How to Break a Monolith into Microservices

Breaking a monolith into microservices while keeping production systems running requires executing hundreds of coordinated changes without breaking existing functionality. Teams that approach this as a big-bang rewrite spend 12-18 months rebuilding functionality that already works, burning engineering resources without shipping new features. Teams that extract services incrementally using strangler fig patterns complete migrations in 6-9 months while continuously delivering value. The difference is methodology, not technical skill.

This guide provides a step-by-step extraction process used successfully in production environments serving 10,000 to 10 million requests per day. You'll learn how to identify extraction candidates, implement gradual migration patterns, maintain data consistency across services, and measure success at each phase. The approach minimizes risk by making reversible changes and validating each extraction before proceeding to the next.

We'll cover technical implementation details including code examples, deployment strategies, and specific tools that simplify the migration process.

When to Start the Migration

Migrating from monolith to microservices makes sense only when you face specific, measurable problems that microservices solve better than monolith optimization. The most common valid triggers are team scaling bottlenecks, deployment coordination overhead exceeding two hours per release, or specific features requiring 10x different scaling than the baseline application.

Team scaling bottlenecks manifest when you have more than 15-20 developers working in one codebase. Merge conflicts occur daily. Code review queues exceed 24 hours. Different teams request conflicting database migrations. These symptoms indicate that your organization has outgrown single-codebase coordination. Splitting into microservices aligns architecture with team structure.

Deployment coordination becomes a blocker when releases require scheduling windows days in advance. Multiple teams must coordinate their changes. Rollbacks affect unrelated features. A bug in Feature A blocks deployment of Feature B. If you spend more than 20% of engineering time managing deployment coordination, that overhead likely exceeds the complexity cost of microservices.

Pre-Migration Assessment

Before starting any extraction work, quantify your current state and desired outcomes. Measure baseline metrics: deployment frequency, deployment duration, time to recover from failures, and mean time between failures. These provide objective criteria for whether microservices improve your situation.

Document your monolith's module boundaries. Most monoliths have implicit modules: /src/payments, /src/users, /src/orders. Analyze dependencies between modules. Which modules call which? What data do they share? Modules with minimal dependencies and clear boundaries make good extraction candidates. Heavily interconnected modules should stay together.

Calculate infrastructure costs. Your monolith runs on N servers at X cost per month. Microservices will run on different configurations. Estimate costs including per-service fixed overhead: load balancers, databases, monitoring, and operational time. If microservices cost 2x more and your constraints are primarily organizational (team coordination) rather than cost, this might be acceptable. If cost is a constraint, this informs which services to extract.

Identifying Extraction Candidates

The sequence of extraction matters significantly. Extracting the wrong service first creates maximum pain for minimum benefit. The right first extraction builds confidence, establishes patterns, and delivers measurable value. Choose services that are simple to extract and provide clear benefits.

Ideal first extraction candidates have three characteristics: clear boundaries with minimal dependencies, genuine reasons to exist independently, and low business risk if something goes wrong. Notification systems, reporting engines, and search functionality often meet these criteria. They consume data from core features but those features don't depend on them. If the notification service goes down, the core application continues functioning.

Avoid extracting foundational services first. Authentication, user management, and core domain models have dependencies from every other feature. Extracting them requires updating every part of the codebase simultaneously. Extract these later after you've established migration patterns and built team confidence with simpler extractions.

Dependency Analysis

Map dependencies between modules before choosing what to extract. Use static analysis tools to generate dependency graphs. In a Node.js codebase, tools like Madge analyze import statements and visualize module relationships. In Java, use JDepend or Structure101. The goal is understanding which modules are leaves (few dependencies on them) versus roots (many dependencies on them).

# Generate dependency graph for Node.js project
npx madge --image graph.png src/

# Analyze circular dependencies
npx madge --circular src/

# Show dependencies for specific module
npx madge --depends payments src/

Prioritize extracting leaf modules. A module that zero other modules import can be extracted without touching any other code. A module that 15 other modules import requires updating 15 import sites. The coordination cost is 15x higher. Build the dependency graph, identify leaves, and start there.

Check runtime dependencies beyond code imports. Does the module access other modules' database tables? Does it assume specific environment configuration? Does it rely on shared file systems or caching layers? These runtime dependencies are harder to identify than code dependencies but equally important. Extract modules with minimal runtime coupling first.

