Microservices Killed Our Startup. Monoliths Would’ve Saved Us

We raised $2.5M in seed funding.

Had a working product. Paying customers. Revenue growing 40% month-over-month.

Then our lead architect walked in with a proposal: “We need to move to microservices.”

He had slides. Diagrams. References to Netflix and Uber. The whole nine yards.

“Scalability,” he said. “Team autonomy. Independent deployments. This is how modern companies build software.”

The investors loved it. The board loved it. Hell, even I loved it.

Six months later, we were out of money, our product was broken, and half the team had quit.

Microservices didn’t scale our startup. They killed it.

I ended up collecting these incidents into a single playbook so teams stop repeating the same mistakes.

I’ve seen too many backend systems fail for the same reasons — and too many teams learn the hard way.

So I turned those incidents into a practical field manual:
real failures, root causes, fixes, and prevention systems.

No theory. No fluff. Just production.

👉 The Backend Failure Playbook — How real systems break and how to fix them:

Firstly you can look and get detail:

👉 Production Failure Playbook — 50 Real Incidents That Cost Companies $10K–$1M

How We Got Here (The Setup)

Let me paint you a picture of what we had before the great “modernization.”

Our Product: B2B SaaS for inventory management. Nothing fancy, but it worked.

The Stack:

• Single Rails monolith (yeah, I know, “outdated”)
• PostgreSQL database
• Redis for caching
• Deployed on Heroku (literally one-click deploys)
• 4 backend engineers, 2 frontend

The Performance:

• 50,000 daily active users
• Response times under 200ms
• 99.7% uptime
• Zero scaling issues

The Team Velocity:

• Ship new features every week
• Bug fixes deployed in hours
• Onboarding new devs took 2 days

Everything was working. That should’ve been our first warning sign.

Because in startup culture, “working” is never enough. You have to be “scaling.” You have to be “modern.” You have to use the same tech stack as companies 1000x your size.

The Microservices Pitch (That Sounded Amazing)

Our architect’s proposal was seductive. I mean, really seductive.

“Right now,” he explained, pointing at our monolith like it was a dead rat, “everything is coupled. One bug can bring down the entire system.”

True. We’d had a couple incidents where a memory leak in one feature affected everything.

“With microservices, we’ll have:

• User Service
• Inventory Service
• Order Service
• Notification Service
• Analytics Service
• Billing Service”

Each service would:

• Have its own database (because coupling is bad, apparently)
• Deploy independently (ship faster!)
• Scale independently (handle millions of users!)
• Be owned by specific teams (clear ownership!)

“Netflix does this,” he said. “Uber does this. Amazon does this.”

And there it was. The magic words that make founders lose all common sense.

“If we want to scale like them, we need to build like them.”

Nobody asked the obvious question: Are we Netflix? Do we have 500 engineers and dedicated DevOps teams?

Nope. We had 4 backend developers and a DevOps guy who was already stretched thin.

But sure, let’s rebuild our entire architecture because Netflix does it.

Before we ever deploy to production, we run through a brutal checklist — and when things break, we follow a structured incident process.
I made both public:

– Pre-Production Checklist + Incident Response Template (free):
👉

– Production Failures Playbook — 30 real incidents with timelines, root causes, and fixes:
👉

Use them if you want to avoid learning these lessons the expensive way.

Month 1: The Honeymoon Phase

First month was actually… fine?

We started with the “easy” service — notifications. Email and SMS alerts. Pretty isolated functionality.

Took us 3 weeks to extract it. Set up the new repo, configure CI/CD, write the API contracts, migrate the database tables.

It worked. We deployed it. It sent notifications.

“See?” our architect said, triumphantly. “This is the future.”

I should’ve noticed the warning signs:

• Our deploy time went from 5 minutes to 30 minutes (multiple services now)
• We had 2 repos instead of 1 (more context switching)
• Our staging environment needed 2 databases instead of 1
• Integration tests became way more complex

But we were riding high on “microservices energy.” We’d read all the blog posts. We knew the patterns. What could go wrong?

Month 2–3: Cracks Start Showing

We split off the Billing Service next. This is where things got interesting.

Billing needed to:

• Read user data (User Service)
• Check inventory status (Inventory Service)
• Create orders (Order Service)
• Send notifications (Notification Service)

So now Billing Service had to make HTTP calls to four other services.

