionManager(
            @Qualifier("billingDataSource") DataSource dataSource) {
        return new DataSourceTransactionManager(dataSource);
    }
}

// Using Java modules for strong encapsulation
module com.company.billing {
    exports com.company.billing.api;
    exports com.company.billing.events;
    
    requires com.company.shared;
    requires spring.boot;
    requires spring.data.jpa;
    
    // Internal packages not exported
    // com.company.billing.internal
    // com.company.billing.repository
}

@Component
public class OrderService {
    private final ApplicationEventPublisher eventPublisher;
    
    @Transactional
    public Order createOrder(CreateOrderRequest request) {
        // Business logic
        var order = processOrder(request);
        
        // Publish event for other modules
        eventPublisher.publishEvent(new OrderCreatedEvent(
            order.getId(),
            order.getCustomerId(),
            order.getTotalAmount()
        ));
        
        return order;
    }
}

// In billing module
@EventListener
@Async
public void handleOrderCreated(OrderCreatedEvent event) {
    // Create invoice asynchronously
    invoiceService.createInvoice(event);
}

-- Logical separation with schemas
CREATE SCHEMA billing;
CREATE SCHEMA inventory;
CREATE SCHEMA orders;

-- Clear ownership
GRANT ALL ON SCHEMA billing TO billing_service;
GRANT SELECT ON orders.orders TO billing_service;

-- Foreign keys only through IDs, no cross-schema joins
CREATE TABLE billing.invoices (
    id UUID PRIMARY KEY,
    order_id UUID NOT NULL, -- Reference only, no FK
    amount DECIMAL(10,2) NOT NULL
);

# Team ownership in microservices
services:
  payment-service:
    team: payments-team
    sla: 99.99%
    on-call: true
    dependencies:
      - user-service (read-only)
      - notification-service (async)
    
  inventory-service:
    team: supply-chain
    sla: 99.9%
    scaling: horizontal
    data-store: dedicated-postgresql

// Service with specific scaling needs
@RestController
@ServiceIdentity(name = "image-processor")
public class ImageProcessingService {
    
    @PostMapping("/process")
    @CircuitBreaker(name = "image-processing")
    @Bulkhead(name = "image-processing", type = Type.THREADPOOL)
    public Mono<ProcessedImage> processImage(@RequestBody ImageRequest request) {
        return Mono.fromCallable(() -> {
            // CPU-intensive image processing
            return imageProcessor.process(request);
        })
        .subscribeOn(Schedulers.boundedElastic())
        .timeout(Duration.ofSeconds(30));
    }
}

// Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: image-processor
spec:
  replicas: 20  # High replica count for CPU-intensive work
  template:
    spec:
      containers:
      - name: image-processor
        resources:
          requests:
            cpu: "2"
            memory: "4Gi"
          limits:
            cpu: "4"
            memory: "8Gi"
      nodeSelector:
        workload-type: cpu-optimized

// Node.js service for real-time features
const WebSocket = require('ws');
const Redis = require('redis');

class RealtimeNotificationService {
    constructor() {
        this.wss = new WebSocket.Server({ port: 8080 });
        this.redis = Redis.createClient({ 
            url: process.env.REDIS_URL 
        });
        
        this.setupEventHandlers();
    }
    
    async handleOrderUpdate(orderId, status) {
        const subscribers = await this.redis.smembers(`order:${orderId}:subscribers`);
        
        subscribers.forEach(userId => {
            const ws = this.connections.get(userId);
            if (ws && ws.readyState === WebSocket.OPEN) {
                ws.send(JSON.stringify({
                    type: 'ORDER_UPDATE',
                    orderId,
                    status,
                    timestamp: Date.now()
                }));
            }
        });
    }
}