Value vs Effort Matrix

Plot potential extractions on a 2x2 matrix: value (benefit from extraction) versus effort (complexity to extract). High-value, low-effort candidates go first. High-value, high-effort candidates go second after you've built confidence. Low-value candidates probably shouldn't be extracted at all.

The Strangler Fig Pattern

The strangler fig pattern gradually replaces monolith functionality with microservices without requiring a full rewrite. The name comes from strangler fig trees that grow around host trees, eventually replacing them. In software, you build new services alongside the monolith, gradually route traffic to the new services, and deprecate monolith code paths once the services prove stable.

This pattern minimizes risk through incremental change. Each extraction is small, tested, and reversible. You validate that the new service works in production before removing monolith code. If issues arise, you route traffic back to the monolith while fixing the service. This beats big-bang migrations where you discover problems only after replacing everything.

Implementation requires three components: a routing layer that directs traffic to either monolith or service, the new microservice itself, and monitoring to verify the service matches monolith behavior. You gradually shift traffic from 1% to 100%, validating correctness at each step.

Setting Up the Routing Layer

Use a reverse proxy or API gateway to implement routing logic. For self-hosted solutions, nginx or HAProxy work well. For cloud deployments, use AWS Application Load Balancer, Google Cloud Load Balancing, or Azure Application Gateway. The proxy routes specific URL patterns to the new service and everything else to the monolith.

# Nginx configuration for strangler fig pattern
upstream monolith {
    server monolith:3000;
}

upstream notification_service {
    server notification-service:3001;
}

server {
    listen 80;

    # Route notification endpoints to new service
    location /api/notifications {
        proxy_pass http://notification_service;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }

    # Route everything else to monolith
    location / {
        proxy_pass http://monolith;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }
}

For more sophisticated routing, use feature flags to control traffic percentage. Route 1% of requests to the new service, 99% to the monolith. This requires the routing layer to make per-request decisions, typically by hashing user ID or request ID and checking if it falls within the enabled percentage.

Cloud API gateways provide this out of the box. AWS API Gateway supports weighted routing between targets. Kong and Tyk open-source gateways provide canary deployment features. This infrastructure investment pays dividends across all future extractions.

Building the First Microservice

Start by copying the relevant code from the monolith into a new service repository. This isn't the final architecture — you'll refactor later — but it gets a working service deployed quickly. The notification module from the monolith becomes the notification service with minimal changes.

Extract interface definitions from the monolith code. If the monolith's notification module exports functions like sendEmail(userId, template, data), the microservice implements the same interface as HTTP endpoints. This makes testing easier — you can verify the service returns the same results as the monolith for the same inputs.

// Monolith notification module
export async function sendEmail(userId, template, data) {
    const user = await db.users.findById(userId);
    const rendered = renderTemplate(template, data);
    await emailProvider.send(user.email, rendered);
}

// Microservice HTTP endpoint
app.post('/api/notifications/email', async (req, res) => {
    const { userId, template, data } = req.body;

    // Call the same function, just via HTTP
    await sendEmail(userId, template, data);

    res.json({ success: true });
});

Deploy the service but don't route traffic to it yet. Run integration tests against the service to verify it works independently. Check that it can access required data (more on this in the data migration section). Confirm monitoring, logging, and error tracking work. Only after the service proves functional in production infrastructure do you start routing traffic.

Handling Data Migration

The hardest part of extracting microservices is data ownership. The monolith has one database with all tables. Microservices should own their data, which means each service needs its own database. Migrating data while keeping the application running requires careful planning and gradual migration.

Three approaches handle data migration: shared database initially then split, dual writes during transition, or event-driven replication. Each has different complexity and risk profiles.

Shared Database Approach

The simplest approach: the new microservice connects to the same database as the monolith. The notification service reads from the monolith's users table and writes to the monolith's notifications table. This violates microservices principles but reduces migration risk. You've separated the service code without separating data.

This works as a transitional state. Extract the service, validate it works, get it stable in production. Then tackle data separation as a separate project. Trying to extract the service AND migrate data simultaneously multiplies complexity and failure modes. Do one thing at a time.

The downside is continued coupling. Schema changes to shared tables affect both monolith and service. Database failures impact both. You can't independently scale database load. Plan the data separation step from the beginning — shared database is a temporary state, not the end goal.