What we expected:

• Clean separation of concerns
• Independent scaling
• Team autonomy

What we got:

• Network latency between services (suddenly 50ms turned into 250ms)
• Cascading failures (one service down = everything breaks)
• Distributed transactions (how do you rollback across 5 databases?)
• Debugging hell (logs scattered across multiple services)

Our response times went from 200ms to 800ms. Just from network overhead.

“It’s fine,” our architect assured us. “We just need to implement caching. And maybe a message queue. And circuit breakers. And…”

The technical debt was piling up faster than we could ship features.

Month 4: The Coordination Nightmare

Remember how microservices were supposed to give us “independent deployments”?

Yeah, that was a lie.

Turns out, when your services are all calling each other, you can’t deploy them independently. You need coordination.

Example: We wanted to add a new field to the User model.

In the monolith days:

• Add column to database
• Update the model
• Update the views
• Deploy
• Total time: 2 hours

In microservices world:

• Update User Service database
• Update User Service API
• Deploy User Service (carefully, so nothing breaks)
• Update Billing Service to use new field
• Update Order Service to use new field
• Update Inventory Service to use new field
• Update all API contracts
• Write migration scripts for all databases
• Deploy everything in the correct order
• Hope nothing breaks
• Total time: 3 days

We went from shipping features weekly to spending weeks coordinating deployments.

Team velocity? Destroyed.

Month 5: The Database Disaster

Here’s something nobody tells you about microservices: distributed data is hard.

Like, really hard.

In our monolith, database transactions just worked. Everything was in one database. ACID guarantees. Rollbacks. Foreign keys. All the good stuff.

With microservices and separate databases? Welcome to hell.

The Scenario: User places an order.

What needs to happen:

1. Check inventory (Inventory Service)
2. Reserve inventory (Inventory Service)
3. Charge payment (Billing Service)
4. Create order (Order Service)
5. Send confirmation (Notification Service)

What happens if step 3 fails? Payment declined.

Now you need to:

• Release the inventory reservation
• Don’t create the order
• Send a different notification

Sounds simple? In a monolith, you just wrap it in a transaction and rollback on error.

In microservices? You need:

• Saga pattern (complex orchestration)
• Compensating transactions (write code to undo things)
• Idempotency (so retries don’t double-charge)
• Event sourcing (maybe?)
• Two-phase commit (please no)

We spent a month implementing the Saga pattern. It had bugs. Orders got lost. Inventory got double-reserved. Customers got charged twice.

Our support ticket volume tripled.

Month 6: The Team Breakdown

By month 6, the team was falling apart.

Developer Experience:

“In the monolith days, I could:

• Clone one repo
• Run one command
• Have the entire app running locally
• Make changes and see results immediately”

“Now I need to:

• Clone 6 repos
• Set up 6 databases locally
• Configure service discovery
• Run Docker Compose with 6 containers
• Wait 10 minutes for everything to start
• Debug why Services aren’t talking to each other
• Give up and just test in staging”

Onboarding new developers went from 2 days to 2 weeks. And they still didn’t understand the system.

Debugging Production Issues:

Monolith debugging:

• Check logs (one place)
• Find the error
• Fix it
• Deploy
• Done

Microservices debugging:

• Which service is failing? (check 6 different log streams)
• Is it a network issue? (check service mesh)
• Is it a database issue? (check 6 databases)
• Is it a message queue issue? (check RabbitMQ)
• Is it a race condition? (good luck)
• Find the error after 3 hours
• Fix it
• Coordinate deployment with 3 other teams
• Hope it works
• It doesn’t work
• Cry

Feature Development:

Simple feature: Add a “preferred payment method” field.

Monolith: 4 hours of work.

Microservices:

• Update User Service (2 hours)
• Update Billing Service (2 hours)
• Update API contracts (1 hour)
• Update frontend to call both services (2 hours)
• Write integration tests (3 hours)
• Coordinate deployment (4 hours)
• Debug why it’s not working (6 hours)
• Total: 20 hours for a 4-hour feature

Our velocity was in the toilet. We were shipping 1/5th of the features we used to.

The Breaking Point

Three things happened in the same week:

Monday: A cascading failure took down the entire platform for 4 hours. One service had a memory leak, which caused it to slow down, which caused other services to time out, which caused retries, which brought everything down. In the monolith days, we would’ve just restarted one app. Now we had to debug a distributed system failure.

