Thinking about breaking up your monolith? Here's a battle-tested approach to migrating to microservices without killing your startup in the process.
Ivan Smirnov
Founder, Smirnov Labs
“Should we move to microservices?” This question comes up in almost every architecture review I conduct. The answer is rarely a simple yes or no—it depends on your scale, team structure, and business goals.
After helping multiple startups navigate this transition, I’ve learned that microservices aren’t a destination—they’re a tool. And like any tool, they solve specific problems while creating others. Let’s talk about when to make the move and how to do it successfully.
Let’s start with the hard truth: most startups don’t need microservices. You probably shouldn’t migrate if:
You should seriously consider microservices when:
Before touching your code, get your infrastructure ready:
Implement Distributed Tracing
Set Up Service Mesh (Optional but Recommended)
Containerize Everything
Establish Monitoring and Alerting
Anti-pattern: Skipping this phase and jumping straight to extraction. You’re flying blind without observability.
This is the most important phase. Get it wrong here, and you’ll create a distributed monolith—the worst of both worlds.
How to identify good service candidates:
Example from a real migration:
Monolith modules:
├── User Management (auth, profiles, permissions)
├── Billing (subscriptions, payments, invoicing)
├── Core Product (main application logic)
├── Analytics (data processing, reporting)
└── Notifications (email, SMS, push)
First extraction: Notifications
Why? Clear boundary, different scaling needs, minimal dependenciesStart small. Extract one low-risk service first to validate your approach.
Named after the strangler fig tree that grows around its host, this pattern lets you gradually replace the monolith without a risky big-bang migration.
Step 1: API Gateway
Add an API gateway in front of your monolith:
Client → API Gateway → MonolithStep 2: Extract First Service
Build your first microservice, route specific requests to it:
Client → API Gateway → Monolith
└→ Notifications ServiceStep 3: Dual Write (Temporarily)
During transition, write to both old and new systems:
async function sendNotification(userId, message) {
// New service (primary)
await notificationService.send(userId, message);
// Old monolith code (fallback)
try {
await legacyNotificationSend(userId, message);
} catch (err) {
console.error('Legacy notification failed:', err);
}
}Step 4: Validate and Cut Over
Step 5: Repeat
Extract the next service. Each extraction teaches you something.
Define clear contracts between services:
openapi: 3.0.0
info:
title: Notification Service API
version: 1.0.0
paths:
/notifications:
post:
summary: Send a notification
requestBody:
required: true
content:
application/json:
schema:
type: object
properties:
userId:
type: string
message:
type: string
channel:
type: string
enum: [email, sms, push]Version your APIs from day one. Breaking changes will happen.
For async operations, use events instead of direct service calls:
// Service A publishes event
await eventBus.publish('user.created', {
userId: '123',
email: '[email protected]',
timestamp: Date.now()
});
// Service B subscribes
eventBus.subscribe('user.created', async (event) => {
await notificationService.sendWelcomeEmail(event.email);
});Use RabbitMQ, Kafka, or AWS SNS/SQS depending on your scale.
Each service owns its data. No shared databases.
Monolith DB:
├── users
├── billing
├── products
└── notifications
After extraction:
Monolith DB: Notification Service DB:
├── users └── notification_logs
├── billing └── notification_preferences
└── productsHandling distributed data queries:
Services will fail. Build resilience from the start:
const circuitBreaker = new CircuitBreaker(notificationService.send, {
timeout: 3000, // 3 second timeout
errorThresholdPercentage: 50,
resetTimeout: 30000 // Try again after 30 seconds
});
// With exponential backoff
async function sendWithRetry(userId, message, maxRetries = 3) {
for (let i = 0; i < maxRetries; i++) {
try {
return await circuitBreaker.fire(userId, message);
} catch (err) {
if (i === maxRetries - 1) throw err;
await sleep(Math.pow(2, i) * 1000); // 1s, 2s, 4s
}
}
}Mistake: Extracting 10 services in your first sprint.
Solution: Extract 1-2 services per quarter. Master operational complexity before adding more.
Mistake: Service A calls Service B calls Service C for a single user request.
Solution: Denormalize data, use caching, consider API composition at the gateway.
Mistake: Trying to maintain ACID transactions across services.
Solution: Use saga patterns, eventual consistency, and idempotent operations.
Mistake: Each service deployed differently.
Solution: Standardize on infrastructure-as-code, CI/CD templates, and deployment patterns.
Be honest about the operational overhead:
Rule of thumb: Microservices add 30-50% operational overhead. Make sure the benefits justify the cost.
If you’re considering this migration, consider bringing in experienced help. The mistakes are expensive and the learning curve is steep. I’ve guided multiple startups through this transition and can help you:
Schedule a consultation to discuss your specific architecture and migration strategy.
Microservices are a powerful pattern for the right organization at the right time. The key is understanding:
Done right, microservices enable team autonomy and independent scaling. Done wrong, they create a distributed mess that’s harder to manage than the monolith ever was.
Start small, measure everything, and only proceed if the benefits clearly outweigh the costs.
If the challenges discussed in this article resonate with you, let's talk. I help startups navigate complex technology decisions, scale their teams, and build products that last.
