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The Death of Microservices? Building Scalable Modular Monoliths in 2025

Let's be honest. For the last decade, if you didn't say "Microservices" during a System Design interview, you weren't getting hired.

We watched startups with three engineers spinning up Kubernetes clusters and splitting their 500 lines of business logic into six different services before they even had a single paying customer. It was the golden age of Resume Driven Development.

But in 2025, the wind has shifted. From Amazon Prime Video's famous refactor (saving 90% in costs by moving away from microservices) to the Rails community's advocacy for the "Majesty of the Monolith," the industry is waking up to a harsh reality:

"Microservices are complexity debt with high interest rates, and most teams can't afford the payments."

This article isn't a rant against microservices—they have their place. Instead, it is a deep dive into why the pendulum is swinging back, the rise of the Modular Monolith, and how to architect systems that are actually maintainable.

The Broken Promise of Decoupling

The sales pitch was seductive: "Decouple your system! Let teams work independently! Deploy without fear!"

In theory, Service A talks to Service B via a clean API. If Service B changes, Service A doesn't care.
In practice, most teams built a Distributed Monolith. And that is the worst of both worlds.

1. You didn't decouple, you just added latency

If Service A calls Service B, which calls Service C... you are tightly coupled. The only difference is that instead of a function call that your IDE can refactor in milliseconds, you now have a network call that can fail in 100 different ways.

When you change the data shape in Service C, you often have to orchestrate a "lockstep deployment" across three different repositories to prevent breaking the chain. You haven't decoupled anything; you've just made your coupling slower and harder to debug.

2. The Latency Tax

In a monolithic application, function calls are effectively free (nanoseconds).
In a microservices architecture, that same interaction involves:

  1. Serialize request to JSON (CPU cost)
  2. Send over network (Latency)
  3. Ingress/Load Balancer (Latency)
  4. Deserialize payload (CPU cost)
  5. Execute logic
  6. Serialize response
  7. Network back...

Do this for every single interaction, and you face the "N+1 Problem" on a distributed scale. I've seen login flows that trigger 50+ internal HTTP calls just to return a user profile. No amount of caching fixes a fundamentally broken architecture.

The Operational Nightmare

"It works on my machine." Sure, but now your "machine" needs to spin up 15 Docker containers and your laptop sounds like a jet engine.

  • Observability: You can't just grep logs anymore. You need distributed tracing (Jaeger, Datadog) just to understand where a request failed.
  • Consistency: "Database per service" sounds great until you need to join data. Now you're implementing the Saga pattern, handling eventual consistency, and writing complex compensation logic for transactions that would have been a simple BEGIN... COMMIT.

The Solution: Modular Monolith

So, do we go back to "Spaghetti Code"? The 5GB server.js file where everything touches everything?
No. We move forward to the Modular Monolith.

A Modular Monolith creates strong boundaries within a single codebase and deployment unit. It gives you the logical separation of microservices with the operational simplicity of a monolith.

Core Rules

  1. Single Deployable Unit: One CI/CD pipeline. One binary or container.
  2. In-Memory Communication: Modules talk via public interfaces (function calls), not HTTP. Fast and type-safe.
  3. Strict Boundaries: Code in Module A cannot import from Module B's internal database models.

Implementation Strategy

In a modern Node.js/TypeScript environment, you can enforce this structure using tools like Nx, Turborepo, or strict ESLint rules.

src/
  modules/
    users/
      index.ts      # (PUBLIC) Only exports interfaces/facades
      core/         # (PRIVATE) Domain logic
      db/           # (PRIVATE) Database access
    orders/
      ...
  shared/           # Minimal shared utils
  app.ts            # Wiring everything together

If the orders module tries to import ../users/db/user-model.ts, your linter should fail the build. This preserves the architecture better than any diagram.

When To Actually Use Microservices

Am I saying Microservices are dead? No. They are dead as a default setting.
You should move to microservices when:

  1. Organizational Scale: You have 100+ backend engineers. Coordinating on a single repo becomes too slow. You pay the technical price to gain organizational agility.
  2. Diverse Technology Requirements: Module A needs GPUs + Python for AI, Module B is a standard CRUD app in Node.js. Separate them.
  3. Independent Scalability: One specific part of your system receives 1000x traffic compared to the rest (e.g., a voting button). Extract just that part.

Conclusion

System design is about trade-offs. For 95% of companies, Microservices are a bad trade.
In 2025, the most senior architectural decision you can make is to keep it simple.

Build a Modular Monolith. Define clear boundaries. And only break it apart when your metrics—not your ego—tell you it's time.
The "Boring" architecture is usually the one that makes the most money.

System DesignArchitectureMicroservicesBackendDevOps