Hot take: You don’t have a microservice architecture, you have a distributed monolith with trust issues.

In the rush to “go micro,” many teams end up slicing their systems into tens of tiny, chatty services that spend more time talking to each other than doing any real work. Every API call adds latency. Every dependency adds failure points. Every “independent” deployment ends up blocked by another team’s version bump.

Sound familiar? 🙂

The pain you’re feeling isn’t the cost of scale, it’s the cost of premature, arbitrary decomposition.

The Microservices Trap

How we got here: It starts innocently enough. You read about Netflix’s architecture. You attended some random conference, you read some articles online. Someone mentions “Conway’s Law” in a late Friday meeting. Suddenly, the mandate comes down: “We’re going microservices.”

Within six months, you have:

• A user service
• An auth service
• A notification service
• An email service (because notifications and emails are totally different domains)
• A logging service
• A metrics service
• A service that just... creates uuids?

Each one has its own:

• Repository
• CI/CD pipeline
• Database
• Deployment schedule
• API versioning scheme
• Team ownership

The reality check: To fetch a user’s profile, you now make 7 API calls across 4 services. Your p99 latency is 800ms. Your error budget is constantly exceeded because something is always down. Your observability costs more than your compute. You’ve achieved distributed monolith status.

The Hidden Costs Nobody Talks About

1. Network is not free

Monolith: function call = 0.001ms Microservice: HTTP call = 5-50ms (plus serialization, auth, retries...)

When your checkout flow hits 12 services, that’s 60-600ms of network overhead before you’ve done any real work.

And that’s assuming everything works. Add retries, circuit breakers, and cascading failures, and you’re looking at seconds, not milliseconds.

2. Distributed debugging is a nightmare

Bug report: “User can’t complete checkout.”

In a monolith:

• Check the logs
• Set a breakpoint
• Find the issue
• Fix it
• Deploy

In microservices:

• Which service failed?
• Check distributed traces (if they exist)
• Correlate logs across 6 services
• Find the issue is a timeout in service D caused by a memory leak in service B triggered by bad data from service A
• Coordinate deployments across 3 teams
• Hope you didn’t introduce new bugs

3. “Independent” deployments aren’t independent

Your user-service runs on SQLAlchemy 1.4. The payments team just upgraded their shared models package to SQLAlchemy 2.0 for “better async support.” Now your queries throw deprecation warnings everywhere and half your tests fail.

When Microservices Actually Make Sense

Don’t get me wrong, microservices can be the right choice. But they’re an optimization for specific problems, not a default architecture pattern.

When done right, microservices unlock real organizational power.

They let large teams ship features independently, scale bottlenecks in isolation, and mix technologies to fit different workloads. You can deploy a single service without freezing the entire platform. You can experiment faster, fail safely, and iterate without merge conflicts across 50 engineers.

For truly global-scale systems, think payments, logistics, or media streaming, microservices let you scale the right parts independently. Instead of scaling the whole app just because one endpoint gets hammered, you scale that service and keep costs predictable.

They also make it easier to enforce clear domain ownership. Each team owns their service, their schema, and their roadmap, which reduces cross-team dependency chaos when you’re big enough to need it.

Good reasons to split services:

1. Genuine scale differences

Example: Your image processing pipeline handles 10K requests/sec
Your admin panel handles 10 requests/sec

These shouldn’t share resources. Split them.

2. Team autonomy at real scale If you have 50+ engineers stepping on each other’s toes in the same codebase, and you’ve already tried modularization, then consider splitting.

3. Technology constraints You need Python’s ML libraries for recommendations but Go’s performance for your API gateway. Fair enough.

4. Actual domain boundaries Payments and product catalogs are genuinely different domains with different business rules, compliance requirements, and failure modes. They can evolve independently.

The Monolith Advantage (That Nobody Admits)

A well-structured monolith gives you:

Simplicity:

• One codebase to understand
• One deployment pipeline
• One database transaction (ACID guarantees for free!)
• One place to search for code
• One set of dependencies to manage

Performance:

• In memory function calls, not HTTP
• No serialization overhead
• No network failures
• Shared caches actually work

Developer experience:

• Run the entire app locally
• Debugger actually works
• Tests run fast
• Refactoring is safe

“But monoliths don’t scale!”

Wrong. Shopify runs on a Rails monolith and handles Black Friday traffic. GitHub’s monolith serves millions of developers. Stack Overflow famously runs on a handful of servers.

You scale a monolith by:

1. Vertical scaling (modern instances are HUGE)
2. Horizontal scaling (stateless apps scale fine)
3. Strategic caching
4. Database optimization

The Middle Path (What You Should Actually Do)

Here’s the nuance nobody talks about: You don’t choose between monolith and microservices. You choose when to split.

Start with a modular monolith: This isn’t just about folders, it’s about Bounded Contexts.

app/
├── modules/
│   ├── users/
│   │   ├── domain/
│   │   ├── api/
│   │   └── repository/
│   ├── payments/
│   │   └── ...
│   └── inventory/
│       └── ...

Good modules have:

• Clear interfaces (defined contracts between modules)
• Weak coupling (changes in one don’t ripple to others)
• Strong cohesion (related logic lives together)

When to extract a service:

You have data that justifies the split:

• This module is causing 80% of deploys
• This team is blocked waiting for other teams
• This component needs different scaling characteristics
• This domain has genuinely independent lifecycle

The extraction looks like:

Monolith → Modular Monolith → 3 well-defined services → Scale what needs it

Not:

Monolith → 47 microservices → ??? → Black Magic

Red Flags You’ve Gone Too Micro 🚩

You might have a problem if:

Your services call each other in chains If request flow looks like: A → B → C → D → B → E, you’ve just built a distributed ball of mud.

You can’t add a feature without touching 5+ services That’s not independence, that’s tight coupling with extra steps.

Your team spends more time on infrastructure than features Kubernetes, service mesh, distributed tracing... these are costs, not features.

Simple changes require “cross-team coordination meetings” You’ve replaced code dependencies with human dependencies. That’s slower.

Your error messages look like: "Service timeout in payment-gateway calling order-validator calling inventory-checker calling warehouse api"

Wrapping Up

The truth is: microservices aren’t a magic scalability pill, they’re an organizational tool. If your team isn’t struggling with coordination or monolith scaling yet, breaking things apart just creates complexity without benefit. The real skill isn’t in cutting your system into tiny pieces, it’s knowing where to draw the lines. Strong service boundaries come from domain understanding, not arbitrary code size.

So before you spin up service number 47, ask yourself:

“Is this solving a scaling problem, or just creating a communication problem?”

Sometimes the best architecture decision is the one you don’t make.

– What’s your take? Are you running a microservices architecture or a distributed monolith? Let me know in the comments, I’d love to hear your war stories.