Throughout our experience in software development, one of the most challenging decisions we face is choosing the right architecture for our projects. Software architecture is the backbone of any application, and the decision between opting for a simple or a scalable approach can have a significant impact on the success of a project.
Context matters more than technology
When we start a new project, the temptation to apply the latest best practices and modern patterns is strong. After all, we want to build something that lasts, that scales, and that is maintainable. But the truth is that the best architecture is not the one that uses the most advanced technologies, but the one that solves the right problem at the right time.
The dilemma arises exactly here: how do we balance the need to deliver value quickly with the concern of not creating unsustainable technical debt? How do we know when we are being pragmatic and when we are being negligent?
The answer lies in deeply understanding the business context before opening the code editor.
MVP vs Final Product: different stages, different decisions
When we are validating a hypothesis
At the MVP stage, the priority is to learn fast. We need to validate whether the problem we identified really exists and whether our solution makes sense for the market. At this moment, over engineering is the biggest enemy of validation.
We don’t need microservices, distributed cache, message queues, or hexagonal architecture when we still don’t know if anyone will use the product. The risk here is not technical, it’s market-related. We need to build the minimum necessary to test our hypothesis and be ready to pivot or completely discard the solution.
The ideal architecture for an MVP is one that allows fast iteration and painless disposal. A well-structured monolith, with a clear separation of responsibilities, is usually more than enough.
When we are building to last
Now, when validation has already happened, users are coming in, and the product starts to gain traction, then yes, we need to think about robustness and scalability. But be careful: this does not mean rewriting everything from scratch applying every architectural pattern you know.
The transition from MVP to a consolidated product should be gradual and guided by real metrics. Which part of the system is struggling with increased load? Where is complexity making maintenance harder? These are the areas that deserve architectural refactoring, not the entire system.
Complexity vs Volume: understanding the nature of the problem
One of the most important decisions is understanding what kind of challenge we are solving. Not every problem is the same, and the architecture needs to reflect that.
High complexity, few users
Imagine a hospital management system used by a single hospital with 50 employees. The business complexity is huge: medical records, scheduling, integrations with equipment, healthcare regulations, multiple access profiles. But the request volume is relatively low.
In this scenario, the architecture needs to deal with domain complexity, not infrastructure scale. Investing in Domain-Driven Design patterns, clear separation of bounded contexts, and solid data modeling makes much more sense than implementing load balancers and auto-scaling.
The common mistake here is focusing on infrastructure when the real challenge is understanding and organizing business rules.
Functional simplicity, high volume
On the other hand, consider a URL shortener API. The business logic is trivial: receive a long URL, generate a unique hash, store it, and return it. But if the service handles millions of requests per day, the architecture needs to be very different.
Here it does make sense to think about aggressive cache usage, CDNs, read-optimized databases, and possible sharding. The complexity lies in scale, not in the domain.
Confusing these two scenarios is one of the main causes of over engineering or under engineering in projects.
Signs of over engineering: when we are going too far
Identifying over engineering is not always obvious, especially when we are excited about a new technology or architectural pattern. Here are some clear signs that we might be exaggerating:
Setup time exceeds development time: If setting up the environment, running containers, configuring observability tools, and CI/CD takes longer than implementing the first feature, something is wrong.
No one understands the entire architecture: If only one person on the team can explain how everything works together, the complexity is probably too high. Good architecture is one that new developers can understand in a few days.
We are solving problems we don’t have: Implementing circuit breakers when we don’t yet have critical integrations, or using event sourcing without complex audit requirements, are classic examples.
The solution is more complex than the problem: If the architecture has more components than the product has features, it’s time to simplify.
Simple changes require changes in many places: When adding a field to a form requires changes across five different layers, the separation of responsibilities has gone too far.
Decision framework: practical questions
When facing architectural decisions, some questions help keep the focus on what really matters:
About the product stage:
- Are we still validating the idea or do we already have product-market fit?
- What is the cost of pivoting or discarding this solution?
- Do we have real users waiting for this functionality?
About volume and scale:
- How many concurrent users do we expect in the next 6 months? And in 2 years?
- What volume of data are we going to process?
- Do we have specific availability requirements or SLAs to meet?
About domain complexity:
- Are business rules complex and frequently changing?
- Are there multiple business contexts that need to be isolated?
- Is business logic the core of the product or just commodity?
About the team:
- What is the team’s seniority level?
- Do we have the capacity to maintain a more complex architecture?
- Will new developers be able to contribute quickly?
About constraints:
- What is the available infrastructure budget?
- Do we have strict delivery deadlines?
- Are there mandatory technical dependencies or integrations?
These questions don’t provide ready-made answers, but they help frame the problem correctly. Architecture should emerge from the answers, not precede the questions.
There is no recipe, only context
The biggest lesson we’ve learned over the years is that there is no perfect architecture that works for every case. There is an appropriate architecture for that specific context, at that specific moment, with those specific constraints.
A well-structured monolith can be more efficient and more maintainable than a poorly planned microservices architecture. A serverless application can be more expensive than a dedicated server depending on usage patterns. Event-driven architecture can add unnecessary complexity if there are no real asynchronous processing requirements.
The key is being honest about the problem we are solving. Starting simple and evolving as needed is almost always better than starting complex and trying to simplify later.
Building software is an exercise in constant trade-offs. Between speed and robustness. Between flexibility and simplicity. Between cost and performance. Architecture is just a tool to navigate these trade-offs consciously.
In the end, the best architectural decision is the one that allows you to deliver value to the user in a sustainable way, considering the real limitations of the project. No more, no less.