How breaking your system into services can increase complexity and slow everything down

In the previous part, we discussed how to design systems that survive under pressure.

Microservices are often seen as a solution to scaling and reliability.

But in practice, many systems become slower and harder to manage after moving to microservices.

Too many network calls

In a monolithic system, components communicate in memory.

In microservices, communication happens over the network.

Every request between services adds:

• network latency
• serialization and deserialization cost
• additional failure points

A single user request may trigger multiple internal calls.

This increases total response time.

What was once a fast internal function call becomes a slower network operation.

Chatty services problem

Microservices often become too dependent on each other.

Instead of one efficient call, services make many small calls.

For example:

• service A calls service B
• service B calls service C
• service C returns partial data

This creates a chain of requests.

This pattern is known as chatty services.

It is one of the most common causes of slow systems.

Distributed failures

In a distributed system, failures spread easily.

If one service becomes slow or unavailable:

• dependent services are affected
• requests start timing out
• retries increase traffic

This can lead to cascading failures across the system.

Unlike monoliths, where failure is contained, microservices increase the surface area of failure.

Harder debugging

Debugging performance issues becomes more complex.

In a single system, it is easier to trace a request.

In microservices:

• requests pass through multiple services
• logs are spread across systems
• latency is distributed

Finding the root cause requires tracing across multiple components.

Without proper observability, diagnosing issues becomes difficult.

Data consistency challenges

Microservices often manage separate databases.

This improves independence but creates consistency challenges.

• data may not be updated at the same time
• systems may temporarily disagree
• additional logic is required to handle this

Managing consistency adds complexity and can impact performance.

Overengineering too early

Microservices are often adopted too early.

For small systems, they introduce:

• more services to manage
• more deployment complexity
• more communication overhead

Before scaling becomes a real problem, this added complexity slows development and performance.

A simple system becomes unnecessarily complicated.

Conclusion

Microservices are powerful, but they are not a default solution.

They introduce network overhead, increase system complexity, and make failures harder to manage.

Choosing the right architecture depends on the problem, not the trend.

In the next part, we will look at synchronous systems and how waiting on responses can slow down your backend.

Thanks for reading.