// ❌ WRONG: Synchronous chains
@Service
public class OrderService {
    public Order createOrder(OrderRequest request) {
        // This creates a distributed monolith!
        User user = userService.getUser(request.userId); // HTTP call
        boolean hasCredit = billingService.checkCredit(user); // HTTP call
        Inventory inv = inventoryService.reserve(request.items); // HTTP call
        Payment payment = paymentService.charge(user, request.total); // HTTP call
        
        return new Order(user, inv, payment);
    }
}

// ✅ CORRECT: Event-driven choreography
@Service
public class OrderService {
    public Order createOrder(OrderRequest request) {
        // Create order in pending state
        Order order = orderRepository.save(new Order(request, OrderStatus.PENDING));
        
        // Publish event for other services
        eventBus.publish(new OrderCreatedEvent(order));
        
        return order; // Return immediately
    }
    
    @EventListener
    public void on(PaymentCompletedEvent event) {
        orderRepository.updateStatus(event.orderId, OrderStatus.PAID);
        eventBus.publish(new OrderPaidEvent(event.orderId));
    }
}

// ❌ WRONG: Multiple services accessing same database
@Repository
public interface ProductRepository extends JpaRepository<Product, Long> {
    // Used by: catalog-service, inventory-service, pricing-service
    // Result: Tight coupling, no independent deployment
}

// ✅ CORRECT: Each service owns its data
// catalog-service
@Entity
@Table(name = "catalog_products")
public class CatalogProduct {
    @Id private UUID id;
    private String name;
    private String description;
    private List<String> images;
}

// inventory-service
@Entity
@Table(name = "inventory_items")
public class InventoryItem {
    @Id private UUID productId; // Reference only
    private Integer quantity;
    private Integer reserved;
}

@Component
public class ArchitectureDecisionEngine {
    
    public ArchitectureRecommendation analyze(OrganizationContext context) {
        // Weighted scoring based on real-world impact
        ScoreCard scoreCard = new ScoreCard();
        
        // Team Topology Score (Conway's Law in action)
        double teamScore = calculateTeamTopologyScore(context);
        scoreCard.addFactor("team_topology", teamScore, 0.3); // 30% weight
        
        // Domain Complexity Score
        double domainScore = calculateDomainComplexity(context);
        scoreCard.addFactor("domain_complexity", domainScore, 0.25);
        
        // Operational Maturity Score
        double opsScore = calculateOperationalMaturity(context);
        scoreCard.addFactor("operational_maturity", opsScore, 0.25);
        
        // Performance Requirements Score
        double perfScore = calculatePerformanceNeeds(context);
        scoreCard.addFactor("performance_needs", perfScore, 0.2);
        
        return generateRecommendation(scoreCard);
    }
    
    private double calculateTeamTopologyScore(OrganizationContext context) {
        // Based on Team Topologies book principles
        int teamCount = context.getTeamCount();
        double avgTeamSize = context.getAverageTeamSize();
        boolean hasStreamAlignedTeams = context.hasStreamAlignedTeams();
        boolean hasPlatformTeam = context.hasPlatformTeam();
        
        double score = 0.0;
        
        // Optimal team size is 5-9 people (Two-pizza teams)
        if (avgTeamSize >= 5 && avgTeamSize <= 9) {
            score += 0.3;
        }
        
        // Multiple stream-aligned teams favor microservices
        if (hasStreamAlignedTeams && teamCount > 3) {
            score += 0.4;
        }
        
        // Platform team essential for microservices
        if (hasPlatformTeam || teamCount < 4) {
            score += 0.3;
        } else if (teamCount >= 4) {
            score -= 0.2; // Penalty for no platform team
        }
        
        return score;
    }
}

@Service
public class ConwayAnalyzer {
    
    public ArchitectureAlignment analyzeAlignment(
            OrganizationStructure org, 
            SystemArchitecture arch) {
        
        // Map team boundaries to service boundaries
        Map<Team, Set<Service>> ownership = new HashMap<>();
        
        for (Team team : org.getTeams()) {
            Set<Service> services = arch.getServicesOwnedBy(team);
            ownership.put(team, services);
            