Wednesday: Our biggest customer (25% of revenue) complained about slow response times and threatened to leave. We’d gone from 200ms response times to 2 second response times in some cases. Network hops between services were killing us.

Friday: Our head of engineering quit. In his exit interview: “I didn’t sign up to be a DevOps engineer. I signed up to build product. We’re spending all our time managing infrastructure instead of shipping features.”

That weekend, I looked at our metrics:

Before Microservices (6 months ago):

• Features shipped per month: 20–25
• Mean response time: 180ms
• Monthly uptime: 99.7%
• Infrastructure costs: $3,000/month
• Team happiness: High

After Microservices (now):

• Features shipped per month: 4–6
• Mean response time: 1,200ms
• Monthly uptime: 97.2%
• Infrastructure costs: $12,000/month (more servers, more databases, more everything)
• Team happiness: People were quitting

We’d spent 6 months making everything worse. And we were running out of money.

The Uncomfortable Conversation

I called an emergency meeting with the architect who’d pushed for this.

“The microservices aren’t working,” I said.

He looked offended. “They’re working fine. We just need to invest more in:

• Better monitoring (more tools, more cost)
• Service mesh (Istio or Linkerd)
• Better CI/CD (more DevOps engineers)
• Event-driven architecture (rebuild everything again)
• More documentation
• More training”

“So your solution to microservices being too complex… is to add more complexity?”

“It’s not complex if you do it right. Netflix — “

“WE’RE NOT NETFLIX!” I finally snapped. “Netflix has 500 engineers. We have 4. Netflix has dedicated DevOps teams. We have one guy. Netflix has millions of users. We have 50,000.”

Silence.

“We’re spending all our time managing infrastructure instead of building features. Our customers are leaving because the product is slow and buggy. We’re bleeding money. And your solution is to add more tools?”

He didn’t have an answer.

The Decision: Back to the Monolith

We had two choices:

Option 1: Double down. Hire more DevOps engineers. Invest in more tools. Spend another 6 months fixing our microservices architecture.

Option 2: Admit defeat. Go back to the monolith. Save what’s left of the company.

I chose Option 2.

The architect quit. Called me “technologically backwards.” Said we’d “never scale.”

I didn’t care anymore. I cared about survival.

The Migration Back (Or: How We Unfucked Ourselves)

We spent 8 weeks merging everything back into the monolith.

Week 1–2: Merged the Notification Service back. Pretty easy since it was isolated.

Week 3–4: Merged the Billing Service. Harder. Had to consolidate databases.

Week 5–6: Merged the Order Service and Inventory Service. Lots of duplicate code to clean up.

Week 7–8: Testing everything. Making sure nothing broke.

The Results (After Migration):

Features shipped per month: Back to 20+ (we were building again, not managing infrastructure)

Mean response time: 220ms (even better than before with some optimizations)

Monthly uptime: 99.8% (more stable than microservices ever were)

Infrastructure costs: $4,500/month (still higher than before because we’d grown, but way lower than microservices)

Team happiness: People stopped quitting

We lost 6 months. We lost some good engineers. We burned through money we didn’t have.

But we survived.

One last thing.

I’m actively talking to teams who are dealing with problems like:

• services slowly eating memory until they crash
• rising cloud costs nobody understands anymore
• incidents that feel “random” but keep repeating
• systems that only one or two people truly understand

If any of this sounds like your team, I’d genuinely love to hear what you’re dealing with.

I’m not selling anything here — I’m trying to understand where teams are struggling most so I can build better tools and practices around it.

You can reach me here:

📩 ozcaydevrim3@gmail.com
🔗 https://www.linkedin.com/in/devrimozcay/

What We Actually Learned (Not Theory)

Lesson 1: Microservices Are an Optimization for Organizational Problems, Not Technical Ones

You need microservices when:

• You have 50+ engineers stepping on each other
• You need truly independent team deployment
• Different services have wildly different scaling needs
• You have the infrastructure team to support it

You don’t need microservices when:

• You have <20 engineers
• Your monolith isn’t actually slow
• You’re optimizing for development velocity
• You can’t afford the operational overhead

Lesson 2: Netflix/Uber/Amazon Are Not Your Role Models

These companies didn’t start with microservices. They evolved to them after hitting massive scale with huge engineering teams.

Netflix has 1000+ engineers. You have 5. Stop comparing.

