As an architect, I’ve seen teams enthusiastically adopt microservices, sold on the dream of “infinite scale” and “team autonomy.” I’ve also seen those same teams a year later, drowning in complexity, wondering why it takes six weeks to add a new feature.

“Microservices Hell” is real, and the rent is high. It’s the state you reach when your plumbing is infinitely more complex than the business logic it’s supposed to support.

Based on my experience, here’s what that journey into hell really looks like.

1. The “Eventual Consistency” Headache (aka The Death of ACID)

The first thing that hits you is the database.

I remember a project where we had a critical flow: Create Order → Update Inventory → Process Payment. In a monolith, this is a single, beautiful database transaction. It’s atomic. It’s safe. It just works.

In microservices, this is now 3 services, probably with 3 separate databases. You can’t have a transaction. You are now forced to write compensating logic (also known as a Saga, but it’s basically “code to undo code”).

This “compensating logic” is a massive source of bugs. You’re now living in the land of “eventual consistency,” which is just a polite way of saying, “Your data is currently wrong, but we’ll probably fix it… eventually.” For any FinTech or HealthTech system, this is a non-starter.

Many teams try to “hack” this by using a shared database. Don’t do that. You’ve just created a monster: a hidden coupling… a single-point-of-failure.

2. The Network Tax (aka “My Function Call is Now a Bug”)

When you trade reliable in-memory function calls for unreliable HTTP calls, you pay a heavy tax. Every developer on your team must now become a distributed systems expert, whether they like it or not.

Every. Single. Call. must handle:

  • Timeouts: What happens when the UserService just… doesn’t answer?
  • Retries: If you retry, was the request idempotent? Congrats, you just charged the customer twice.
  • Circuit Breakers: You must implement these to stop one dead service (InventoryService) from killing every other service that calls it in a “cascading failure”.

Cognitive load skyrockets. And it gets worse when teams get “Service-Mania”. I’ve seen teams of 10 engineers trying to maintain 40 services. A new feature? “Create a new service!”. And now a simple feature requires deploying 3 services at the same time. You’ve just rebuilt your monolith, but over a slow network link.

3. The Observability Tax

This is the part that kills velocity. Remember when you had one log file? Good times.

Now, a single user click might touch 10 different services. When it fails, you’re not debugging; you’re playing a distributed game of Clue.

Before you can even start writing features, you’re forced to pay the “Observability Tax”:

  1. Distributed Tracing (e.g., Jaeger) to follow a request.
  2. Centralized Logging (e.g., ELK Stack) to find the logs.
  3. Metrics Aggregation (e.g., Prometheus) to see what’s on fire.

And this isn’t just a production problem; it destroys your development environments. How can you run 40 services on a developer’s laptop? How do you test E2E (End-to-End) when you can’t even be sure which version of a service is running?

4. The People & Management Hell

But the worst tax isn’t technical. It’s the people tax.

  • An Engineer-to-Service Ratio Gone Wild: I’ve seen teams with 4-5 services per engineer. This isn’t “autonomy”; it’s “burnout.” One person is now the operator, debugger, and on-call for half a dozen systems.
  • “Resume-Driven Development”: When “autonomy” means “anarchy,” you get a service in Kotlin, one in Go, and one in Rust that only one person understands. When that person leaves, you’ve “orphaned” a part of your system.
  • Architecture That Mirrors the Org Chart: Your architecture will inevitably look like your company’s org chart. This is fine… until the reorg. And the company always reorgs. Suddenly, the “Payments” team is split in two, but all the infrastructure, namespaces, and IAM policies are still tangled together. You’ve just signed yourself up for a painful, long-term migration project that delivers zero value to the customer.

So… When is the “Boring” Monolith (Done Right) Just Better?

A well-structured Modular Monolith (decoupled modules in a single codebase) isn’t “legacy.” It’s a pragmatic, often superior, choice. In my experience, the monolith wins hands-down in these cases:

  1. When Transactional Integrity (ACID) is King: If you’re building FinTech, HealthTech, or a complex ERP, your business must be 100% consistent. The simplicity and reliability of a real database transaction are non-negotiable. Don’t trade this for compensating logic.

  2. When You Are an Early-Stage Product (Speed-to-Market is King): Your biggest risk isn’t scale; it’s building the wrong thing. A Modular Monolith lets you move incredibly fast. Refactoring a module inside a monolith is 100x easier than refactoring 10 microservices that you defined incorrectly.

  3. When You Are a Small-to-Medium Team (1-20 Engineers): Microservices are a tool to solve people scaling. If you’re one team, microservices will kill your velocity with meetings about API contracts. A monolith lets your team just… code.

  4. When You Don’t Have a Dedicated Platform Team: Choosing microservices without a dedicated SRE/Platform team is like buying a Formula 1 car to go grocery shopping. It’s expensive, incredibly hard to drive, and you’re going to crash.

My Parting Advice

Start with a Modular Monolith.

Design it with clean boundaries, communicate between modules via interfaces, and do not share database tables between modules. This gives you 90% of the benefits of microservices (decoupling) with 10% of the operational cost.

Only extract a module into its own microservice when you have a clear, painful, and obvious reason (like asymmetric scaling needs or a new tech stack). Microservices are a refactoring step, not a starting point.