Software Architecture Patterns – How to Find a Setup that Supports Scalability

Intro

Systems naturally evolve, but predicting all future use cases or an application's direction is challenging. For example, a startup still searching for its user base may use a minimum viable product to test market fit.

The initial development choices, including software architecture, may not be sufficient for the long term. 

As a result, many modern distributed systems combine multiple architectures and design patterns. To navigate this complexity, you need to understand the various patterns, their differences, and how they overlap.  

In this article, I’ll outline the main patterns and explain how you can support scalability in the further development of their products or services by making the right software architecture decisions.

Notable system design and software architecture patterns

Here’s an overview of the major software architecture patterns for web applications. Before you dive in, I want to underline that – though many sources online present the subject in clear-cut terms – you’re not choosing just one architecture pattern. Rather – a guiding principle.

As systems evolve, they may become a mix of event-driven communication, point-to-point integrations, orchestration and choreography of operation, and single-purpose or reusable services. What we often choose is the overarching design principle to stick with as the default.
This determines what constitutes an exception to the rule, of which there often are some.

Even in the simplest case – where a single process serves requests and communicates with a database – there is still a class of problems characteristic of distributed systems, such as concurrency issues, latency, and the potential for network partitions, all of which require careful consideration and handling.

Software and system patterns:

While discussing the major patterns, we’ll discuss them in the context of client-server architecture.

1. Monolithic Architecture

Monolithic architecture is a traditional software architecture where all components – business logic, database, often even UI – are tightly integrated into a single deployment unit. This approach simplifies development, testing, and deployment but can become difficult to scale as the application grows. Also, bear in mind that even with a separately deployed UI client, while the backend is monolithic, we're still talking about a monolith. 

Any update requires redeploying the entire system, making maintenance and scaling complex. While monolithic architectures work well for small applications, they often struggle with agility and performance in large-scale systems. 

Companies may start with a monolithic design and later transition to microservices for better modularity and scalability. However, monoliths can still be efficient for projects with straightforward requirements and/or controlled or otherwise limited traffic within the system capacity.

2. Modular Monolith Architecture

Also known as “modulith”, the modular monolith, with its self-contained, independent modules, sits between traditional monolithic architecture and microservices. It’s ideal when a monolithic system is hindering performance, affecting maintainability and you're anticipating that microservices might be a better fit as your project grows; you have clearly distinct areas of functionality that may better be suited to be developed independently.

While moduliths are more complex to develop at an application level than standard monoliths, they are less operationally challenging than microservices. Their modular nature allows them to support development and maintenance of large applications efficiently, while remaining open to splitting services out, if designed well and implemented with discipline.

A modular monolith can be fully implemented as if individual modules were integrating over a network; calling each other only through well-defined API clients, integrating data via asynchronous messaging.
This approach makes turning to true over-the-network distributed-system communication and integration of independent modules a matter of choice and infrastructure change - not a system rewrite.

With clearly defined boundaries between modules, yet still a single code base that is easy to deploy and test as a unit, work can be easily parallelized across individual modules, and benefit from code reuse for common functionality, utilities, and module setup.

We recommend this approach at Brainhub because it provides a cost-effective way to start a project while allowing for a smooth transition to other system design patterns. 

You can learn more about the benefits of modular monolith architecture in our dedicated piece. 

3. Microservices Architecture

Microservices architecture splits applications into independent services communicating over the network via APIs, enabling scalability, flexibility, and easier maintenance.

Teams can develop and deploy services separately, improving agility. While resilient and fast to develop, it adds complexity and requires very strong monitoring and observability. Used by Netflix and Amazon, it's best for high-traffic, high-availability applications that are large and require dynamic scaling.

In some ways, it may be thought of as a choice in work organization. It promotes technological independence and team autonomy. It is suitable for large projects that need scoped changes performed with little impact in day-to-day development.

While carrying many benefits, it is always worth considering the communication overhead in communicating, coordinating the rollout of breaking changes, and the interdisciplinary skillset required to implement and support such a complex system setup if production issues arise.

4. Service-Oriented Architecture (SOA)

While there can often be debate on their specific relationship, in an abstract sense SOA is the predecessor to microservices approach. Historically, it'd come to be associated with orchestration through ESB, though this is not a requirement of its stated principles.

Service-Oriented Architecture (SOA) structures software as reusable, interoperable services communicating via protocols like SOAP or REST. It promotes flexibility and system integration, focusing on designing functionality reusable by other services. However, it can introduce complexity, governance challenges, and performance overhead, especially with an enterprise service bus (ESB).

5. API Gateway

A pattern to implement a single point of entry into the system backend, facilitating the centralization of caching, authentication and authorization concerns, and request bundling.