Dual Write Pattern

Dual writing maintains data consistency during migration by writing to both old and new locations. When creating a notification, the service writes to both the monolith database and its own database. Reads come from the service database. This lets you verify the service database has correct data before cutting over completely.

Implementation requires careful ordering. Write to the new database first, then the old. If the new database write fails, abort the entire operation. If the old database write fails, log the error but continue — the new database has the data, which is what matters for future reads.

async function createNotification(data) {
    // Write to new service database first
    const notification = await serviceDb.notifications.create(data);

    try {
        // Then write to monolith database for compatibility
        await monolithDb.notifications.create(data);
    } catch (error) {
        // Log but don't fail - service database is source of truth
        logger.error('Failed to sync to monolith database', error);
    }

    return notification;
}

Run dual writes for at least two weeks in production. This builds confidence that the service database has complete, accurate data. Monitor for discrepancies between databases. Once you verify consistency, stop reading from the monolith database. Later, stop writing to it. Eventually, deprecate those tables in the monolith schema.

Event-Driven Data Replication

For complex data relationships, event-driven replication provides better consistency. The monolith publishes events when data changes. The microservice consumes these events and builds its own data views. This creates eventual consistency but avoids dual-write coordination.

Use change data capture (CDC) tools like Debezium to publish database changes as events. The monolith doesn't need code changes — Debezium watches the database transaction log and publishes events to Kafka. The microservice consumes these events and maintains its own tables.

This approach shines when the service needs data from multiple monolith tables but in a different structure. The notification service needs user email addresses and preferences. Instead of joining the monolith's users and preferences tables, it maintains a notification_recipients table built from events. This denormalized view optimizes for the service's access patterns.

Implementing Gradual Rollout

Never route 100% of traffic to a new service on day one. Use gradual rollout with feature flags to control traffic percentage. Start with 1%, monitor for errors and latency differences, increase to 5%, then 10%, then 25%, 50%, and finally 100%. This catches problems when they affect small traffic volumes rather than all users.

Implement rollout controls in your routing layer. If using a cloud load balancer, configure weighted targets. If using a feature flag service like LaunchDarkly or Split, add routing logic that checks the flag value and directs traffic accordingly.

// Feature flag-based routing
app.use('/api/notifications', async (req, res, next) => {
    const useNewService = await featureFlags.isEnabled(
        'notification-service-rollout',
        req.user.id
    );

    if (useNewService) {
        // Proxy to microservice
        return httpProxy.web(req, res, {
            target: 'http://notification-service:3001'
        });
    } else {
        // Continue to monolith handler
        next();
    }
});

Monitoring and Comparison

During rollout, compare behavior between monolith and service. Track error rates, response times, and response content. If the service shows higher error rates or slower response times, investigate before increasing rollout percentage. If responses differ, you have a bug.

Implement shadow mode testing: send requests to both monolith and service but only return the monolith's response to users. Compare responses and log discrepancies. This lets you validate service behavior without affecting users. Once shadow testing shows 99%+ agreement, route actual traffic.

Create dashboards comparing key metrics side-by-side. Track monolith notification endpoint latency versus service notification endpoint latency. Graph error rates for both. Set up alerts for significant divergence. This operational visibility makes rollout decisions data-driven rather than guesswork.

Rollback Procedures

Define clear rollback criteria before starting rollout. If error rates exceed X%, rollback immediately. If latency exceeds Y milliseconds, rollback. If specific error types appear, rollback. Make these objective, automatic criteria rather than judgment calls.

Test rollback procedures before you need them. Intentionally route traffic to the service, verify it works, then practice rolling back to the monolith. Confirm that toggling the feature flag immediately shifts traffic. Time how long rollback takes. You want sub-60-second rollback capability, not a 30-minute emergency deploy.

Keep the monolith code path working for at least two weeks after reaching 100% service traffic. This provides instant rollback capability. If you discover a subtle bug in the service a week later, you can immediately route back to the monolith while fixing the service. Only remove monolith code after extended service stability.

Handling Service Communication

Once you have multiple services, they need to communicate. The monolith handled this through function calls. Microservices use HTTP APIs or message queues. Choosing the right communication pattern affects reliability, performance, and complexity.

Synchronous HTTP APIs work well for request-response patterns where the caller needs an immediate result. The payment service calls the user service to verify a user exists before processing payment. This is straightforward but creates coupling — the payment service fails if the user service is down.

