Monolith to Modular: How to Break Up a Legacy Codebase

What is a modular monolith? A modular monolith is a software architecture where the application is deployed as a single unit (like a traditional monolith) but internally organized into clearly separated, independent modules with defined boundaries and interfaces. It provides most of the organizational benefits of microservices — clear ownership, independent development, and easier testing — without the operational complexity of distributed systems. For most growing applications, a modular monolith is the ideal middle ground between a tangled monolith and premature microservices.

Every successful software product starts as a monolith. That's not a failure — it's efficient. When you're moving fast, proving a market, and figuring out what your product actually is, a single deployable unit makes perfect sense.

The problem isn't that you built a monolith. The problem is that you never stopped to organize it as it grew. Now you have a tangle — and every change is slower, riskier, and more frustrating than it should be.

This guide walks you through the practical process of turning that tangle into a well-organized modular architecture, without the risk and overhead of jumping to microservices.

Why monoliths become painful

A monolith isn't inherently bad. A tangled monolith is. Here's what that looks like:

• Everything depends on everything else. Change the user model and the payment code breaks. Update the notification logic and the reporting module throws errors. Nobody can explain why.
• Deployments are all-or-nothing. You want to ship a small fix to the checkout flow, but you have to deploy the entire application — including the half-finished feature someone else is working on.
• Teams step on each other. Two developers can't work on different features without merge conflicts. The codebase has no clear ownership boundaries.
• Testing is impossible to scope. You can't test just the billing module because it reaches into the user module, the product module, and three utility files with 2,000 lines each.
• Onboarding takes months. New developers need to understand the entire system before they can safely change any part of it.

If more than two of these resonate, your monolith has become a liability. But the answer isn't to burn it down and start over with microservices. The answer is to modularize.

The architecture spectrum

Most discussions frame this as a binary: monolith vs. microservices. That's wrong. There's a spectrum:

The jump from "tangled monolith" to "modular monolith" gives you 80% of the benefits of microservices with 20% of the complexity. That's the move most teams should make.

Why jumping to microservices is usually wrong

We need to say this directly because the industry has been overselling microservices for a decade: microservices are an organizational scaling solution, not a technical one.

Microservices make sense when you have:

• 50+ developers who need to deploy independently without coordinating
• Dramatically different scaling needs — one component handles 100x the traffic of another
• Dedicated platform/DevOps teams to manage the infrastructure
• Mature observability — distributed tracing, centralized logging, service mesh

If you don't have all four, microservices will make your life harder, not easier. You'll trade one set of problems (tangled code) for another (distributed systems complexity):

• Network calls instead of function calls — latency, failure modes, retries
• Distributed transactions — what happens when Service A succeeds but Service B fails?
• Deployment orchestration — 15 services need to be deployed in the right order
• Debugging across services — a bug might span three services and two message queues
• Data consistency — the same customer record exists in five databases, slightly different in each

We've seen startups with 8 developers running 25 microservices. They spent more time debugging infrastructure than building features. Don't be that team.

The step-by-step modularization process

Step 1: Map your domain boundaries

Before writing any code, map out the logical domains in your application. These are the business capabilities — not technical layers.

Wrong way to split (by technical layer):

• Controllers
• Services
• Models
• Utilities

Right way to split (by business domain):

• User management (registration, authentication, profiles)
• Billing (subscriptions, invoicing, payments)
• Product catalog (products, categories, inventory)
• Orders (cart, checkout, fulfillment)
• Notifications (emails, SMS, in-app)

Each domain should make sense as something a non-technical person can describe: "the billing system" or "the product catalog."

How to identify boundaries:

• Look at which database tables are always queried together
• Look at which code changes tend to happen together
• Ask: "If a different team owned this piece, what would the interface look like?"
• Look for natural seams — areas where two concepts meet but don't deeply intertwine

Step 2: Establish module structure

Create a physical folder structure that enforces your domain boundaries:

src/
  modules/
    users/
      index.ts          # Public API — the only thing other modules can import
      users.service.ts
      users.repository.ts
      users.types.ts
      __tests__/
    billing/
      index.ts
      billing.service.ts
      billing.repository.ts
      billing.types.ts
      __tests__/
    orders/
      index.ts
      orders.service.ts
      orders.repository.ts
      orders.types.ts
      __tests__/

The critical rule: modules can only communicate through their public API (the index.ts file). No reaching into another module's internals. If the billing module needs user data, it calls users.getById(id) — it never imports users.repository.ts directly.