            // Calculate coupling between teams
            for (Team otherTeam : org.getTeams()) {
                if (team.equals(otherTeam)) continue;
                
                int sharedInterfaces = countSharedInterfaces(
                    services, 
                    arch.getServicesOwnedBy(otherTeam)
                );
                
                if (sharedInterfaces > 3) {
                    // High coupling indicates architectural mismatch
                    warnings.add(new ArchitecturalSmell(
                        SmellType.CONWAY_VIOLATION,
                        String.format(
                            "Teams %s and %s have %d shared interfaces - " +
                            "consider team reorganization or service merger",
                            team.getName(), 
                            otherTeam.getName(), 
                            sharedInterfaces
                        )
                    ));
                }
            }
        }
        
        return new ArchitectureAlignment(ownership, warnings);
    }
}

// Real-world example configuration
@Configuration
public class TeamTopologyConfig {
    
    @Bean
    public TeamStructure defineTeamStructure() {
        return TeamStructure.builder()
            // Stream-aligned teams (business capability focused)
            .streamAlignedTeam("checkout-team")
                .ownsServices("cart-service", "checkout-service", "payment-service")
                .businessCapability("customer-checkout-experience")
            .streamAlignedTeam("inventory-team")  
                .ownsServices("inventory-service", "warehouse-service")
                .businessCapability("inventory-management")
            
            // Enabling teams (technical capability focused)
            .enablingTeam("security-team")
                .supportsCapabilities("authentication", "authorization", "encryption")
            
            // Platform team (self-service platform)
            .platformTeam("platform-team")
                .ownsServices("api-gateway", "service-mesh", "observability-stack")
                .providesCapabilities("deployment", "monitoring", "service-discovery")
            
            // Complicated subsystem team
            .complicatedSubsystemTeam("ml-team")
                .ownsServices("recommendation-engine", "fraud-detection")
                .specialization("machine-learning")
            
            .build();
    }
}

@Component
public class StranglerFigMigration {
    
    public MigrationPlan createPlan(MonolithAnalysis analysis) {
        List<MigrationPhase> phases = new ArrayList<>();
        
        // Phase 1: Identify seams (Month 1-2)
        phases.add(new MigrationPhase(
            "Identify Seams",
            Duration.ofDays(60),
            List.of(
                "Analyze database coupling",
                "Map domain boundaries",
                "Identify high-value extraction targets",
                "Create dependency graph"
            )
        ));
        
        // Phase 2: Build platform foundation (Month 3-4)
        phases.add(new MigrationPhase(
            "Platform Foundation",
            Duration.ofDays(60),
            List.of(
                "Set up service mesh",
                "Implement API gateway",
                "Create CI/CD templates",
                "Establish observability"
            )
        ));
        
        // Phase 3: Extract first service (Month 5-6)
        phases.add(new MigrationPhase(
            "First Service Extraction",
            Duration.ofDays(60),
            List.of(
                "Extract authentication service",
                "Implement circuit breakers",
                "Set up data synchronization",
                "Validate in production"
            ),
            new MigrationMetrics(
                expectedDowntime: Duration.ZERO,
                riskLevel: RiskLevel.LOW,
                rollbackTime: Duration.ofMinutes(5)
            )
        ));
        
        // Phase 4-N: Incremental extraction (Month 7-18)
        for (BoundedContext context : analysis.getBoundedContexts()) {
            phases.add(createExtractionPhase(context));
        }
        
        return new MigrationPlan(phases, calculateTotalTimeline(phases));
    }
}

// Pattern 2: Big Bang Rewrite (Avoid at all costs!)
// Including for completeness and as a warning
@Deprecated
public class BigBangMigration {
    // Timeline: 24-36 months
    // Success rate: <20%
    // Common failure modes:
    // - Feature parity never achieved
    // - Business changes during rewrite
    // - Team burnout
    // - Budget overruns
}