Lesson 3: Complexity Is a Tax You Pay Every Day

Every new service is:

• Another repo to maintain
• Another database to backup
• Another thing that can fail
• Another integration to test
• Another service to monitor

That tax compounds. Fast.

Lesson 4: Network Calls Are Never Free

Each service boundary = network call = latency + failure point

In-process function call: nanoseconds, never fails

HTTP call between services: milliseconds, can timeout/fail

That adds up quick.

Lesson 5: The Monolith Isn’t Your Enemy

A well-structured monolith can:

• Scale to millions of users (Shopify, GitHub, Stack Overflow prove this)
• Deploy quickly (we had 5-minute deploys)
• Be easy to debug (one codebase, one log stream)
• Keep team velocity high

The enemy isn’t the monolith. The enemy is bad architecture. And you can have bad architecture in both monoliths and microservices.

🔧 Tools that saved me from production hell

After a few years of building and breaking real systems, I noticed a pattern:

I kept losing time (and sometimes sleep) to the same problems:
bad test data, silent failures, broken automations, fragile deployments, and debugging in the dark.

So I stopped rewriting the same fixes over and over again — and turned them into small tools and runbooks I actually use.

If you’re dealing with any of these pains, this might save you some time:

❌ “Our tests keep failing because the data is broken.”

Relational Test Data Generator (TDG)
Generate realistic, relationally consistent test data — without touching production or breaking foreign keys.
👉

❌ “We only discover problems when production is already on fire.”

Production Engineering Toolkit — Real Production Failures
A collection of real incidents, failure patterns, and how to avoid them before they hurt.
👉

❌ “Our internal automations are fragile, unmaintainable, and always break.”

Selenium Automation Starter Kit (Python)
A clean, extensible base for building internal tools, scrapers, and test automations that don’t rot after a week.
👉

❌ “We’re starting a mobile product but don’t want to waste weeks on boilerplate.”

Expo Habit App Boilerplate — Production Ready
A ready-to-ship mobile foundation for habit, health, and tracking apps. No setup hell.
👉

❌ “Nobody taught me what actually matters in production.”

Production Engineering Cheatsheet
The fundamentals nobody tells you until things break at 2 AM.
👉

❌ “Spring broke again and I don’t know where to look.”

Spring Boot Troubleshooting — When Things Break in Production
A battle-tested debugging guide for Spring systems based on real failures, not docs.
👉

❌ “I know Python, but not how to run it in production.”

Python for Production — Cheatsheet
The parts of Python that actually matter when systems run 24/7.
👉

❌ “I want to ship an AI product without drowning in overengineering.”

Ship an AI SaaS MVP — The No-BS Checklist
A practical checklist to ship an AI MVP fast, without building a science project.
👉

❌ “I want a real starting point, not a demo repo.”

AI SaaS Starter Kit (Next.js + OpenAI)
A clean foundation for spinning up AI-powered products quickly.
👉

❌ “Our backend setup always takes longer than expected.”

Spring Boot Microservices Starter Kit v2
A production-ready backend stack you can run locally in under 30 minutes.
👉

❌ “Our frontend is always a mess at the start.”

Next.js SaaS Starter Template
A minimal, clean frontend foundation for SaaS products.
👉

❌ “I’m preparing for interviews but don’t want trivia.”

Cracking the AWS & DevOps Interview
Real questions, real answers — no filler.
👉

❌ “Java interviews still scare me.”

Top 85 Java Interview Questions & Answers
Curated questions that actually show up in real interviews.
👉

I’m not selling motivation or dreams — just the tools I built because I was tired of solving the same problems over and over again.

If one of these saves you even a few hours, it already paid for itself.

When Microservices Actually Make Sense

I’m not saying microservices are always wrong. But they’re wrong for most startups most of the time.

Consider microservices if:

You have separate teams that really need autonomy

• 50+ engineers on the same codebase
• Genuine organizational need for independent deploys
• Different services with very different scaling characteristics (e.g., video processing vs. API server)

You have the infrastructure to support it:

• Dedicated DevOps team
• Service mesh expertise
• Monitoring and observability built out
• Budget for increased infrastructure costs

You’ve actually hit limits with your monolith:

• Build times are in hours
• Deploy times are unacceptable
• Can’t scale specific bottlenecks independently

Don’t do microservices if:

You’re “preparing for scale” — You don’t need it yet

• “Netflix does it” — Netflix is not you
• Your architect read a blog post — Blog posts lie
• You think it’ll make development faster — It won’t
• You have <20 engineers — Too small

The Resources That Actually Helped Us Recover

When we were rebuilding, a few resources genuinely saved us from making the same mistakes again.