It is worth noting that some of these responsibilities are also facilitated by an Enterprise Service Bus, but here the focus is on handling incoming external requests.

While centralizing these infrastructure concerns may be quite beneficial in itself, an API Gateway can also serve to address the coupling of the client application to backend and its API.

As mentioned before, even if deployed as separate units, the backend and frontend of a system can often be thought of as part of a monolithic solution, given that the frontend application can often be tightly coupled to both the network location of the backend servers as well as their specific API semantics; routes, expected shape of data, protocols.

An API Gateway can serve to address this coupling to shield the client application from “under the hood” changes through taking care of bundling and composing data returned by backend to the expected form (a Backend For Frontend), while leaving the backend service API surface free to change - even drastically - without directly affecting its external callers.

6. Microkernel (Plugin-Based) Architecture

Microkernel (plugin-based) software design pattern, features a core system with essential functions, while plugins add flexibility. Common in OSs, IDEs.
While niche in the realm of web applications, it can prove vital for solutions to be deployed at sites owned by the customer, private clouds etc. with different sets of functionality bundled per-customer on per-need, bundle size or licensing basis, if the business model requires that.

Communication:

7. Broker Pattern

Broker Pattern, often extended to an enterprise service bus (ESB), is a middleware-based communication pattern where components communicate via a central broker that handles service requests and responses.

This pattern decouples clients and services, enabling dynamic discovery, load balancing, and scalability. The broker manages communication, ensuring flexibility and interoperability, making it suitable for distributed systems, message queuing, and enterprise integration. 

However, it introduces a single point of failure and potential performance bottlenecks if not properly managed. It is worth noting that many ESB functionalities such as request aggregation, command and query dispatch, and other infrastructure concerns may also appear at the application architecture or API Gateway level. 

8. Event-Driven Architecture

Event-Driven Architecture (EDA), though it assumes a message broker, can be understood as a wider system design pattern, enabling asynchronous communication through messages (be it statements of fact such as events, commands or other), enhancing scalability, loose coupling, and responsiveness.

While not a strict requirement, it is often the default choice for most scenarios in data integration and inter-service communication in microservices system architecture, with synchronous communication utilized to a lesser extent.

While carrying many benefits, it calls for strong observability for debugging the choreography of operation across multiple services, understanding tradeoffs in latency and eventual consistency, message delivery patterns, additional service-level mechanisms such as outbox/inbox for service or communication failure with the broker, considerations around event design to not leak data schema details of services across the system.

Still, the last point, can often have less stringent enforcement when implementing propagating data changes through direct database captures (Change Data Capture), which is an approach that does have its benefits; be it the ease of implementation or issues at application level that make event production hard to implement - like needing to identify and modify a multitude of code paths responsible for raising change notification in an established code base.

Implications of software architecture choices – things to consider

Prepare for future scalability

When building a new project, it’s tempting to either over-optimize for future scalability or ignore it entirely. The best approach is somewhere in the middle – starting with a modular monolith. This structure keeps things relatively simple at first while ensuring that parts of the system can be separated into independent services later if needed.

If you don’t require a launch of a massive scale, focus on what’s actually needed now, but keep future growth in mind. One way to do this is by structuring components as if they were already distributed, even when running on a single server. This small upfront effort makes scaling later far easier without adding unnecessary complexity too soon.

Deciding whether to optimize early or delay it should be a business decision, not just a technical one. If rewriting parts of the system later is cheap and easy, and what may need changing is well-isolated. But if a lack of planning will lead to costly and painful rework, then some proactive design is worth the effort. 

At the end of the day, pragmatism beats perfection – what matters is building a system that actually works and can evolve with business needs.

Think how to rearchitect an existing system without breaking things

Rearchitecting a system is always a challenge, especially when it’s in active, heavy use and can’t afford downtime. Instead of rushing into big changes, take a gradual, strategic approach to avoid breaking things.

Start by identifying which parts of the system need to change and why. Is the goal better performance? Easier maintenance? Scalability? Once that’s clear, look for low-risk but impactful areas where changes can be introduced without disrupting everything.

One effective strategy is the Strangler Pattern—gradually replacing old parts of the system with new ones while keeping everything running. Instead of rewriting everything at once, introduce new components alongside the existing system and slowly shift traffic over as they prove stable.

Another key consideration is data consistency. If multiple services depend on shared data, plan how they will communicate during and after the transition. Using APIs, database replication, or event-driven messaging can help avoid inconsistencies. However, avoid relying too strongly on an old system’s data schema so that you aren’t trapped in a distributed monolith.

Finally, always test changes in a controlled environment first. Use feature flags, canary releases, or blue-green deployments to minimize risk. Rearchitecting doesn’t have to be disruptive—it just requires the right strategy and patience.