Step 3: Define interfaces and contracts

Each module's public API should be a thin, stable interface:

// modules/users/index.ts
export interface UserModule {
  getById(id: string): Promise<User>;
  getByEmail(email: string): Promise<User | null>;
  updateProfile(id: string, data: UpdateProfileInput): Promise<User>;
}

This interface is a contract. Other modules depend on it. The implementation behind it can change freely — different database, different caching strategy, different validation — without affecting anything outside the module.

Step 4: Untangle dependencies (the hard part)

This is where the real work happens. You'll find circular dependencies, shared state, and unclear ownership. Tackle them methodically:

Circular dependencies: If Module A imports from Module B and Module B imports from Module A, you need to either:

• Extract the shared concept into a new module
• Introduce an event/callback pattern where one module emits an event and the other subscribes

Shared database tables: If both billing and orders write to the same transactions table, decide who owns it. The owner provides the interface; the other module calls it.

Utility dumping grounds: The utils/ or helpers/ folder that every module imports. Break these into specific utilities that belong to specific modules, or extract genuinely shared utilities into a shared/ module with a strict API.

Step 5: Enforce boundaries

Without enforcement, developers will take shortcuts and your modules will re-tangle within months. Use tooling:

• ESLint import rules — Configure eslint-plugin-import or eslint-plugin-boundaries to forbid cross-module internal imports
• Architecture tests — Write tests that verify import paths conform to module boundaries
• Code review discipline — Any PR that adds a cross-module internal import gets flagged

This isn't bureaucracy. It's the entire point. Without enforcement, you're just reorganizing folders.

Practical patterns for incremental migration

The Strangler Fig pattern

Named after the strangler fig tree that grows around its host tree and eventually replaces it. Instead of rewriting all at once:

1. Identify one module to extract (start with the simplest, most self-contained one)
2. Create the new module structure alongside the existing code
3. Route new functionality through the new module
4. Gradually move existing functionality from the old code to the new module
5. When the old code is no longer called, delete it

This pattern lets you ship working software throughout the entire migration. There's never a "big bang" cutover.

Branch by Abstraction

When you need to replace a deeply embedded component:

1. Create an abstraction (interface) for the component you want to replace
2. Modify all consumers to use the abstraction instead of the concrete implementation
3. Build the new implementation behind the same abstraction
4. Switch the abstraction to use the new implementation
5. Remove the old implementation

This is especially useful for replacing shared services like authentication, logging, or database access.

Timeline and effort estimates

Be realistic about how long modularization takes:

The 20% rule applies here: allocate 20% of development time to modularization work. This keeps feature development moving while steadily improving the architecture.

Critical success factors:

• Executive buy-in (this takes time away from feature development)
• Clear definition of "done" for each module extraction
• Automated boundary enforcement from day one
• Incremental delivery — never go more than 2 weeks without shipping

When microservices DO make sense

After your monolith is well-modularized, you might find that one or two modules genuinely need to be separate services. Common reasons:

• Independent scaling: Your search module handles 100x the requests of your billing module
• Different technology needs: Your ML recommendation engine needs Python; everything else is TypeScript
• Team autonomy at scale: You have 60+ developers and teams need fully independent deployment
• Regulatory isolation: Certain data must be processed in specific regions or on isolated infrastructure

When this happens, extracting a well-defined module into a service is straightforward — because the boundaries and interfaces already exist. That's the whole point. Modularize first, extract to services only when proven necessary.

What this means for your codebase

If your monolith is causing pain, you don't need a grand architectural revolution. You need discipline, clear boundaries, and incremental improvement.

Start by mapping your domains. Extract one module. Enforce the boundaries. Repeat. In six months, you'll have a codebase that's dramatically easier to work in — without the operational nightmare of premature microservices.

If you're unsure where to start, a code audit can identify the highest-leverage module boundaries in your specific codebase — the places where extraction will have the biggest impact on development velocity.

For teams already dealing with the symptoms of a tangled codebase — slow deployments, cascading bugs, developer frustration — read our guide on recognizing technical debt and deciding whether to fix or rebuild.

Struggling with a monolith that's slowing your team down? Talk to us — we'll assess your architecture and give you an honest, practical modernization plan. Or get started with a technical audit.