The Spring Boot Production Checklist (free) helped us remember all the production concerns we’d forgotten while chasing architecture astronaut dreams. It’s surprisingly easy to forget the basics when you’re busy being “modern.”

For the Java/Spring stack we ended up back on, Grokking the Spring Boot Interview and the Spring Boot Troubleshooting Cheatsheet became our bible for 3 AM production issues. When you’re trying to stabilize a system that’s been torn apart and stitched back together, having practical debugging guides matters.

The Top 85 Java Interview Questions actually helped us interview replacements for the team members we lost during the microservices disaster.

And honestly? The Production Engineering Cheatsheet taught us more about what actually matters in production than any microservices conference talk ever did.

I write about what actually breaks in production.
No fluff. No tutorials. Just real engineering.

👉 Free here:

The Awkward Truth Nobody Wants to Hear

Most companies using microservices shouldn’t be.

They’re doing it because:

• It’s trendy
• Big tech companies do it
• Their architect wants it on their resume
• They think they’ll need it “eventually”
• They read too many blog posts

Meanwhile they have 10 engineers and 1000 users.

It’s architecture cosplay. Dressing up like Netflix without understanding why Netflix made those choices.

And it’s killing startups.

What I’d Tell My Past Self

If I could go back to that meeting where we decided to adopt microservices:

“Your monolith is fine. Optimize it. Add caching. Fix the slow queries. Improve the deploy process. But don’t tear it apart.

You don’t have Netflix’s problems. Stop trying to implement Netflix’s solutions.

Focus on shipping features. Focus on making customers happy. Focus on growing revenue.

Architecture is a tool, not a goal. And right now, the simplest architecture is the right architecture.

When you actually hit the limitations of a monolith — and you’ll know when you do — then consider alternatives. But not before.”

I wouldn’t have listened. Nobody ever listens. You have to learn this lesson the hard way.

But maybe you’ll be smarter than I was.

The Current State (12 Months Later)

We’re still alive. Barely.

We’re back on a monolith. We’re shipping features again. Response times are good. Infrastructure costs are manageable.

We lost 6 months and about $800K in runway. Three good engineers quit. Our growth stalled.

But we survived.

And ironically? Now that we’re back on a monolith and shipping fast again, we’ve started growing again. Customers are happier. The team is happier.

The investors who loved the microservices idea? They’re happy too. Because we’re not dead.

Turns out staying in business matters more than having a “modern” architecture.

Who knew?

Tools I Wish I Had During This Mess

Look, when you’re rebuilding a system from scratch (or unfucking a microservices nightmare), you need real tools, not just theory.

I spent years rebuilding the same patterns. So I finally cleaned them up into actual starter kits.

If you’re building production systems (not demos), the Backend to SaaS Bundle has everything together — backend, frontend, AI. It’s the stack I wish we’d started with.

For mobile projects, the Expo Habit App Boilerplate is production-ready with offline support and localization — all the stuff you forget until production.

And if you’re dealing with test data nightmares (we had plenty of those), the Relational Test Data Generator generates realistic, consistent data without touching production. Would’ve saved us weeks during migration.

I’m not selling dreams — just tools I built because I kept re-doing the same painful work.

Final Thoughts

Microservices are not inherently bad. They solve real problems at real scale.

But they also create real problems. And for most startups, those problems outweigh the benefits.

Start with a monolith. Build it well. Scale it vertically. Optimize it. Cache aggressively. Use read replicas. Add load balancers.

You can get really far with a well-built monolith. Shopify processes billions in transactions on a Rails monolith. GitHub serves millions of developers on a Rails monolith. Stack Overflow handles millions of daily users on a .NET monolith.

You probably don’t have more scale than them. So why do you need microservices?

The best architecture is the one that lets you ship features and make money. Everything else is just ego.

Your turn: Have you made a similar architecture mistake? Jumped on a trend that wasn’t right for your scale? Let me know in the comments.

And if you’re currently considering microservices for your 5-person startup… maybe reconsider. Future you will thank present you.

Now if you’ll excuse me, I have features to ship. In my beautiful, boring, working monolith.

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