Microservices vs Monolith: When is the right time to break up?
Microservices are hyped, but monoliths have their place. Learn when each architecture is right and how to make the transition when the time comes.
The debate between microservices and monolithic architectures is one of the hottest in software development. Microservices have been hyped as the solution to all problems, while monoliths are often portrayed as legacy and outdated. But the truth is far more nuanced: there's no one-size-fits-all answer. Both architectures have their place depending on context, team, and business requirements.
The problem with hype is that many companies rush into microservices without understanding the costs. They see success stories from Netflix, Amazon, and Spotify and think 'we need that too'. But they forget that these companies have hundreds of engineers and years of experience building distributed systems. For smaller teams, microservices can become a nightmare of complexity.
When monolith is the right choice
For new projects and small teams, monoliths are often the superior choice – even for startups planning to 'scale big'. A well-structured monolith gives you velocity, simplicity, and ability to iterate quickly. All code is in one place making changes easy to test and deploy. You have a clear overview of the system. Debugging is simpler because all logs are in one place and no network calls between components.
Monoliths get unfairly criticized as 'big balls of mud', but a well-architected monolith with clear module boundaries and strong encapsulation can be incredibly maintainable. The key is to design it like you would design microservices – with clear separation of concerns, loosely coupled modules, and well-defined interfaces between components. This is often called modular monolith.
Performance is often better in monoliths. Function calls within the same process are orders of magnitude faster than HTTP calls over network. Transaction management is simpler – ACID transactions in a database are much easier than distributed transactions across services. Resource efficiency is better since you don't have overhead from multiple processes, network serialization, and duplicated dependencies.
Operational simplicity is another big advantage. Deployment is straightforward – build one artifact, deploy one application. Monitoring is simpler with one application log and one set of metrics. Security is easier with fewer attack surfaces and no internal network traffic to secure. For teams without dedicated DevOps expertise, this is invaluable.
When microservices make sense
Microservices start making sense when your organization and system grow beyond what a monolith can handle. The key driver is usually team scaling – when you have multiple teams working on the same codebase and coordination becomes bottleneck. With microservices, teams can work independently on different services, deploy without coordinating with other teams, and choose tech stack best suited for their specific problem.
Independent scaling is another strong argument. Different parts of your system likely have very different load patterns. The payment service might handle 100 requests/second while product search gets 10,000. With microservices, you can scale each service independently based on its needs. This is both cost-efficient and gives better performance.
Technology flexibility is valuable in some cases. One service might benefit from Node.js for high I/O, another from Python for ML integration, and a third from Go for low latency requirements. But be careful – too much tech diversity can become operational nightmare. At Aidoni, we generally recommend standardizing on 2-3 technologies max.
Fault isolation is a major benefit. If one service crashes, others can continue working. You can implement graceful degradation – if recommendation service is down, users can still browse and buy products. This resilience is harder to achieve in monoliths where everything shares the same process.
But microservices come with significant costs
Distributed systems are fundamentally complex. Network calls can fail, latency is unpredictable, and eventual consistency is hard to reason about. Transactions spanning multiple services require complex patterns like Saga or two-phase commit. Debugging becomes nightmare – a single user request might touch 20 services with logs scattered everywhere.
Operational overhead explodes. You need service discovery, load balancing, API gateway, monitoring and tracing, centralized logging, CI/CD for each service, container orchestration (Kubernetes), and service mesh for secure service-to-service communication. This requires significant DevOps expertise and infrastructure investment.
Data management becomes much harder. Each service should own its data, but often you need to join data across services. This leads to API calls where you previously had simple SQL joins. Maintaining data consistency across services is challenging and you often have to accept eventual consistency.
Testing complexity increases dramatically. Integration tests require running multiple services. End-to-end tests are slow and flaky because of network dependencies. Contract testing becomes essential to ensure services can talk to each other. Local development requires running many services or complex mocking.
The migration: from monolith to microservices
If you decide microservices are right, the migration should be gradual – never rewrite everything at once. The strangler fig pattern is proven approach: slowly wrap and replace parts of the monolith with services. The monolith continues running while new functionality is built as services and old functionality is gradually extracted.
Start by identifying bounded contexts in your domain. These are natural seams where system can be split. Each bounded context should have clear responsibilities, minimal dependencies on other contexts, and be aligned with team boundaries. Good service boundaries follow domain logic, not technical layers.
Extract the simplest service first to learn. Choose something with few dependencies, clear boundaries, and not business-critical. This lets you build infrastructure, learn operational patterns, and make mistakes on something less risky. Once you've learned, extract services with more business value.
Database split is often the hardest part. Start by creating separate schemas in the same database. Then use database views or APIs to enforce data access boundaries. Finally, move to separate databases when you've verified boundaries work well. Rushing database split is common mistake leading to messy data dependencies.
Our recommendation at Aidoni
Start with well-structured monolith. Design it modularly from day one with clear boundaries between components. When you have multiple teams or clear scaling needs, consider breaking out the first service. Grow gradually into microservices architecture as needs drive it – not because it's trendy.
The best architecture is the one that fits your organization, team, and business context – not the one that sounds coolest on tech blogs. We help companies make this assessment and build the right architecture for their situation, whether that's a monolith, microservices, or something in between.