Asynchronous messaging handles fire-and-forget scenarios. When an order is placed, publish an event. The notification service consumes it and sends confirmation emails. The inventory service consumes it and updates stock. The caller doesn't wait for these operations. This reduces coupling but adds eventual consistency complexity.

REST API Patterns

Design service APIs with backward compatibility in mind. Add fields, don't remove them. Use API versioning for breaking changes. Document expected response times and error codes. The payment service's API contract becomes critical infrastructure once other services depend on it.

// Good: Backward-compatible API changes
// v1: Returns basic user info
GET /api/users/123
{
    "id": "123",
    "email": "[email protected]",
    "name": "John Doe"
}

// v2: Adds new field, keeps all v1 fields
GET /api/users/123
{
    "id": "123",
    "email": "[email protected]",
    "name": "John Doe",
    "preferences": {  // New field, v1 clients ignore it
        "newsletter": true
    }
}

Implement circuit breakers to prevent cascading failures. If Service A calls Service B and Service B is down, the circuit breaker stops attempting calls after repeated failures. This prevents Service A from wasting resources on calls that will fail. Use libraries like Netflix Hystrix (Java) or opossum (Node.js).

Set aggressive timeouts on inter-service calls. If Service B normally responds in 100ms, set a 500ms timeout. This prevents slow dependencies from degrading the entire system. Return cached data or gracefully degraded responses when dependencies timeout.

Event-Driven Communication

For async operations, use a message queue or event bus. When the order service creates an order, it publishes an OrderCreated event to Kafka or RabbitMQ. Other services consume this event and react accordingly. The order service doesn't know or care what happens next.

This pattern decouples services temporally (they don't need to be online simultaneously) and functionally (adding new consumers doesn't require changing publishers). The downside is complexity: debugging requires tracing events through the system, and ensuring events are processed exactly once requires careful implementation.

// Publishing events from order service
async function createOrder(orderData) {
    const order = await db.orders.create(orderData);

    // Publish event for other services
    await eventBus.publish('OrderCreated', {
        orderId: order.id,
        userId: order.userId,
        items: order.items,
        total: order.total,
        createdAt: order.createdAt
    });

    return order;
}

// Consuming events in notification service
eventBus.subscribe('OrderCreated', async (event) => {
    await sendOrderConfirmation(
        event.userId,
        event.orderId
    );
});

Design events to be self-contained. Include all data consumers need rather than just IDs. The OrderCreated event includes order details, not just orderId. This lets consumers process events without calling back to the order service. If consumers need additional data, they maintain their own cached views built from events.

Testing Strategy for Microservices

Testing microservices requires different strategies than monoliths. Unit tests remain the same — test individual functions and classes. Integration tests become more complex because you're testing across network boundaries. End-to-end tests require orchestrating multiple services.

Focus testing effort on contract tests. These verify that service APIs match what consumers expect. If the payment service's /process-payment endpoint expects specific request fields and returns specific response fields, contract tests verify this without testing implementation details. Use tools like Pact for consumer-driven contract testing.

Integration Testing Approaches

Running all services for integration tests is slow and brittle. Instead, use test doubles for dependencies. When testing the notification service, mock the user service responses. This makes tests fast and reliable — they don't break when the user service changes unrelated functionality.

Maintain a test-doubles library that returns realistic responses. Capture actual production responses and use them as test fixtures. This ensures test doubles match reality. Update test doubles when service contracts change, catching integration issues in tests rather than production.

// Integration test with mocked dependency
describe('NotificationService', () => {
    beforeEach(() => {
        // Mock user service responses
        nock('http://user-service')
            .get('/api/users/123')
            .reply(200, {
                id: '123',
                email: '[email protected]',
                name: 'Test User'
            });
    });

    it('sends email to correct address', async () => {
        await notificationService.sendEmail('123', 'welcome', {});

        expect(emailProvider.send).toHaveBeenCalledWith(
            '[email protected]',
            expect.any(String)
        );
    });
});

End-to-End Testing

Run end-to-end tests in dedicated staging environments that mirror production architecture. Use Docker Compose or Kubernetes to spin up all services. Seed test data, execute workflows that span services, and verify outcomes. These tests catch integration issues that unit and contract tests miss.

Keep end-to-end tests focused on critical paths. Don't try to test every feature combination — that's what lower-level tests do. Test scenarios like "user registers, receives confirmation email, makes purchase, receives order confirmation." These validate that services work together for real user workflows.

