For years, microservices were treated as the “correct” architecture.
Today, many senior engineers are quietly moving back to monoliths.
Not messy monoliths.
Modular monoliths.
Because most teams adopted microservices too early.
The Microservices Dream
The promise sounded perfect:
- Independent deployments
- Team autonomy
- Infinite scalability
But in reality, most companies got:
- Network latency
- Debugging nightmares
- Operational overhead
- Complex local development
Microservices solved scaling problems that many startups never actually had.
The Hidden Complexity Explosion
Every microservice adds:
- CI/CD pipelines
- Monitoring
- Logging
- Authentication between services
- Version compatibility issues
Instead of building features, teams started maintaining infrastructure.
Engineering time shifted from product → plumbing.
The Distributed Monolith Problem
Ironically, many microservice architectures became:
A monolith… but over the network.
Services tightly depended on each other, so deployments weren’t truly independent.
Now failures were harder to trace because they crossed service boundaries.
What Is a Modular Monolith?
A modular monolith keeps:
- One deployable application
- Clear internal boundaries
- Separate modules
- Strict code ownership
But avoids:
- Network calls between components
- Complex orchestration
- Service-to-service auth overhead
You still get structure — without distributed system pain.
Why Teams Are Switching Back
Modern companies realized:
Scaling engineering complexity before product-market fit is expensive.
A modular monolith gives:
- Faster development
- Easier debugging
- Lower infra cost
- Simpler onboarding
Microservices are powerful — but only when scale truly requires them.
When Microservices Actually Make Sense
Use them if you have:
- Multiple independent teams
- High traffic at global scale
- Clear domain boundaries
- Mature DevOps platform
Otherwise, they slow you down.
Final Thought
Architecture decisions should follow scale.
Not trends.
Premature microservices are the new premature optimization.