You adopt a modular monolith because you are tired of distributed failure modes, deployment choreography, and “we cannot change anything without a two week coordination tax.” Sensible. But the modular monolith is not a vacation from architecture. It is a different kind of discipline. The teams that succeed treat boundaries as first class, design for independent evolution, and instrument the codebase like it is already distributed. The teams that struggle keep the monolith shape and swap the label. You still get coupling, you just get it faster.
Below are the structural mistakes that quietly turn a modular monolith into a big ball of mud with better PR.
1. Treating “modules” as folders instead of contracts
If your module boundary is “everything under src/payments,” you did not create a module. You created a namespace. In practice, structural integrity comes from enforceable contracts: stable APIs, explicit data ownership, and rules about who can call what. Without that, the path of least resistance wins. Engineers import internal classes “just for now,” bypass domain rules, and you end up with implicit dependencies that no one can reason about during incident response.
The tell is when refactors require “search and replace across the repo” instead of changing a single module API and updating a handful of consumers. Real modularity forces you to pay the cost at the seams. That cost is the point.
2. Letting the database define the architecture
A common failure mode is “modular monolith on top, shared database underneath.” The code looks neat, but the schema becomes your integration layer. Any module can reach into any table, and the most convenient join becomes the default integration pattern. The result is tight coupling that is harder to see because it happens in SQL, not code review.
If you want modules that can evolve independently, you need explicit data ownership. That can still be a single physical database, but treat it as multiple logical databases: separate schemas, migrations owned by a module, and no cross module writes without an explicit API. Otherwise, you will rediscover microservices coupling, just inside a single Postgres cluster.
3. Building a shared “core” that becomes a dependency magnet
Every modular monolith seems to grow a core module. It starts as “shared types and utilities,” then becomes the place everyone adds “one more helper.” That turns into a reverse dependency trap: every module depends on core, and core depends on nothing, so core becomes the only safe place to put behavior. Eventually, core is where business logic goes to die because moving it later is politically and technically expensive.
A healthier pattern is to keep shared code aggressively boring: primitives, error types, observability wrappers, and truly generic infrastructure. If “core” needs to know about orders, invoices, or identity, it is not core. It is just a module that everyone is afraid to name.
4. Using synchronous in process calls for everything
In process calls are seductive because they feel free. No network. No serialization. No retries. But if every cross module interaction is a synchronous method call, you create temporal coupling and you erase backpressure boundaries. During load spikes, one hot path can fan out across modules and saturate CPU in ways that look like a distributed cascade, except now it is all in one process and harder to isolate.
Teams that scale modular monoliths tend to introduce asynchronous seams early. Not necessarily Kafka everywhere, but at least an internal event bus or durable outbox for domain events. When Shopify leaned on a monolith for years, part of the reason it worked was a strong internal discipline around boundaries and operational thinking that resembles distributed design even when the deployable unit is single. Your goal is to make “this module is slow” diagnosable without reading the whole call graph.
5. Skipping dependency direction rules
Even with clean code structure, you can still end up with circular dependencies at the domain level: billing depends on orders, orders depends on promotions, promotions depends on billing for eligibility. Teams often justify this with “they are all part of the same product.” That is how you get deadlocks in decision making and deadlocks in code.
You need a dependency strategy that is simple enough to enforce. Common patterns include a layered architecture (domain above infrastructure), or a stable core domain that other modules depend on, or explicit anti corruption layers for anything that crosses bounded contexts. The key is that dependency direction is not a suggestion. It is a structural rule that should fail the build.
6. Not investing in enforcement tooling early
A modular monolith without enforcement is like an SLO without alerts. It exists in a slide deck. The teams that keep boundaries intact usually add mechanical constraints: build time dependency checks, import restrictions, or tooling like ArchUnit (JVM) or Nx style dependency graphs (JS/TS) to prevent forbidden edges.
Even a simple rule set catches the slow drift. One “quick import” during an incident becomes a permanent coupling edge. Enforcement is not about bureaucracy. It is about preventing entropy from becoming your architecture.
7. Mixing domain logic with orchestration in the wrong places
When you adopt modules, a new question appears: where does workflow coordination live? Many teams sprinkle orchestration across modules, so each module becomes responsible for half a business process. That makes behavior hard to test and harder to change, because the “real logic” is emergent from interactions rather than encapsulated.
A pattern that works is to separate domain modules from application orchestration. Let modules own invariants and state transitions. Let a thin application layer coordinate use cases, call module APIs, and publish events. If you cannot explain where a business rule lives without pointing at three modules, you have a structural problem, not a code style issue.
8. Treating testing as optional because “it is one deployable”
Modular monoliths changes the testing game. You can run everything locally, which is great, but you also lose the forcing function that service boundaries impose. Teams often stop writing contract tests and rely on end to end suites that are slow, flaky, and not diagnostic. Then every change feels risky, and you are back to release trains, just inside one repo.
High leverage here looks like: module level tests that treat other modules as black boxes, contract tests for public APIs, and a limited set of end to end paths for “the whole system still boots.” Google’s SRE framing applies even here: you want fast feedback loops and a clear blast radius when something fails. Modular monoliths with only end to end tests is not modular. It is just centralized.
9. Ignoring observability because “there is no network”
This one bites late. In production, the hardest incidents are not “the service is down,” they are “latency doubled for 20 percent of requests.” In a monolith, that turns into “something in the call chain got slower” and the chain is long. If you did not add tracing, structured logs, and per module metrics, you cannot localize performance regressions.
Strong teams instrument module boundaries like they are RPC calls: duration, error rate, queue depth (if async), and saturation signals. Treat a cross module call as an integration point worth measuring. Otherwise your on call experience becomes archaeology.
10. Assuming the modular monolith is a permanent end state
The point of modular monoliths is optionality. You can keep it forever if it serves you. You can also carve out services when a module hits scaling or ownership limits. The mistake is adopting the pattern as a badge and never planning for evolution. If you do not know which modules could graduate to services, you probably did not define boundaries in a way that supports it.
A pragmatic approach is to pick one or two modules with natural seams and design them as “service ready.” Clear API. Owned data. No direct writes from outsiders. If the day comes when you need to split, it is a refactor, not a rewrite. If that day never comes, you still benefit from clean boundaries and better team autonomy.
Modular monoliths work when you treat structure as a product, not a one time refactor. The architecture lives in contracts, dependency rules, data ownership, and operational signals, not in the repo layout. If you want the upsides, invest early in enforcement and observability, and design module seams like you will need to defend them under load and during incidents. You are not avoiding complexity. You are relocating it to places you can control.
Kirstie a technology news reporter at DevX. She reports on emerging technologies and startups waiting to skyrocket.