// Pattern 3: Branch by Abstraction (6-12 months)
@Component  
public class BranchByAbstractionMigration {
    
    public MigrationPlan createPlan(MonolithAnalysis analysis) {
        return MigrationPlan.builder()
            .phase("Create Abstractions", Duration.ofDays(30))
                .task("Define service interfaces")
                .task("Implement facades")
                .task("Add feature toggles")
            .phase("Parallel Implementation", Duration.ofDays(120))
                .task("Build new services behind toggles")
                .task("Maintain backward compatibility")
                .task("Gradual traffic shifting")
            .phase("Cleanup", Duration.ofDays(30))
                .task("Remove old implementation")
                .task("Optimize service boundaries")
            .build();
    }
}

@Service
public class OperationalComplexityCalculator {
    
    public ComplexityReport calculateTotalComplexity(
            ArchitectureType type,
            SystemScale scale) {
        
        ComplexityReport report = new ComplexityReport();
        
        // Infrastructure Complexity
        report.addDimension("infrastructure", 
            calculateInfrastructureComplexity(type, scale));
            
        // Operational Complexity  
        report.addDimension("operational",
            calculateOperationalComplexity(type, scale));
            
        // Development Complexity
        report.addDimension("development",
            calculateDevelopmentComplexity(type, scale));
            
        // Cognitive Complexity
        report.addDimension("cognitive",
            calculateCognitiveComplexity(type, scale));
            
        return report;
    }
    
    private ComplexityScore calculateInfrastructureComplexity(
            ArchitectureType type, SystemScale scale) {
        
        if (type == ArchitectureType.MONOLITH) {
            return ComplexityScore.builder()
                .servers(3) // App servers
                .databases(1) // Single database
                .loadBalancers(1)
                .messageQueues(1)
                .caches(1)
                .totalComponents(7)
                .monthlyOperationalHours(10)
                .requiredExpertise(Expertise.JUNIOR)
                .build();
        } else { // MICROSERVICES
            int serviceCount = scale.getServiceCount();
            return ComplexityScore.builder()
                .servers(serviceCount * 3) // 3 instances per service
                .databases(serviceCount / 3) // Shared databases
                .loadBalancers(serviceCount + 1) // Per service + main
                .messageQueues(5) // Event bus, DLQ, etc
                .caches(serviceCount / 2)
                .serviceMesh(1)
                .apiGateway(1)
                .configServer(1)
                .serviceRegistry(1)
                .distributedTracing(1)
                .totalComponents(serviceCount * 5 + 20)
                .monthlyOperationalHours(200)
                .requiredExpertise(Expertise.SENIOR)
                .build();
        }
    }
    
    // Real metrics from production systems
    private OperationalMetrics getProductionMetrics(ArchitectureType type) {
        if (type == ArchitectureType.MONOLITH) {
            return OperationalMetrics.builder()
                .mttr(Duration.ofMinutes(15))
                .deploymentFrequency("5 per week")
                .deploymentDuration(Duration.ofMinutes(30))
                .rollbackTime(Duration.ofMinutes(5))
                .onCallRotation(2) // people
                .incidentsPerMonth(2)
                .debuggingComplexity(Complexity.LOW)
                .build();
        } else {
            return OperationalMetrics.builder()
                .mttr(Duration.ofHours(2))
                .deploymentFrequency("50 per week")
                .deploymentDuration(Duration.ofHours(2))
                .rollbackTime(Duration.ofMinutes(30))
                .onCallRotation(10) // people
                .incidentsPerMonth(15)
                .debuggingComplexity(Complexity.HIGH)
                .build();
        }
    }
}

@Configuration
public class ServiceMeshConfig {
    
    @Bean
    public ServiceMeshRequirements analyzeServiceMeshNeeds(
            MicroservicesArchitecture architecture) {
        
        ServiceMeshRequirements reqs = new ServiceMeshRequirements();
        
        // Istio vs Linkerd vs Consul Connect decision
        if (architecture.getServiceCount() > 20) {
            reqs.setRecommendation(ServiceMesh.ISTIO);
            reqs.addReason("Full-featured mesh needed for complex topology");
        } else if (architecture.needsMultiCluster()) {
            reqs.setRecommendation(ServiceMesh.CONSUL_CONNECT);
            reqs.addReason("Best multi-datacenter support");
        } else {
            reqs.setRecommendation(ServiceMesh.LINKERD);
            reqs.addReason("Lightweight, easy to operate");
        }
        