Database bottlenecks and data consistency challenges in high-traffic applications

Scaling a system often introduces data consistency challenges, especially when splitting a monolith into multiple deployment units. One classic example is an inventory system and a product catalog. When handled within a single process writing to a database in a local transaction, consistency is easy to maintain. But when they are separated, race conditions can occur—users might add an item to their cart, only to find out later that it’s actually out of stock.

At the same time, a similar class of problems applies when simply loading a catalog item into a UI that refreshes with latency. Should we open a long transaction even when just viewing the items?

Also, it’s worth asking yourself the following:

• Do we implement pessimistic concurrency mechanisms and a system for locking resources? Or do we accept that such scenarios happen, and that the system can function to eventually reach a consistent state? 
• Do customers complain frequently or is this an outlier, one that’s even worth addressing?

Ultimately, scaling means choosing the right trade-offs. If strict consistency is critical, expect more complexity and performance overhead. But if slight inconsistencies are acceptable, there are ways to simplify the system while still delivering a great user experience.

Consider technical trade-offs while making scalability decisions

The choices discussed, such as asynchronous messaging vs request-response communication, have implications beyond the degree of coupling we accept in the system.

Abstract models such as CAP theorem, or its extension PACELC, allow reasoning in general terms about the tradeoffs and balancing acts to be made in a distributed system.

The choice, however, is not purely technical, as it largely depends on what we require from functionality.

In the end; there are always some tradeoffs due to the nature of technology, even down to signal travel speed.

A real-world example: latency, fallback strategies, and cache complexity

Say we have a feature that requires near real-time updates to clients, sent piece-wise, item-by item.

Information is pushed to clients via asynchronous messaging at a steady pace, but a spike occurs that causes us to reach the limit of network bandwidth for this infrastructure piece, and we reach a cap.

This slows down what’s being sent. Our service generating this information works away steadily with no issues, but a bottleneck has been reached elsewhere.

Can the client wait for information to arrive 300 milliseconds later? How long is too long?

Maybe the client should react to unexpected latency, and fall back to directly asking the service for the newest state of data. Does the service have the capacity?

Maybe the service could cache the freshest data for the same duration that the accepted latency in asynchronous messaging scenario?

With many service instances this would require a distributed cache for each service instance to retrieve data from.

But then we have a subtle complication between the expected update arrival time, interval and frequency at which we ask the service, and the caching period with overlaps that may be harder to reason about.

What if the database from which the data is pulled by the service fails? If the data could be stale for longer periods, the service could still respond for prolonged time with data that in a sense is truthful, as no one can modify that data due to database failure, and the service is available.

And finally; if the service successfully sustains the load in such a scenario, and handles the issue as the fallback, should we maybe switch to this mechanism as the default? But what if data size retrieved directly grows in the future to size that makes piece-wise asynchronous updates more feasible again ?

At scale and in extremes, there’s always a choice to be made to accommodate the reality of current needs, and there is always the possibility of change. That’s why solution flexibility and foresight are paramount

This is just one scenario, but I believe it illustrates perfectly what kinds of considerations you must go through.

Bridge business and software architecture designs

Here’s how a conversation with a CTO might look:

"We want to build something."

"How does it fit into the system?"

"It'll work like this way and do that."

"Alright, let’s put something together."

But it’s different from saying, “This system must handle anything.” If a new client brings 100 million records instead of 10,000, the approach changes. Unless it's truly a one-off project, adding scalability might be worth the complexity.

If quick rewrites are possible, optimization isn't always needed. But this is a business decision – balancing risk, probability, and impact. At the C-level, making it work often outweighs perfect code. If needed, it can be rewritten later. Sometimes, speed and practicality win.

Work with an experienced partner

Building or rearchitecting a system isn’t just about writing code—it’s about making strategic decisions that impact scalability, performance, and long-term maintainability. Without the right expertise, it’s easy to create bottlenecks, overcomplicate the design, or make choices that will be costly to fix later.

An experienced partner brings a deep understanding of architecture patterns, trade-offs, and real-world scaling challenges. They can help:

• Avoid common pitfalls like database bottlenecks, inefficient service communication, or poor modularization.
• Choose the right approach – whether it’s a modular monolith, microservices, or a hybrid model, they can align the architecture with business goals.
• Ensure smooth transitions – rearchitecting a system without breaking existing functionality requires careful planning, testing, and rollout strategies.
• Optimize for growth – instead of premature optimization or short-term fixes, an expert ensures the system can evolve as demand increases.

If you want to ensure your app’s architecture allows scalability, reach out – we’ll be happy to discuss your project goals.