Microservices solve real problems. They also create real problems. Here's when each approach makes sense.
The microservices vs. monolith debate is one of the most polarized in software engineering. Advocates on both sides act like the answer is obvious. It isn't. Both architectures are valid, and the right choice depends on your team size, your product stage, and your operational maturity.
The monolith: underrated
A well-structured monolith is simpler to develop, test, deploy, and debug than a distributed system. One process, one database, one deployment pipeline. For teams under 20 engineers, a monolith is almost always the right starting point. Many successful companies - including Shopify and Stack Overflow - run on monoliths at impressive scale.
When microservices make sense
- Different parts of the system have very different scaling requirements
- Teams are large enough that they block each other working in the same codebase
- Parts of the system need to be deployed independently with different release cycles
- You need polyglot services (some in Python for ML, some in Node for web)
The hidden costs
Microservices add network latency between services, distributed transaction complexity, service discovery overhead, more complex debugging (one request touches 5 services), and 5x the deployment infrastructure. If you're not solving a problem that requires distributed architecture, you're paying these costs for nothing.
Our recommendation
Start with a modular monolith. Keep clear boundaries between domains in your code. When a specific module needs to scale independently or be deployed separately, extract it. This "monolith first" approach gives you the simplicity of a monolith with a clear path to services if you need them.
Ben Arledge
CEO & CTO, CloudOwlNeed help building this?
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