Why You Should NEVER Start With Microservices

The hidden costs of premature optimization

You’re starting a new project. You’ve read about how Netflix, Amazon, and Uber use microservices to handle millions of requests.

You want to build something scalable from day one.

So you begin designing your system with separate services for users, orders, payments, notifications, and inventory. Each with its own database, its own deployment pipeline, its own monitoring setup.

Six months later, you have 15 services, a team of 3 developers, and you’re spending 80% of your time debugging distributed system issues instead of building features.

This is the microservices trap, and it catches more teams than you might think.

In this article, I’ll explain:

• Why microservices are exciting but dangerous for new projects

• The hidden costs that nobody talks about

• When microservices actually make sense

• The “Monolith First” approach and why it works

• Why microservices are important in System Design Interviews

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The Microservices Hype

Microservices sound exciting and they always look impressive in system design interviews.

The idea of having small, independent services that can be developed, deployed, and scaled independently sounds like engineering nirvana.

When you read about microservices, you hear about:

• Independent deployments: Deploy one service without affecting others

• Technology flexibility: Use the best language/framework for each service

• Team autonomy: Small teams own their services end-to-end

• Scalability: Scale only the services that need it

• Fault isolation: One service failure doesn’t bring down the system

These benefits are real. But they come with a catch.

These benefits only materialize at scale, both in terms of traffic AND team size.

For a startup or small team, microservices don’t just fail to provide these benefits. They actively work against you.

Let me show you why.

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The Hidden Costs of Microservices

1. Distributed Systems Complexity

The moment you split your application into multiple services, you’ve created a distributed system. And distributed systems are hard.

Problems that didn’t exist in a monolith suddenly become your daily reality:

• Network failures - Service A can’t reach Service B. Now what?

• Latency - A function call that took microseconds is now a network call taking milliseconds

• Data consistency - How do you maintain consistency across multiple databases?

• Debugging nightmares - A single request might touch 10 services. Good luck tracing that error.

In a monolith, calling a function is guaranteed to either work or throw an exception.

In microservices, a service call can:

• Succeed

• Fail with an error

• Timeout (but did it actually succeed?)

• Succeed but return stale data

• Succeed on retry but cause duplicate operations

Each of these scenarios requires different handling. Multiply this by every service-to-service call in your system.

What about debugging?

In a monolith you get a stack trace.

In a microservices architecture, a single user request might touch 10 different services. When something goes wrong, you need to trace through logs across all of them, correlate timestamps, and figure out which service caused the failure.

But distributed complexity is just the beginning. How do you even manage all these services?

2. Operational Overhead

A monolith means one thing to deploy, monitor, and maintain.

With microservices, everything multiplies:

You’ll also need additional infrastructure that a monolith simply doesn’t require:

• Service discovery: How do services find each other?

• API Gateway: How do external clients reach your services?

• Distributed tracing: How do you debug across services?

• Circuit breakers: How do you handle cascading failures?

• Message queues: How do services communicate asynchronously?

Each of these adds complexity, cost, and potential failure points. A small team can easily spend more time managing infrastructure than building features.

Speaking of performance costs, let’s look at what happens to your application’s speed.

3. Network Is Now Your Bottleneck

In a monolith, data access is fast (~1 microsecond). Objects are in memory or a single database call away.

In microservices, what was once a method call is now:

1. Serialize request to JSON           ~0.5ms
2. DNS lookup                          ~1-10ms
3. TCP connection                      ~1-5ms
4. TLS handshake                       ~5-30ms
5. Send HTTP request                   ~1-5ms
6. Service B processes                 ~varies
7. Serialize response                  ~0.5ms
8. Network transfer back               ~1-5ms
9. Deserialize response                ~0.5ms

Time: 10-50+ milliseconds (10,000x slower!)       

This overhead adds up quickly. A page that required 5 internal method calls in a monolith might now require 5 network calls, each adding 10-50ms of latency.

4. Data Management Becomes a Nightmare

One of the “benefits” of microservices is that each service owns its data. But this creates serious challenges.

The Join Problem

In a monolith, you can easily join users with orders with products in a single SQL query:

SELECT u.name, o.total, p.title
FROM users u
JOIN orders o ON u.id = o.user_id
JOIN order_items oi ON o.id = oi.order_id
JOIN products p ON oi.product_id = p.id
WHERE u.id = 123;

In microservices, the same data lives in different databases:

To get user + orders + products, you need to:

1. Call the Users service

2. Call the Orders service

3. Call the Products service

4. Join the data in your application code

The Transaction Problem

In a monolith, you can wrap multiple operations in a database transaction:

In microservices, each service has its own database. You can’t have a transaction span multiple services. Instead, you need distributed transactions or saga pattern - both are complex and error-prone.

5. Testing Is Exponentially Harder

Testing a monolith is straightforward: spin up the application, run your tests.

Testing microservices requires multiple layers:

• Contract testing between services

• Integration tests that spin up multiple services

• End-to-end tests across the entire system

• Chaos engineering to verify fault tolerance

Your CI/CD pipeline becomes a complex orchestration of building, deploying, and testing multiple services in the correct order. A change to a shared API requires updating and testing all dependent services.

6. You Don’t Know Your Domain Yet

This might be the most important point.

When you’re starting a new project, you don’t fully understand your domain.

