It sounds great in the pitch deck, but the architectural debt is drowning you before you even hit product-market fit.
There is an epidemic in early-stage software engineering. Startups with three developers and zero paying customers are architecting their systems like they are Netflix. They split their core domain into five different microservices, introduce Kafka for event streaming, and spend 40% of their sprints just managing Kubernetes manifests.
We stopped doing this. And our clients are shipping twice as fast because of it.
The Microservice Trap
Microservices solve an organizational problem, not necessarily a technical one. When you have 500 engineers, you need microservices so teams don't step on each other's toes when deploying.
When you have a startup trying to find product-market fit, your biggest threat isn't scale. Your biggest threat is time.
1. The Cost of Serialization
Every time Service A needs to talk to Service B, you are crossing a network boundary. You must serialize data, handle latency, implement retry logic, and worry about dead-letter queues. What used to be a simple function call in a monolith const user = await getUser(id) is now a distributed systems problem.
2. Deployment Nightmare
Instead of deploying one container, you are now orchestrating a fleet. You need distributed tracing just to figure out why a user's password reset failed.
The Return of the Majestic Monolith
At Diloxy, we have completely reverted our stance for any system under 100,000 daily active users. We build Majestic Monoliths.
We use a heavily modularized Node.js structure.
// A beautifully simple, self-contained architecture
import { PaymentService } from './modules/payments';
import { UserService } from './modules/users';
export async function processCheckout(userId, amount) {
const user = await UserService.findById(userId);
if (!user.isActive) throw new Error("Account inactive");
return await PaymentService.charge(user.stripeId, amount);
}
No network hops. Transitive type safety. A single test suite that can run end-to-end locally on a single developer's laptop without needing Docker Compose or a cloud staging environment.
When do we scale?
We scale when it hurts. If the PaymentService starts taking 80% of the CPU load because of intense PDF generation, then we carve it out into a separate queue-worker. But we extract it out of necessity, not out of premature optimization.
If you are an enterprise sitting on a massive, highly entangled microservice architecture that feels like it's suffocating your release cycle, you might have gone too far.
Stop predicting scale. Start optimizing for velocity.