Why You Should Avoid Microservices (Unless You Are Actually Netflix)
In the world of software architecture, few buzzwords have held as much sway over the last decade as "microservices." It became the industry standard aspiration. The narrative was clear: monoliths are dinosaurs, clunky, slow, and hard to scale, while microservices are agile, modern, and future-proof.
Read any engineering blog from a FAANG company, and the blueprint for success seemed obvious: break everything apart.
But recently, a counter-narrative has emerged, born from the trenches of practical engineering experience. It turns out that adopting the architecture of a tech giant when you don’t have giant problems doesn't make you faster. It often does the exact opposite.
Proprietary cloud vendors and massive tech conglomerates love selling you on complexity because it requires more resources, more managed services, and expensive consultancy hours. Luckily, a pragmatic approach exists: the Modular Monolith. You can set this up on your own, manage it easily, and avoid the crippling "microservice tax" imposed on small teams.
A Note of Caution: The Risks of Premature Distribution
Before you rush to dockerize every function in your codebase, a note of caution: you have no idea how much complexity distributed systems introduce until you are debugging a latency spike across 14 different services.
To secure your project's future and your own sanity, consider these risks:
- Network Latency: Local function calls are nanoseconds; RPC calls over the network are milliseconds. This adds up.
- Debugging Nightmares: You will need distributed tracing (Jaeger/Zipkin) just to follow a simple request.
- Data Integrity: Maintaining consistency across 8 different databases without distributed transactions (Sagas/2PC) is incredibly difficult.
- DevOps Overhead: Managing CI/CD pipelines for 14 repos is not the same as managing 1.
1. The Microservice Promise vs. Reality
Overview Microservice architecture structures an application as a collection of loosely coupled, independently deployable services. Ideally, you have a "Payment Service" or "Inventory Service" that scales independently.
Under the Hood The theoretical stack usually involves polyglot programming—perhaps Node.js for I/O heavy tasks and Python for data science—communicating via gRPC or REST. The promise is "Fault Isolation," where if the recommendation engine crashes, the site stays up.
The "Netflix" Reality It sounds perfect. But as many engineering teams have discovered, these benefits come with a massive price tag. Gabriele Ferreri, a Senior Full-Stack Engineer, recently shared a reality check. His company, with only 50,000 users, adopted a Netflix-style architecture (designed for 250 million users).
The result was not agility; it was friction.
- Before (Monolith): 1 repo, 1 deployment, 1 database. Feature shipping time: 2 days.
- After (Microservices): 14 repos, 14 deployments, 8 databases. Feature shipping time: 2 weeks.
2. The Bottleneck: Why You Slow Down
Overview Ferreri detailed exactly why they experienced a 7x slowdown. The architecture meant that simple tasks became complex ordeals.
Under the Hood Local development became a nightmare. You might find yourself running dozens of Docker containers that hog your RAM and take 20 minutes just to boot up.
Features & "Integrations"
- Distributed Detective Work: Debugging meant tracing requests across 14 different logs.
- Coupling in Disguise: A minor change, like adding a field to a user object, required synchronized updates across multiple services, the API gateway, and consumers.
- Deployment Anxiety: Instead of one atomic deploy, you are running 14 pipelines and "praying nothing breaks."
3. The Solution: The Modular Monolith
Overview This is the open-source, pragmatic alternative that Ferreri’s team eventually adopted to return to their original shipping speed. It is a single codebase with clear module boundaries.
Under the Hood You keep logical separation—modules like /users/, /orders/, and /payments/—and you can even enforce separate database schemas per module to prevent spaghetti code. However, it is compiled and deployed as a single service.
Why Developers Prefer It This architecture provides the organizational benefits of defined boundaries without the operational crippling of distributed infrastructure. As Ferreri wisely points out, you can always extract a module into a microservice later, if and when the need actually arises (e.g., passing the "Netflix scale" threshold).
Starting with distributed systems means you pay the "microservice tax" upfront, often before you have any revenue or user traction to pay for it.
Conclusion
Architecture must serve the business needs of today, not the hypothetical needs of a future ten years from now. When a small team tries to adopt the complex, distributed workflows designed for thousands of engineers, productivity grinds to a halt.
Trying to parallelize work by arbitrarily splitting a cohesive application into dozens of pieces rarely speeds things up. In fact, it usually introduces communication overhead and integration nightmares that delay delivery.
Year ago, I consulted for a customer's company project, I found the developer is insisting on using Microservices, while there is no place for this in the simple app, I had to say regarding the fallacy of splitting things to go faster:
"It is like getting 9 pregnant women in one room and expecting them to deliver one baby in a month."
If you have experienced the pain of premature microservices, or have a success story with Modular Monoliths, feel free to mention it in the comments.