Requirements will change. You’ll discover that what you thought were separate concerns are actually deeply intertwined.

In a monolith, refactoring is moving code between packages. In microservices, refactoring is redesigning APIs, migrating data, and coordinating deployments across teams.

Martin Fowler calls this the Monolith First approach:

“Almost all the successful microservice stories have started with a monolith that got too big and was broken up.”

— Martin Fowler

Starting with microservices means you’re drawing service boundaries before you understand where they should be. And wrong boundaries are incredibly expensive to fix.

So when do microservices actually make sense?

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When Microservices Actually Make Sense

Microservices aren’t bad. They’re a solution to specific problems that most projects don’t have.

Consider microservices when:

1. You Have Multiple Teams That Need Independence

If you have 50+ developers and they’re constantly stepping on each other’s toes in a monolith, microservices can provide team autonomy.

The rule of thumb: You need at least one team (5-8 developers) per service for microservices to make organizational sense.

2. You Have Genuinely Different Scaling Requirements

Traffic Distribution:

Search Service    ████████████████████████████████  90%
User Service      ███                               8%
Admin Service     █                                 2%

If 90% of your traffic goes to your search service while your user service handles 10%, and you’re paying significantly more to scale everything together, microservices might help.

But ask yourself: does your company really have this problem? Or can one optimized monolith handle it all?

3. You Have Clear, Stable Domain Boundaries

If you’ve been running your monolith for years and you clearly understand which parts of the system are independent and rarely change together, you can confidently extract services.

4. You Have the Infrastructure and Expertise

Running microservices well requires mature infrastructure:

• A mature CI/CD pipeline

• Container orchestration (Kubernetes or similar)

• Service mesh or API gateway

• Distributed tracing and logging

• On-call rotation for multiple services

If you don’t have these, you’ll spend more time on infrastructure than features.

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The Monolith First Approach

Here’s the recommend approach:

1. Start With a Well-Structured Monolith

Build a monolith, but structure it well internally. Use clear module boundaries, separate concerns, and write clean interfaces between components.

my-app/
├── modules/
│   ├── users/
│   │   ├── api/
│   │   ├── service/
│   │   └── repository/
│   ├── orders/
│   │   ├── api/
│   │   ├── service/
│   │   └── repository/
│   └── products/
│       ├── api/
│       ├── service/
│       └── repository/
├── shared/
└── infrastructure/

This is sometimes called a “modular monolith” or “majestic monolith”.

2. Define Clear Internal Boundaries

Even within a monolith, modules should communicate through well-defined interfaces. Don’t let the Orders module directly access User database tables. Have it call the Users module’s public interface.

This makes future extraction easier while keeping current development simple.

3. Extract Services When Pain Becomes Real

Only extract a service when you experience actual pain that microservices would solve:

• “We can’t deploy because another team is using the same codebase”

• “We need to scale this component 100x but the rest doesn’t need it”

• “This component needs a different technology stack”

• “Deployments take 2 hours because the codebase is so large”

When you extract, you already understand the domain, the boundaries are proven, and you have real requirements to guide you.

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Real-World Examples

Some of the most successful tech companies started with monoliths:

Amazon

Amazon ran as a monolith for years before transitioning to services. They didn’t start with microservices. They evolved to them when their monolith couldn’t scale further and organizational bottlenecks became real.

Shopify

Shopify is famous for their “modular monolith” architecture. Even at massive scale, they’ve chosen to optimize their monolith rather than split into microservices. Their monolith handles billions of dollars in transactions.

Etsy

Etsy deliberately chose to stick with their monolith and invest in tooling to make it better, rather than split into microservices. They’ve been profitable and successful while competitors struggled with microservices complexity.

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Decision Framework

Use this framework to decide your architecture:

If you answered “No” to most of these, a monolith is almost certainly the right choice.

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But Wait: What About System Design Interviews?

Here’s where things get nuanced.

Everything I’ve said applies to real-world architecture decisions. But system design interviews are a different game.

In interviews, you should discuss microservices. Here’s why:

Interviews Test Knowledge, Not Just Judgment

Interviewers want to see that you understand distributed systems concepts, even if you wouldn’t use them on day one.

They’re evaluating:

• Can you break down a system into logical components?

• Do you understand service boundaries and APIs?

• Can you discuss trade-offs between approaches?

• Do you know how to scale individual components?

The Winning Interview Strategy

The best candidates show evolutionary thinking. Here’s how to approach it:

1. Acknowledge the starting point

“For an early-stage product, I’d start with a modular monolith. It’s simpler to develop, deploy, and debug.”

2. Then show the scaled architecture

“As the system grows to millions of users, we’d extract services based on scaling needs and team structure.”

3. Discuss trade-offs explicitly

“Splitting into microservices adds network latency and operational complexity, but gives us independent scaling and deployment.”

This approach demonstrates both practical judgment (start simple) and technical depth (know how to scale).

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Closing Thoughts

Microservices sound appealing and for good reasons. They’re intellectually interesting, they look great on architecture diagrams, and they’re what the cool companies use.

But the cool companies didn’t start with microservices. They evolved to them after years of growth.

Your job as an engineer isn’t to use the fanciest architecture, it’s to solve problems with the simplest solution that works.

For most projects, that solution is a well-structured monolith.

Start boring. Get fancy when you need to.

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See you soon,
Ashish