Run end-to-end tests less frequently than unit tests. Unit tests run on every commit. Integration tests run on every pull request. End-to-end tests run nightly or before releases. This balances coverage with feedback speed — you can't wait 30 minutes for end-to-end tests on every commit.

Monitoring Distributed Systems

Monitoring microservices requires distributed tracing to understand request flows across services. When a user reports an error, you need to see which services were involved and where the failure occurred. Traditional monolith monitoring doesn't provide this visibility.

Implement correlation IDs that flow through all service calls. When Service A calls Service B, it includes a unique request ID in headers. Service B logs this ID with all operations. Service C receives it from Service B and logs it too. You can then query logs for that request ID and see the full request path.

// Express middleware to handle correlation IDs
app.use((req, res, next) => {
    // Use incoming correlation ID or generate new one
    req.correlationId = req.headers['x-correlation-id'] || uuidv4();

    // Add to response headers
    res.setHeader('x-correlation-id', req.correlationId);

    // Make available to all logs
    req.log = logger.child({ correlationId: req.correlationId });

    next();
});

// Include in outgoing service calls
async function callUserService(userId) {
    const response = await axios.get(
        `http://user-service/api/users/${userId}`,
        {
            headers: {
                'x-correlation-id': req.correlationId
            }
        }
    );
    return response.data;
}

Distributed Tracing Tools

Use dedicated tracing tools like Jaeger, Zipkin, or cloud provider solutions (AWS X-Ray, Google Cloud Trace). These provide visualization of request flows, showing which services were called, in what order, how long each took, and where errors occurred. This makes debugging distributed systems tractable.

Instrument your code with OpenTelemetry, an industry-standard tracing library. It provides automatic instrumentation for common frameworks (Express, Flask, Spring Boot) and exports traces to any compatible backend. This prevents vendor lock-in while providing production-grade tracing.

Monitor service-level indicators (SLIs) for each service: latency, error rate, and throughput. Set service-level objectives (SLOs) like "99% of requests complete in under 500ms" and "error rate stays below 0.1%." Alert when SLOs are violated. This provides objective service health metrics.

Centralized Logging

Aggregate logs from all services into a centralized system like ELK stack (Elasticsearch, Logstash, Kibana), Splunk, or cloud logging services. Structure logs as JSON with consistent fields: timestamp, service name, correlation ID, log level, message, and context.

This lets you query across all services. Find all ERROR logs for correlation ID X. Find all logs from the payment service in the last hour. Find all logs mentioning user ID Y. These queries are impossible when logs are scattered across service instances.

// Structured logging example
logger.info({
    service: 'notification-service',
    correlationId: req.correlationId,
    userId: userId,
    action: 'send_email',
    template: template,
    duration: Date.now() - startTime
}, 'Email sent successfully');

Common Migration Mistakes

The biggest mistake is extracting too many services simultaneously. Teams excitedly split the monolith into 10 services at once, then discover they've created a distributed monolith. Services are tightly coupled, deployments fail due to coordination issues, and debugging becomes impossible. Extract one service at a time, validate it works, then extract the next.

Another common failure is incorrect service boundaries. Teams split services along technical layers (API service, business logic service, data service) rather than business capabilities. This creates chatty inter-service communication where every request traverses multiple services. Service boundaries should match business domains: payments, orders, users, inventory.

Ignoring operational overhead creates migration failure. Each service needs deployment pipelines, monitoring, logging, error tracking, and documentation. A team that can barely manage one monolith deployment struggles with 10 service deployments. Build operational maturity before migrating. Implement CI/CD, monitoring, and incident response processes in the monolith, then apply these patterns to services.

The Distributed Monolith Anti-Pattern

Distributed monoliths have microservices architecture overhead without microservices benefits. Services share databases. Changes require synchronized deployments. Service A directly calls Service B's database. You've paid the complexity cost but services aren't actually independent.

Symptoms include: services that always deploy together, shared database tables, circular dependencies between services, and synchronous call chains where Service A calls Service B calls Service C for every request. If you see these patterns, stop extracting more services and fix the boundaries of existing ones.

Fixing distributed monoliths requires identifying true bounded contexts. The payment and order services shouldn't share database tables. If order creation requires immediate payment processing, handle this through well-defined APIs or events, not shared data access. Each service should be deployable and testable independently.