        // Calculate overhead
        reqs.setMemoryOverheadPerPod("100-150MB");
        reqs.setCpuOverheadPerPod("0.1-0.2 cores");
        reqs.setLatencyOverhead("1-2ms p99");
        
        // Required features checklist
        reqs.addRequiredFeatures(
            ServiceMeshFeature.MUTUAL_TLS,
            ServiceMeshFeature.CIRCUIT_BREAKING,
            ServiceMeshFeature.RETRY_POLICIES,
            ServiceMeshFeature.LOAD_BALANCING,
            ServiceMeshFeature.OBSERVABILITY
        );
        
        // Operational requirements
        reqs.setRequiredExpertise(Expertise.SENIOR);
        reqs.setMaintenanceHoursPerMonth(40);
        reqs.setUpgradeComplexity(Complexity.HIGH);
        
        return reqs;
    }
}

// Service mesh patterns
@Component
public class ServiceMeshPatterns {
    
    public void implementCircuitBreaker() {
        // Envoy configuration for circuit breaking
        String envoyConfig = """
            circuit_breakers:
              thresholds:
              - priority: DEFAULT
                max_connections: 100
                max_pending_requests: 100
                max_requests: 100
                max_retries: 3
                consecutive_errors: 5
                interval: 30s
                base_ejection_time: 30s
            """;
    }
    
    public void implementRetryPolicy() {
        // Istio retry policy
        String istioPolicy = """
            apiVersion: networking.istio.io/v1beta1
            kind: VirtualService
            spec:
              http:
              - retries:
                  attempts: 3
                  perTryTimeout: 2s
                  retryOn: 5xx,reset,connect-failure
                  retryRemoteLocalities: true
            """;
    }
}

public class ArchitectureDecisionMatrix {
    
    public ArchitectureRecommendation evaluate(ProjectContext context) {
        int monolithScore = 0;
        int microservicesScore = 0;
        
        // Team Size
        if (context.teamSize < 20) {
            monolithScore += 3;
        } else if (context.teamSize > 50) {
            microservicesScore += 3;
        }
        
        // Domain Complexity
        if (context.boundedContexts.size() <= 3) {
            monolithScore += 2;
        } else if (context.boundedContexts.size() > 10) {
            microservicesScore += 3;
        }
        
        // Scaling Requirements
        if (context.hasUniformScaling()) {
            monolithScore += 2;
        } else {
            microservicesScore += 2;
        }
        
        // Time to Market
        if (context.timeToMarket < 6) { // months
            monolithScore += 3;
        }
        
        // Operational Complexity Tolerance
        if (context.operationalMaturity < 3) { // 1-5 scale
            monolithScore += 3;
        } else if (context.operationalMaturity >= 4) {
            microservicesScore += 1;
        }
        
        return recommendBasedOnScores(monolithScore, microservicesScore);
    }
}

// Phase 1: Modular monolith with clear boundaries
@Module("payments")
public class PaymentModule {
    // All payment logic contained here
}

// Phase 2: Extract critical module to service
@FeignClient(name = "payment-service")
public interface PaymentServiceClient {
    @PostMapping("/payments")
    Payment processPayment(@RequestBody PaymentRequest request);
}

// Phase 3: Strangler fig pattern
@Service
public class PaymentFacade {
    private final PaymentModule localModule;
    private final PaymentServiceClient remoteService;
    private final FeatureToggle featureToggle;
    
    public Payment processPayment(PaymentRequest request) {
        if (featureToggle.isEnabled("use-payment-service")) {
            return remoteService.processPayment(request);
        }
        return localModule.processPayment(request);
    }
}

