For years, the tech world has been in a kind of architectural arms race. The rallying cry? “Microservices or bust.” It promised scalability, independent deployments, and team autonomy. And for massive, Netflix-scale engineering orgs, it delivered. But for a mid-sized team—you know, the 10 to 50-engineer squad building a real product with real deadlines—the story often played out differently.

Honestly, many of us ended up with what some call a “distributed monolith.” A tangled web of services where changing one thing meant deploying three others, where debugging required a detective’s hat and a map of network calls, and where the operational overhead just… never stopped growing.

Well, here’s the deal: a quiet, pragmatic counter-movement is gaining steam. It’s not about going backwards. It’s about choosing clarity over dogma. We’re talking about the modular monolith.

Why the Pendulum is Swinging Back (And It’s Not Just Hype)

Let’s be clear. This isn’t a rejection of microservices principles. It’s a refinement of them. The core idea—building systems from loosely coupled, focused components—is still golden. The shift is in how we package and deploy those components.

Think of it like organizing a workshop. Microservices gave every tool its own tiny, locked shed scattered across a yard. Sure, they’re independent, but to build a chair you’re running between sheds all day. A modular monolith? It’s a well-organized, single workshop with clearly labeled, dedicated toolboxes. Everything’s in one space, but the hammer doesn’t end up in the paint can.

For mid-sized teams, the pain points that trigger this re-evaluation are painfully familiar:

• Operational Overload: The sheer cognitive and infra cost of managing databases, deployments, and monitoring for dozens of services.
• Local Development Hell: Needing to run 17 different services just to test a button change.
• Network Complexity: Latency, partial failures, and the nightmare of distributed transactions.
• Diminished Velocity: All that “independence” ironically slowing feature development to a crawl.

Modular Monolith: The Architecture of Pragmatism

So what actually is it? A modular monolith is a single, deployable unit—one codebase, one database (usually)—but its internal structure is carved into strictly enforced, loosely coupled modules. Each module owns a distinct business domain (like “Billing,” “User Management,” “Inventory”) and communicates with others through well-defined internal interfaces, not network calls.

The magic is in the boundaries. You get the architectural discipline of service-oriented design without the physical separation. It forces you to think about APIs and contracts, but you enforce them with compiler checks and package visibility, not HTTP and a service mesh.

Key Characteristics That Make It Work

Aspect	Modular Monolith Approach
Deployment	One artifact. One deployment. Gloriously simple.
Communication	In-process method calls (or events within the process). Fast, reliable.
Data Management	Single database, but with schema segmentation per module. Maybe private tables.
Team Workflow	Teams own modules, not services. Clear code ownership within a shared repo.
Boundary Enforcement	Language-level constructs (Java modules, C# internal, Go packages).

The Practical Migration: How to Start Untangling

Okay, you’re convinced it’s worth a look. But you’re not starting from scratch—you’re likely in a microservices maze or a “big ball of mud” monolith. The shift is less about a big bang rewrite and more about intentional refactoring.

Here’s a potential path, a sort of loose guide:

1. Identify Your Bounded Contexts. Use domain-driven design (DDD) principles to map your core business domains. These become your target modules. Start with the most tangled, high-traffic area.
2. Consolidate, Don’t Rewrite. Begin merging the simplest, most tightly coupled microservices back into a main codebase. Treat them as separate modules from day one. This is the “extract module” refactoring, just in reverse.
3. Define Rigorous Internal APIs. For each module, define the interfaces it exposes and consumes. No direct database access from outside the module. No bypassing the API. This discipline is non-negotiable.
4. Introduce a Module System. Leverage your language’s features to enforce boundaries. Use dependency injection to manage inter-module communication. This makes violations obvious at build time.
5. Tackle the Database Last. Seriously. Start with a shared database but separate schemas or tables. Decouple the data logic within the application first. The physical database consolidation can be a later, separate project.

It’s a marathon, not a sprint. The goal is incremental improvement in developer experience and system clarity.

When It Shines—And When to Think Twice

This approach isn’t a universal cure-all. It’s a specific tool for specific situations. Let’s get real about its sweet spot.

Perfect for: Product-focused mid-sized teams where the primary constraint is development speed and clarity, not planetary-scale traffic. SaaS applications, internal business platforms, and startups finding product-market fit. Situations where the team topology is “stream-aligned” and can work well within a single codebase with clear ownership areas.

Think twice if: You have truly disparate scaling requirements (one part of your app needs 1000x the resources of another). Or if you have multiple teams that must deploy on completely independent schedules due to regulatory or operational reasons. Or, honestly, if your organization struggles with basic code discipline—a modular monolith will decay faster than a distributed system without that internal rigor.

The Human Factor: What Changes for Your Team

Beyond the code, this shift changes daily life. Onboarding new engineers gets easier—they clone one repo and run one command. Debugging? You get a single stack trace that tells the whole story. Testing becomes a more integrated, sane process.

But there’s a trade-off. You trade deployment independence for development simplicity. Teams need to coordinate merges and integrations more closely, though good CI/CD and trunk-based development patterns smooth this out. The key is that the coordination happens at the code integration phase, which is cheaper and faster than the service deployment coordination phase.

It requires a mature, collaborative team culture. One that values clean interfaces and collective code ownership. In fact, that might be the biggest prerequisite of all.

Wrapping Up: It’s About Intentional Design

Look, the point isn’t that microservices are “bad.” The point is that for many—maybe most—mid-sized teams, they were a premature optimization. A solution applied before the problem truly existed.

The move to a modular monolith is, at its heart, an admission that simplicity is a feature. That locality of behavior—having related code live close together—is a massive boost for developer productivity and happiness. It’s about getting 80% of the architectural benefit for 20% of the operational cost.

So maybe the next architectural discussion shouldn’t start with “microservices or monolith?” but with “how can we build with clear modules today, and separate them only when we have a proven, concrete need to do so tomorrow?” That’s the practical shift. It’s less about fashion, and more about building software that serves the people building it.