The question arises frequently in engineering discussions: “Should we break up our monolith into microservices?” The answer is often surprising: “Probably not” or “It depends”. This isn’t because microservices are bad—they’re not. It’s because the industry has finally moved past the religious wars where you were either team microservices or team monolith, with no middle ground. The reality in 2025 is far more nuanced.

We’ve All Learned Some Hard Lessons

Remember when every conference talk was about how Netflix or Uber did microservices? Those talks conveniently left out the part about Netflix having hundreds of engineers and a mature platform team that could handle the operational complexity. Meanwhile, startups with five engineers were trying to replicate their architecture and wondering why they were spending more time on Kubernetes YAML than building features.

What’s changed in 2025 is that we now have real data. We’ve watched teams live with their architectural decisions for five, six, seven years. We understand what the actual maintenance burden looks like. We are aware of the hidden costs and the unexpected benefits. Most importantly, we’ve learned that architecture exists on a spectrum, not as a binary choice.

It’s Not Microservices vs Monoliths Anymore

The conversation has evolved beyond the simple monolith versus microservices debate. Today’s engineering teams are considering architectural approaches that range from traditional monoliths to fine-grained microservices, with several intermediate options in between.

At one end, you have the traditional monolith, where everything is in one deployable unit, including a shared database, the whole nine yards. It’s simple and fast to develop, and for many teams, it’s the exact right choice.

Then there’s the modular monolith, where many teams are finding their sweet spot. You get the deployment simplicity of a monolith but with clear internal boundaries. Think of it as a monolith that’s ready to grow up when you need it to.

Some teams are opting for what is sometimes called the “service-oriented monolith”—keeping the core functionality together while extracting specific services where it makes clear business sense. Maybe you pull out authentication because you need it to scale differently, or you extract a reporting service because it has different uptime requirements.

Finally, there is the classic microservices design where everything is broken into smaller, independent services. While this may be the right architecture for some, be aware of the operational overhead cost associated with all the network chatter and data isolation/transactional challenges.

A Framework That Works

The teams that make this decision successfully focus on five dimensions that predict architectural success.

Your Team Is Your Architecture

Conway’s Law isn’t just a clever observation—it’s a prediction. Your architecture will mirror your team structure, whether you plan for it or not, so you might as well plan for it.

If you have a single product team of eight engineers who sit together and share domain knowledge, trying to force them into a microservices architecture is like trying to get all eight of them on one bicycle. You’ll spend more time on coordination overhead than building features.

On the flip side, if you have three autonomous teams of six engineers each, all working on different parts of the business, a shared monolith becomes a coordination nightmare. Every deployment becomes a cross-team effort, every database change needs consensus, and you’re constantly stepping on each other’s toes.

Domain Boundaries Matter More Than You Think

Here’s something that becomes clear after working with distributed systems: the clarity of your domain boundaries is often more important than your technical requirements. If your business processes are tightly coupled, your architecture should be as well.

Experience shows that teams attempt to extract a “user service” and a “payment service,” only to realize that every user operation requires payment information and every payment operation necessitates user context. They ended up with two services that constantly communicated with each other—all the complexity of microservices with none of the benefits.

The best microservices architectures have clean domain boundaries that rarely require communication with each other. The problematic ones have services that are constantly chatting across the network, turning what should be method calls into network requests.

Scale Isn’t Just About Volume

When people talk about scaling, they typically mean handling a larger volume of requests. However, there are different types of scaling problems, and each suggests a distinct architectural solution.

If your entire application scales uniformly—users, orders, and payments all grow together—then scaling the whole thing together with a monolith often makes more sense. You can throw more instances behind a load balancer and consider it done.

But if different parts of your system have fundamentally different scaling characteristics, that’s where microservices start to make sense. Perhaps your user authentication service is accessed on every request, but your report generation service only runs periodically, such as at night.

The Operational Reality Check

This is where many teams often get themselves into trouble. Microservices aren’t just a development choice—they’re a commitment to operational excellence. You’re signing up for distributed systems complexity, and that’s not something you can fake your way through.

Do you have someone who can debug network partitions at 3am? Can your team set up distributed tracing? Do you have monitoring that can tell you which service is slow when your response times spike? If you’re answering “no” to these questions, microservices might not be the right choice yet.

Data Consistency: The Hidden Complexity

One of the most underestimated aspects of the microservices decision is its impact on your data architecture. In a monolith, if you need to update a user’s profile and billing information, you wrap it in a database transaction, and you’re done. Simple, reliable, and atomic.

In a microservices world, that same operation might span a user service and a billing service, each with its own database. Now, you’re dealing with distributed transactions, eventual consistency, and compensation patterns. It’s not impossible, but it’s more complex.

Common Patterns in Practice

Different organizational contexts typically lead to predictable architectural choices, based on patterns observed across the industry.

E-commerce platforms with 10-20 engineers often succeed with modular monoliths when inventory, pricing, and order management need tight coupling for business correctness. The deployment simplicity and transactional consistency make scaling easier than managing distributed state.

Financial services companies with larger engineering teams (50+ engineers) frequently choose microservices due to regulatory requirements that essentially mandate service isolation. Different compliance boundaries naturally align with service boundaries, making autonomous teams a feasible option.

Growing SaaS companies often begin with modular monoliths and gradually extract services as domain boundaries become clearer and teams expand. The strangler fig pattern allows evolutionary architecture without big-bang rewrites.

Making the Right Choice for You

The best architecture is the one that lets your team ship features quickly and reliably. Everything else is implementation details.

If your team is small, your domains are tightly coupled, and you need to move quickly, a well-designed monolith is likely your best choice. You can always extract services later when you have more precise boundaries and more operational maturity.

If you have multiple autonomous teams, clear domain boundaries, and the operational chops to handle distributed systems, microservices can give you capabilities that are hard to achieve any other way.

But remember—there’s a whole spectrum between these extremes, and that’s where many teams find their sweet spot.

The most important thing is to make a deliberate choice, based on your actual constraints and requirements, not on what’s trendy or what worked for another company with entirely different circumstances. Your architecture should enable your team to do their best work, not get in their way.

References

• Richardson, Chris. “Microservices Patterns: With Examples in Java.”
• Newman, Sam. “Building Microservices: Designing Fine-Grained Systems.”
• Fowler, Martin. “MonolithFirst.”
• Fowler, Martin. “Microservice Trade-Offs.“
• Segment Engineering. “Goodbye Microservices: From 100s of problem children to 1 superstar.”
• Shopify Engineering. “Deconstructing the Monolith: Designing Software that Maximizes Developer Productivity.“