Microservices Are Overrated: 10 Important Reasons to Think Twice Before Adopting Them
The hard truths most architects won’t tell you — and when a well-designed monolith is actually the smarter choice.
Hello folks, the title may sound like what?? Everyone is saying to use microservices, and you are saying otherwise? Why? Well, it’s not just me; many people think like that, and there are solid reasons for it, which you will see in this article.
This was also one of the questions that was asked of me last week for a big, reputable bank interview, so knowing when not to use Microservice architecture is as important as knowing when to break your monolith into Microservices.
By the way, if you are preparing for Java developer interviews, then I have shared many resources earlier, which you can also check out to prepare better.
By the way, if you are new to Microservice architecture or just want to revise key Microservice concepts and are looking for resources, then here are a few online courses you can join:
Master Microservices with Spring Boot and Spring Cloud [Udemy]
Building Scalable Java Microservices with Spring Boot [Coursera]
Developing Microservices with Spring Boot [Educative]
Master Microservices with Java, Spring, Docker, Kubernetes [Udemy]
This list includes both video and text-based courses as well as project-based courses for hands-on learning; you can join one or a couple of them to revise Microservices concepts.
10 Reasons Why You Shouldn’t Use Microservices for Everything
Microservices have become one of the most popular software architecture styles over the last decade. Companies like Netflix, Amazon, Uber, and Spotify have shown how powerful they can be when building large-scale systems.
But here’s the catch:
Microservices are not a silver bullet.
Many teams adopt them because they’re trendy, only to realize later that they’ve introduced unnecessary complexity, higher costs, and operational headaches.
Before splitting your application into dozens of services, consider these reasons why a monolith may actually be the better choice.
1. Increased Complexity
Nothing is free in software engineering.
When you move from a monolithic application to microservices, you’re no longer managing a single application—you are managing an entire distributed ecosystem.
Besides your business logic, you now need infrastructure for:
Service Discovery (Eureka, Consul)
API Gateway
Load Balancer
Configuration Management
Distributed Logging
Monitoring
Circuit Breakers
Retry mechanisms
Message Brokers
Service-to-service authentication
For example, imagine an e-commerce application.
In a monolith, placing an order might simply involve calling a Java method:
orderService.placeOrder();In a microservice architecture, the same request may travel through:
Client
↓
API Gateway
↓
Order Service
↓
Inventory Service
↓
Payment Service
↓
Shipping Service
↓
Notification ServiceEach network hop introduces another potential point of failure.
Instead of solving business problems, developers often spend time managing infrastructure.
2. Testing Becomes Much More Difficult
Testing a monolithic application is relatively straightforward.
You can start the application, connect to a database, and run integration tests.
With microservices, every service can have:
Its own database
Independent deployment
Different APIs
Different versions
Now testing becomes much harder.
You need to verify:
Each service individually
Communication between services
API compatibility
Failure scenarios
Network timeouts
Message queues
Contract testing
For example, imagine the Payment Service changes its API.
Suddenly:
Order Service breaks
Billing Service breaks
Refund Service breaks
Even though each service passes its own unit tests.
This is why many organizations invest heavily in:
Contract Testing
Consumer Driven Contracts
End-to-End Tests
Integration Test Environments
Testing quickly becomes one of the biggest engineering investments.
3. Higher Deployment Costs
A monolithic application usually means:
One application
One deployment
One server (or a few replicas)
Microservices multiply everything.
Suppose your system has:
User Service
Order Service
Payment Service
Inventory Service
Email Service
Notification Service
Recommendation Service
Now each service requires:
Containers
CI/CD pipeline
Monitoring
Logging
Scaling
Health checks
Kubernetes deployment
Cloud resources
Instead of deploying one application, you might deploy 40 or even 100 services.
Cloud costs increase quickly.
Even idle services consume CPU, memory, storage, and networking resources.
4. Increased Operational Overhead
Running one service is relatively easy.
Running 100 services is a completely different challenge.
Operations teams must monitor:
CPU usage
Memory usage
Request latency
Error rates
Logs
Certificates
Configuration
Secrets
Autoscaling
Database connections
If one service goes down, it can create a cascading failure across the system.
For example:
Inventory Service fails
↓
Order Service cannot reserve stock
↓
Checkout fails
↓
Payment requests fail
↓
Customers abandon cartsEven though only one service originally failed.
This is why companies need sophisticated monitoring tools like Prometheus, Grafana, Datadog, or New Relic.
