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Summary – The quest for modernization often drives you to split the IT system into microservices for scalability and resilience, but a purely technical separation without clear business boundaries creates a “distributed monolith”: hidden couplings, synchronous flows, cascading failures, and bloated CI/CD pipelines. Synchronized deployments and inefficient development cycles end up stalling the promised agility. You lose robustness and pace of change.
Solution: Adopt a modular monolith structured by functional domains, deployed as a single artifact, with isolated data schemas and clear governance, then gradually extract truly critical components.
In an environment where modernizing information systems is seen as a strategic imperative, microservices often present themselves as a silver bullet. Scalability, resilience, independent deployments: these promises appeal to IT leadership and business stakeholders. Yet many initiatives find themselves paradoxically bogged down by increased complexity and recurring incidents.
This article examines the antipattern of the “distributed monolith” and highlights its roots, its impacts, and its remedies. We will see why a technical decomposition without business considerations turns the promised agility into an operational nightmare. Then we’ll advocate for an alternative approach: the modular monolith, a more controlled framework to evolve at your own pace.
The Roots of the Distributed Monolith
The distributed monolith arises from a technical decomposition that doesn’t align with business boundaries. Without clear borders, each service becomes a potential point of failure and a source of hidden dependencies.
Poorly Defined Service Boundaries
When your service boundaries are drawn solely on technical criteria, you overlook the true business domains. A decomposition carried out without analyzing functional processes leads to services that constantly depend on each other, recreating tight coupling despite the distribution.
This imperfect breakdown results in synchronous call flows between clusters of services that should have been isolated. Each new feature triggers a cascade of adjustments across multiple services, slowing the system’s overall evolution.
The lack of a business-domain map worsens the issue: teams don’t speak the same language, and technical terms mask shared functionality. Over time, this leads to ever more decision meetings and increasingly inefficient development cycles.
Functional Coupling Despite Distribution
Technically, services are separated, but functionally they remain inseparable. You often see shared databases or rigid API contracts that lock down any change. This situation shifts software complexity onto infrastructure and operations.
Teams end up deploying multiple microservices simultaneously to ensure data or workflow consistency. The expected velocity gain vanishes, replaced by the need to orchestrate orchestrators and manage a multitude of CI/CD pipelines.
Each incident in one service has a domino effect on the others. Operations teams then have to monitor not a single monolith but an equally fragile distributed ecosystem, where the absence of one component or the incompatibility of a version can paralyze the entire system.
Example of Technical Decomposition Without Business Insight
A mid-sized Swiss manufacturing company split its legacy ERP application into ten microservices in less than six months. Teams followed a generic decomposition model without aligning each service to a specific business domain.
Result: every deployment required updating eight out of ten services to maintain data and transaction consistency. This project demonstrated that a purely technical split leads to a distributed monolith, with no autonomy gains for teams and over 30% higher operating costs.
Operational and Organizational Consequences
A poorly designed distributed system combines the drawbacks of both monoliths and distributed architectures. Synchronized deployments, cascading incidents, and slow evolution are its hallmarks.
Synchronized Deployments
Instead of independent releases, teams orchestrate deployment waves. Every functional change demands coordination of multiple CI/CD pipelines and several operations teams.
This forced synchronization extends maintenance windows, increases downtime, and raises the risk of human error. Procedures become cumbersome, with endless checklists before any production release.
In the end, the promised agility turns into inertia. The business waits for new features while IT fears triggering a major incident with every change, reducing deployment frequency.
Cascading Incidents
In a distributed monolith, fault isolation is an illusion. A synchronous call or a shared-database error can propagate a failure across all services.
Alerts multiply, and the operations team wastes time pinpointing the true source of an incident in a complex mesh. Recovery times lengthen, and the perceived reliability of the system plummets.
Without well-architected resilience mechanisms (circuit breakers, timeouts, dependency isolation), each exposed service multiplies points of fragility, harming user experience and business trust.
Example of Impact on a Retail Chain
A Swiss retail chain migrated its inventory management platform to a microservices architecture. The order, billing, and reporting services shared the same database without transaction isolation.
During a peak period, a version mismatch overloaded the billing service, making all orders impossible for several hours. This outage showed that distribution without business-driven decomposition creates a domino effect and significantly worsens incident impact.
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Organizational Pressure and Misaligned Objectives
Sometimes, migrating to microservices becomes an end in itself, detached from the actual product stakes. This pressure can lead to ignoring business analysis and multiplying antipatterns.
Microservices Goal versus Business Need
Many organizations set a KPI for “number of services” or a milestone for “going distributed” without questioning its alignment with the functional roadmap.
Architectural decisions are then based on competitor benchmarks or generic recommendations rather than on analysis of specific use cases and real workload patterns.
The risk is turning the architecture into a catalogue of disconnected services whose maintenance and evolution require an expensive cross-functional organization, with no concrete user benefits.
Absence of Domain-Driven Design
Without Domain-Driven Design, services are not aligned with business aggregates. You end up with duplicated features, poorly designed distributed transactions, and inconsistent data governance.
DDD helps define bounded contexts and autonomous data models. Conversely, without this discipline, each team creates its own domain vision, reinforcing coupling and technical debt.
This results in endless back-and-forth between functional and technical teams, global changes whenever a use case evolves, and the inability to scale in isolation.
Example from a Hospital IT Platform
A Swiss hospital group deployed multiple microservices without mapping business contexts, leading to duplication in appointment scheduling, patient records, and billing.
Teams ultimately had to rewrite the data access layer and regroup services into three clearly defined contexts, showing that an initial investment in DDD would have avoided this organizational collapse and major refactoring.
The Modular Monolith: A Pragmatic Alternative
Before diving into distribution, exploring a modular monolith can preserve clarity and reduce complexity. A module structure aligned with business domains fosters progressive, secure evolution of your information system.
Principles of the Modular Monolith
The modular monolith organizes code into clearly separated modules by business domain, while remaining in a single deployment unit. Each module has its own responsibility layer and internal APIs.
This approach limits circular dependencies and simplifies system comprehension. Unit and integration tests stay straightforward to implement, without requiring a distributed infrastructure.
The CI/CD pipeline delivers a single artifact, simplifying version management and team synchronization.
Code and Data Governance
In a modular monolith, the database can be shared, but each module uses dedicated schemas or namespaces, reducing the risk of conflicts or massive migrations.
Governance enforces naming conventions, cross-team code reviews, and clear documentation on each module’s boundaries and responsibilities.
Ultimately, the modular monolith makes it easy to identify areas to extract into independent services when the need truly arises, ensuring a more mature and prepared move to distribution.
Rethink Your Architecture Strategy: Modularity Before Distribution
The lure of microservices must be measured and justified by real use cases. The distributed monolith is not inevitable: it’s better to invest in business-driven modularity to maintain clarity, performance, and cost control. A modular monolith offers a solid learning ground before taking the step toward distribution.
Our Edana experts, IT solution architects, support you in analyzing your functional domains, defining clear boundaries, and implementing a contextual, scalable, and secure architecture. Together, we determine the best path for your organization—not by fashion, but by strategic necessity.
