In the current technology landscape, microservices architecture has become almost synonymous with scalability. Many organizations rush to decompose their monolithic applications, often assuming it’s the only path to handle growth. At DistCoTech, we take a more measured approach, recognizing that microservices aren’t always the answer—and sometimes they create more problems than they solve.

The Microservices Hype Cycle

Microservices architecture offers genuine benefits: independent scaling, technology diversity, organizational alignment, and fault isolation. These advantages have made microservices the default architectural choice for many new projects.

However, this approach comes with significant costs:

• Increased operational complexity
• Distributed system challenges (network latency, consistency, debugging)
• Service coordination overhead
• Deployment and testing complexity
• Monitoring and observability challenges

Before embracing microservices, it’s essential to assess whether these costs are justified by the benefits for your specific context.

Scaling Patterns Beyond Microservices

At DistCoTech, we’ve successfully implemented large-scale systems across various industries without defaulting to microservices. Here are proven approaches that still work exceptionally well:

1. Vertical Scaling: Still Viable in the Cloud Era

Cloud providers offer instances with substantial computing power. For many applications, scaling up remains a viable strategy before scaling out. Modern servers with high CPU core counts, substantial RAM, and SSD storage can handle workloads that would have required multiple machines just a few years ago.

In our OTRO project, we initially deployed on larger instances rather than immediately splitting into microservices. This approach allowed the platform to successfully handle 225,000 user registrations in less than 20 minutes during an unexpected early launch, while maintaining a simpler operational model.

2. Modular Monoliths: Separation Without Distribution

A well-designed monolith with clear module boundaries offers many of the architectural benefits of microservices without the distributed systems challenges. Using techniques like:

• Domain-driven design with bounded contexts
• Clean architecture with well-defined interfaces
• Module-level encapsulation of data and logic
• Internal API contracts between modules

These approaches provide separation of concerns without introducing network boundaries and their associated complexities.

For our LiFEREADER project, we rebuilt a struggling application using a modular Spring-based architecture. This improved maintainability and allowed independent work on different system areas without introducing the operational complexity of microservices.

3. Horizontal Scaling with Stateless Components

For many applications, the simplest path to scale involves:

• Ensuring application components are stateless
• Storing state in dedicated data stores (databases, caches)
• Deploying multiple identical instances behind load balancers

This approach provides linear scalability for application logic while concentrating the scaling challenges in purpose-built data management systems that excel at those specific problems.

4. Database Scaling Strategies

Often, the database is the first scaling bottleneck. Effective solutions include:

• Read replicas for distributing query load
• Sharding for distributing write load
• Caching layers to reduce database load
• Command-Query Responsibility Segregation (CQRS) to optimize read and write paths independently

These patterns allow significant scaling without changing the application architecture.

In our Mporium project, which processes large volumes of real-time data, we implemented Kafka for stream processing and Couchbase for data storage, allowing high throughput without decomposing the application itself into microservices.

5. Asynchronous Processing and Event-Driven Design

Moving appropriate workloads to asynchronous processing can dramatically improve scalability and responsiveness:

• Background job processing for computationally intensive tasks
• Event sourcing for state management
• Message queues for decoupling components
• Event-driven architecture for workload distribution

These patterns can be implemented within a monolithic application structure while providing many of the same benefits as microservices.

When to Consider Microservices

Microservices aren’t wrong—they’re just not always right. At DistCoTech, we consider microservices when:

• Different components have dramatically different scaling needs
• Team organization requires clear ownership boundaries
• Technology diversity offers compelling benefits for specific components
• The complexity cost is justified by measurable benefits

Our approach is pragmatic: we use the simplest architecture that solves the actual problem, not the one that’s currently trending.

Case Study: Scaling a Video Platform Without Microservices

For our Linius project, a digital video virtualization platform, we faced significant scaling challenges. The system needed to handle video processing, content management, AI-driven metadata enrichment, and delivery to various clients.

Rather than immediately decomposing into microservices, we:

1. Built a modular monolith for the core business logic
2. Scaled the data layer independently using distributed MongoDB replica clusters
3. Implemented an ELK stack for search functionality
4. Used cloud-based scaling for processing-intensive components
5. Designed clean internal interfaces that could later become service boundaries if needed

This approach delivered a commercially successful platform that now serves customers in news, sports, and entertainment sectors while maintaining operational simplicity.

The Engineering Discipline of Knowing When to Scale What

At DistCoTech, we’re agnostic to technology as such, with choices being driven by customers’ preferences and the task at hand. This applies to architecture as well—we’re neither pro-monolith nor pro-microservices. We’re pro-solution.

Engineering discipline means understanding available options and selecting the right one for the specific context. Sometimes that means microservices, but often it means applying proven scaling patterns to more traditional architectures.

Conclusion: Fit Architecture to the Problem, Not the Trend

Microservices have their place in the architectural toolkit, but they’re not the only tool available. By understanding the full range of scaling options and their respective trade-offs, you can choose the approach that delivers the best value for your specific needs.

At DistCoTech, we build and operate software manufacturing factories with an emphasis on continuous, repeatable, fully automated development and release processes. This engineering mindset extends to our architectural decisions, where we emphasize pragmatic solutions over technological fashion.

The right architecture isn’t the most complex or the most simple—it’s the one most precisely calibrated to solve your specific problem.