# Hybrid architecture
architecture:
  core-api:
    type: modular-monolith
    handles:
      - user-management
      - product-catalog
      - order-management
    technology: spring-boot
    
  specialized-services:
    image-processor:
      type: microservice
      scaling: horizontal
      technology: python-opencv
      
    recommendation-engine:
      type: microservice
      scaling: horizontal
      technology: python-tensorflow
      
    real-time-analytics:
      type: microservice
      scaling: horizontal
      technology: apache-flink

## From Monolith to Microservices

### Prerequisites
- [ ] Comprehensive test coverage (>80%)
- [ ] Clear module boundaries identified
- [ ] API contracts defined
- [ ] Event sourcing or CDC in place
- [ ] Observability infrastructure ready
- [ ] CI/CD pipeline supports multi-repo
- [ ] Team trained on distributed systems

### Migration Order (Recommended)
1. **Extract read-heavy services first** (less risk)
2. **Authentication/Authorization** (clear boundaries)
3. **Notification service** (typically async)
4. **Payment processing** (critical but isolated)
5. **Core business logic** (last, most complex)

company: B2B SaaS Platform
context:
  team_size: 8 developers
  team_structure: 1 full-stack team
  funding_stage: Series A
  time_to_market: Critical (6 months runway)
  
requirements:
  users: 1,000 businesses
  requests_per_day: 100k
  data_volume: 50GB
  uptime_sla: 99.5%
  
architecture_decision: Modular Monolith

rationale:
  - Single team = no coordination overhead
  - Fast iteration needed for PMF
  - Uniform scaling requirements
  - Limited operational expertise
  
implementation:
  structure:
    - /modules/auth (authentication & authorization)
    - /modules/billing (Stripe integration)
    - /modules/analytics (customer analytics)
    - /modules/api (REST API)
  deployment:
    - 3x AWS EC2 instances (blue-green deployment)
    - 1x RDS PostgreSQL (Multi-AZ)
    - CloudFront CDN
    - Total infra cost: < $1k/month
    
results:
  time_to_launch: 4 months
  deployment_frequency: 5x per day
  mttr: 15 minutes
  developer_productivity: 5 features/sprint
  technical_debt: Low
  
key_learnings:
  - Modular structure enabled easy extraction later
  - Single deployment simplified everything
  - Focus on product, not infrastructure

company: Global E-commerce Platform
context:
  team_count: 15 teams
  team_size_avg: 8 developers
  total_developers: 120
  platform_team_size: 20 engineers
  operational_maturity: High
  
requirements:
  users: 10M consumers
  requests_per_day: 1 billion
  peak_traffic: 10x during sales
  uptime_sla: 99.99%
  multi_region: Yes (US, EU, APAC)
  
architecture_decision: Microservices (30 services)

service_breakdown:
  customer_facing:
    - product-catalog: 3 instances per region
    - shopping-cart: 10 instances (stateful)
    - checkout: 5 instances (critical path)
    - payment: 3 instances (PCI compliant)
    - user-profile: 3 instances
    
  backend_services:
    - inventory: Event-driven updates
    - pricing: In-memory cache
    - recommendations: ML pipeline
    - search: Elasticsearch cluster
    - notifications: Async processing
    
  platform_services:
    - api-gateway: Kong
    - service-mesh: Istio
    - observability: Prometheus + Grafana + Jaeger
    - ci-cd: GitLab + ArgoCD
    
team_ownership:
  checkout_team:
    owns: [checkout, payment, order-service]
    on_call: 24/7 rotation (4 people)
    deployment_autonomy: Full
    
  catalog_team:
    owns: [product-catalog, search, categories]
    on_call: Business hours
    deployment_autonomy: Full
    
operational_metrics:
  deployments_per_day: 100+
  rollback_rate: 2%
  mttr: 12 minutes
  availability: 99.995%
  p99_latency: 250ms
  
complexity_management:
  - Dedicated platform team
  - Standardized service template
  - Automated compliance checks
  - Service mesh for communication
  - Centralized logging/monitoring

company: Fintech Compliance Platform
context:
  team_size: 25 developers
  team_structure: 
    - Core platform team (15)
    - Compliance team (5)
    - Data team (5)
  regulatory_requirements:
    - PCI DSS Level 1
    - SOX compliance
    - Data residency rules
    
