Shady ASThe choice between microservices and monolithic architecture is one of the most consequential decisions an engineering organisation can make. According to a 2024 survey by Solo.io, nearly 85% of enterprises have already embraced microservices architecture, and the global cloud microservices market was valued at USD 1.84 billion in 2024, projected to reach USD 8.06 billion by 2032. Yet these impressive adoption figures mask an important reality: microservices are not a silver bullet, and the wrong architectural choice can cost organisations years of productivity.
A growing counter-movement recognises that complexity has a cost. According to a 2025 CNCF survey, approximately 42% of organisations that initially adopted microservices have consolidated at least some services back into larger deployable units, citing debugging complexity, operational overhead, and network latency as primary drivers. The architectural landscape is evolving beyond a binary choice toward a spectrum of options, with the modular monolith emerging as a compelling middle ground.
Understanding the architectural spectrum
The traditional monolith packages all application functionality into a single deployable unit. It is straightforward to develop, test, and deploy — a single codebase, a single deployment pipeline, a single database. For small teams working on well-defined products, the monolith offers unmatched simplicity. There is no network latency between components, no distributed transaction complexity, and debugging is as simple as stepping through code in a single process.
At the other end, microservices decompose an application into independently deployable services, each owning its own data store and communicating through well-defined APIs. This enables independent scaling, technology heterogeneity, and autonomous team ownership. However, it introduces distributed systems complexity: network failures, eventual consistency, service discovery, and the operational overhead of managing dozens or hundreds of deployment units.
Between these extremes lies the modular monolith — a single deployable unit with strictly enforced module boundaries. Each module has its own domain, its own data access patterns, and interacts with other modules through defined interfaces. The modular monolith preserves the operational simplicity of a monolith while establishing the architectural boundaries that make a future migration to microservices feasible. DZone research found that teams spent an average of 35% more time debugging in microservices architectures compared to modular monoliths.
A decision framework for architecture choice
The right architecture depends on context, not trends. A practical decision framework should evaluate four key dimensions: team size and structure, system complexity and domain boundaries, scaling requirements, and organisational maturity. For teams smaller than 20-30 developers working on a single product, a monolith or modular monolith almost always makes sense. The communication overhead and cognitive load of microservices outweigh the benefits at this scale.
Domain complexity is another critical factor. If your system has clearly defined bounded contexts with minimal cross-domain transactions, microservices can work well. But if your domain is highly interconnected — think financial trading systems where a single transaction touches inventory, pricing, risk, and compliance — the cost of distributed transactions may be prohibitive. The saga pattern and eventual consistency models add significant implementation complexity.
Scaling requirements should be evaluated honestly. If your entire application scales uniformly, a monolith with horizontal scaling behind a load balancer may be sufficient. Microservices shine when different components have drastically different scaling profiles — for example, a search service that handles 100x the traffic of an order processing service. Finally, organisational maturity matters: microservices require robust DevOps practices, comprehensive monitoring, and a culture of service ownership.
NIST research indicates that approximately 53% of SMEs have adopted microservices-based architectures, but adoption alone does not equal success. McKinsey reports that companies adopting enterprise agility with modular architectures see 30-50% improvements in operational performance, but only when the architecture matches the organisational context.
Essential microservices patterns
For organisations that have validated the need for microservices, several architectural patterns are essential for success. The API Gateway pattern provides a single entry point for client requests, handling cross-cutting concerns like authentication, rate limiting, and request routing. This prevents clients from needing to know about individual service locations and simplifies the client-side architecture.
Service mesh technology — with adoption at an all-time high as 70% of companies in the CNCF survey report running one — provides infrastructure-level solutions for service-to-service communication. Platforms like Istio, Linkerd, and Consul handle mutual TLS, load balancing, circuit breaking, and observability without requiring changes to application code. Nearly 65% of new service mesh deployments now enable mutual TLS by default.
The saga pattern addresses the challenge of distributed transactions across services. Instead of a two-phase commit, sagas coordinate a sequence of local transactions, with compensating transactions to handle failures. Choreography-based sagas use events, while orchestration-based sagas use a central coordinator. The choice between these approaches depends on the complexity of the business process and the need for visibility into transaction state.
For data management, the Database per Service pattern ensures loose coupling but requires careful handling of queries that span multiple services. The CQRS (Command Query Responsibility Segregation) pattern, often combined with event sourcing, provides an elegant solution by separating read and write models and using events to maintain materialised views across service boundaries.
The real challenges of microservices
The operational reality of microservices is more demanding than architecture diagrams suggest. Distributed tracing becomes essential — without tools like Jaeger, Zipkin, or commercial APM solutions, debugging a request that traverses 10 or more services becomes nearly impossible. Service mesh management overhead is significant, with 48% of DevOps engineers reporting difficulties with the steep learning curve of mesh architectures.
Data consistency is perhaps the most underestimated challenge. Eventual consistency is a fundamental characteristic of microservices architectures, and teams must design for scenarios where data is temporarily inconsistent across services. This requires a shift in mindset from database-centric thinking to event-driven design, and it demands that business stakeholders understand and accept the trade-offs.
Operational complexity scales with the number of services. Each service needs its own CI/CD pipeline, health checks, logging, metrics, and alerting. The infrastructure cost of running many small services — including the overhead of containerisation, orchestration, and service mesh — can significantly exceed the cost of running a well-optimised monolith. Kubernetes-native deployments represent over 70% of production microservices environments, adding another layer of operational knowledge that teams must acquire.
Migration strategies: from monolith to microservices
For organisations with existing monolithic applications, migration should be incremental rather than a big-bang rewrite. The Strangler Fig pattern — named after the tropical plant that gradually envelops its host — is the most proven approach. New functionality is built as microservices, while existing functionality is gradually extracted from the monolith. An API gateway or reverse proxy routes traffic between the monolith and new services.
A recommended migration path starts with establishing a modular monolith architecture within the existing codebase: define clear module boundaries, enforce interface contracts, and eliminate shared mutable state. This refactoring, which can be done incrementally alongside feature development, validates domain boundaries before committing to the operational complexity of distributed services.
When extracting services, start with the domain that has the clearest boundaries and the highest value from independent deployment or scaling. Common first candidates include authentication services, notification systems, or search functionality. Each extraction should be accompanied by comprehensive integration testing and canary deployment strategies to manage risk. The goal is to maintain business continuity throughout the migration, which may take months or years depending on the size of the monolith.
How Shady AS can help
At Shady AS SRL, we help organisations navigate the critical decision between monolithic and microservices architectures with pragmatism rather than dogma. Our experienced architects assess your team structure, domain complexity, scaling requirements, and operational maturity to recommend the architecture that delivers the most value for your specific context — whether that is a well-structured monolith, a modular monolith, or a microservices ecosystem.
For organisations considering or already undertaking a migration from monolith to microservices, we provide hands-on guidance through every phase: domain analysis and bounded context identification, technology stack selection, CI/CD pipeline design, observability implementation, and team upskilling. Contact Shady AS SRL in Brussels today to discuss your architectural strategy and ensure your technology choices support your long-term business goals.