Measuring Migration Success

Define success metrics before starting migration. Common metrics include deployment frequency (can teams deploy independently?), lead time for changes (has feature development accelerated?), mean time to recovery (can you rollback faster?), and service reliability (has uptime improved or degraded?).

Track these metrics for the monolith before migration. Measure again after extracting each service. If deployment frequency decreases or lead time increases, microservices are making things worse, not better. This signals incorrect service boundaries or insufficient operational maturity.

Expect metrics to temporarily worsen during migration. Building new deployment pipelines and learning distributed system debugging takes time. The question is whether metrics improve after the learning curve. If they don't improve after 3-6 months, reconsider the migration approach.

Team Satisfaction Indicators

Beyond technical metrics, measure team satisfaction. Are developers happier working in microservices? Do they feel more productive? Are merge conflicts and deployment coordination reduced? Unhappy teams indicate architectural problems regardless of technical metrics.

Survey teams quarterly on questions like: "Can you deploy changes without coordinating with other teams?" "Do you understand what your service needs to do without understanding all services?" "Has your development velocity increased or decreased?" These subjective measures reveal organizational impact that technical metrics miss.

Frequently Asked Questions

How long does monolith-to-microservices migration take?

For a medium-sized monolith (50K-200K lines of code), expect 6-12 months to extract the first 3-5 services using strangler fig patterns. The first extraction takes longest (2-3 months) because you're building migration patterns. Subsequent extractions go faster. Complete migration might take 18-24 months. Teams that rush this process create distributed monoliths that require extensive refactoring.

Should I rewrite services from scratch or extract existing code?

Extract existing code first. Copy the relevant module into a new service, deploy it, validate it works. Then refactor the service code while keeping it running. Rewriting from scratch takes longer and introduces bugs. The strangler fig pattern works because you gradually replace working code, not rewrite everything. Save major refactoring for after the service is extracted and stable.

How many services should I end up with?

Match service count to team structure. One team (5-8 people) can own 2-4 services comfortably. If you have 3 teams, 6-12 services is reasonable. More services than this create operational overhead that exceeds team capacity. Fewer services mean teams are stepping on each other. The right number aligns with your organizational structure, not abstract decomposition ideals.

What do I do about shared code between services?

Create shared libraries for genuinely common code like logging, error handling, or API clients. Publish these as internal packages that services import. Avoid creating "framework" services that every service depends on — this creates coupling. Accept some code duplication between services rather than forcing shared dependencies. Duplication is cheaper than coupling in distributed systems.

How do I handle database transactions across services?

You can't use traditional ACID transactions across services. Implement the Saga pattern: each service performs its local transaction and publishes events. If later steps fail, earlier steps execute compensation logic. Design operations to be idempotent (safe to retry) and eventual consistent. This is fundamentally different from monolith transaction handling and requires rethinking business logic.

Should I migrate the database schema first or the code first?

Migrate code first using the shared database approach. Extract the service but have it connect to the monolith database. Validate the service works. Then migrate data as a separate step. Trying to migrate code and data simultaneously multiplies complexity and failure modes. Do one thing at a time.

What if the service extraction fails?

Design for easy rollback. Keep the monolith code path working. Use feature flags to route traffic. If the service has issues, toggle the flag to route traffic back to the monolith while you fix problems. This is why gradual rollout matters — catching failures at 1% traffic is much better than discovering them at 100%. Never remove monolith code until the service has proven stable for weeks.

How do I prevent services from becoming a distributed monolith?

Enforce these rules: services own their data (no shared database access), services communicate through APIs or events (no direct function calls), services deploy independently (no synchronized releases), and service boundaries match business domains (not technical layers). Review service architecture quarterly. If you see violations of these principles, refactor before extracting more services.

Conclusion

Breaking a monolith into microservices succeeds when you use gradual migration patterns, start with high-value low-complexity extractions, and validate each service in production before extracting the next. The strangler fig pattern minimizes risk through incremental change and easy rollback. Data migration requires careful planning using shared databases initially, then gradual separation through dual writes or event replication.

Expect the first extraction to take 2-3 months as you build migration patterns, deployment infrastructure, and monitoring capabilities. Subsequent extractions go faster. Measure success through deployment frequency, team velocity, and service reliability. If these metrics don't improve after 6 months, reconsider your approach. The goal is better organizational outcomes, not microservices for their own sake.