architecture_decision: Hybrid (Monolith + 3 services)

architecture_details:
  monolith:
    handles:
      - User management
      - Transaction processing  
      - Reporting
      - Admin portal
    technology: Spring Boot + PostgreSQL
    deployment: Blue-green on AWS
    
  extracted_services:
    compliance_engine:
      reason: Regulatory isolation requirement
      technology: Java + HSM integration
      deployment: Separate VPC, no internet
      scaling: Vertical (compliance computations)
      
    document_processor:
      reason: Variable scaling needs
      technology: Python + OCR libs
      deployment: Kubernetes
      scaling: Horizontal (CPU intensive)
      
    audit_logger:
      reason: Immutability requirement
      technology: Golang + Append-only DB
      deployment: Multi-region for compliance
      scaling: Write-heavy optimization
      
integration_patterns:
  - Async messaging between services
  - Event sourcing for audit trail
  - API gateway for external access
  - Database-per-service
  
results:
  compliance_audits_passed: 3/3
  complexity_vs_full_microservices: -70%
  team_cognitive_load: Manageable
  deployment_complexity: Medium
  operational_overhead: 40 hours/month
  
key_insights:
  - Not everything needs to be a service
  - Extract based on real requirements
  - Hybrid can be a destination, not just a transition

@Service
public class ArchitectureComparisonService {
    
    public ComparisonReport compareRealImplementations() {
        
        Map<String, ArchitectureMetrics> implementations = Map.of(
            
            "saas_startup_monolith", new ArchitectureMetrics(
                teamSize: 8,
                serviceCount: 1,
                deploymentFrequency: "5/day",
                mttr: Duration.ofMinutes(15),
                infrastructureCost: 1000, // monthly USD
                operationalHours: 10, // monthly
                timeToMarket: Duration.ofDays(120),
                developerProductivity: 0.95 // relative
            ),
            
            "ecommerce_microservices", new ArchitectureMetrics(
                teamSize: 120,
                serviceCount: 30,
                deploymentFrequency: "100/day",
                mttr: Duration.ofMinutes(12),
                infrastructureCost: 125000, // monthly USD
                operationalHours: 800, // monthly
                timeToMarket: Duration.ofDays(365),
                developerProductivity: 0.60 // relative
            ),
            
            "fintech_hybrid", new ArchitectureMetrics(
                teamSize: 25,
                serviceCount: 4,
                deploymentFrequency: "10/day",
                mttr: Duration.ofMinutes(25),
                infrastructureCost: 15000, // monthly USD
                operationalHours: 40, // monthly
                timeToMarket: Duration.ofDays(180),
                developerProductivity: 0.80 // relative
            )
        );
        
        return new ComparisonReport(implementations);
    }
}

## Complexity Analysis: The Hidden Truth

### Monolith Technical Overhead
```yaml
# 1M requests/day, 100GB data
infrastructure:
  servers: 3 instances
  databases: 1 primary + 1 replica
  load-balancers: 1
  monitoring-points: ~50 metrics
  complexity-score: 2/10

operational:
  on-call-rotation: 1 person
  deployment-time: 15 minutes
  debugging-complexity: low
  mttr: < 30 minutes
  
development:
  feature-velocity: 5-8 features/sprint
  testing-complexity: simple
  local-dev-setup: 5 minutes
  cognitive-load: low

# Same load distributed
infrastructure:
  servers: 90 instances (30 services × 3)
  databases: 10 separate instances
  service-mesh: required
  api-gateway: required
  monitoring-points: ~3000 metrics
  complexity-score: 8/10

operational:
  on-call-rotation: 5-10 people
  deployment-time: 2-4 hours (coordinated)
  debugging-complexity: high
  mttr: 2-4 hours
  
development:
  feature-velocity: 2-3 features/sprint
  testing-complexity: high (integration tests)
  local-dev-setup: 30-60 minutes
  cognitive-load: high