Building with AI Coding Assistants? The Monolith Isn't Just 'OK,' It's Your Secret Weapon.

Hello, Indie Hackers.

I am sure that at this point, AI coding assistants, vibe-coding tools, and autonomous software development platforms have established themselves quite well in the lives of many indie hackers. In my first article here, I wanted to cover a topic related to that, but something that is not openly discussed - at least I haven't seen many of these - of how your software design choices impact your product, especially when developing with AI to avoid the vibe-coding nightmares. So, I wanted to talk about the "Majestic Monolith".

We've all seen the diagrams: a dozen little boxes, all talking to each other, promising "infinite scale." It's the microservice dream, plastered across every big tech blog.

But if you're a solo founder or a tiny team using AI assistants to build your product, that dream is a trap. It's a tax on your time, your runway, and your sanity.

I'm doubling down on the "Majestic Monolith" for this very reason. The phrase comes from DHH at Basecamp, and it's simple: keep your app in one solid, well-structured codebase.

This isn't about being old-school. It's about building a better partnership with your new AI developer.

Your AI Copilot Hates Your Microservices.

This is the most important part. Think about how coding assistants like GitHub Copilot or other Autonomous AI Agents work. They have a limited context window.

In a monolith: The AI can see the controller, the service object, the database schema, and the view all at once. You can ask it to "add a company_name field to the user sign-up flow," and it can trace the change from the database migration to the backend model to the API endpoint to the frontend form. It understands the entire flow.

In a microservice jungle: You're asking your AI to write a novel by showing it one word at a time. Its effectiveness plummets. It might update the user-service, but it has no idea that the onboarding-service also needs to be changed. You end up being the one stitching the context together, which defeats the purpose of the AI assistant.

This is the difference between an AI that scaffolds an entire feature for you and one that just autocompletes variable names.

The 2 AM Debugging Nightmare You Can Actually Solve!

We've all been there. Production is on fire. 🔥

With a monolith: You get one stack trace. You can step through the entire flow—from the HTTP request to the database write in your one IDE. You can find the bug, ask your AI to write a test case for it, and deploy a fix.

With microservices: You're tail-ing logs in five different terminals, checking if the message queue dropped something, and praying the timestamps line up. You're trying to figure out if auth-service deployed a breaking change that billing-service didn't know about. It's a crime scene investigation, and you're the only detective.

Stop Wasting Your Runway on "Infra".

Microservices get expensive. Fast. More servers, more databases, more dashboards, more CI/CD pipelines to maintain, more glue code you have to pay to run.

A monolith keeps your hosting bill sane. You can run a ton of apps on one decent server (like on Hetzner or DigitalOcean) and a single managed database.

That's at least $500/month you save on redundant infra is your runway. It's another month you get to find product-market fit. Don't set your cash on fire just to look "modern."

Your Brain is the Real Bottleneck.

As a solo founder, as most indie-hackers start there, you have to hold the entire system in your head. A monolith makes that possible.

You can trace a bug, design a new feature, and understand the performance implications without mentally load-balancing six different service boundaries and their async communication patterns.

And let's be honest about local development:

Monolith: git pull, run your migrations, start the server. You're debugging your app.

Microservices: You're fighting a 500-line docker-compose.yml file, debugging arcane network issues between containers, and praying the local Kubernetes cluster doesn't eat all your RAM. You're debugging your infrastructure.

That's friction that kills startups.

How to Not Build a "Big Ball of Mud".

This isn't an excuse to write spaghetti code, of course. The fear of the "Big Ball of Mud" is what pushes people to microservices in the first place.

The secret is the Modular Monolith. You get the simplicity of one codebase with the clean boundaries of separate services.

Carve domains early. Your code should be organized by what it does (e.g., Auth, Billing, Reporting, Projects), not by what it is (e.g., Controllers, Models). Keep these modules from reaching into each other's internals.

Ports and adapters. Keep "side effects" (database queries, external API calls, S3 uploads) at the edges of your app. This makes it easy to switch providers or, if necessary, extract a service later.

Background jobs, not services. Need to send an email or process an upload? Use a job queue (like Sidekiq, Celery, etc.) that runs in the same codebase. Don't build a whole new service for it.

One DB, many schemas. You can keep data separate and clean within a single database cluster by using different schemas or table prefixes.

How to Supercharge Your AI Assistant with a Monolith.

Building this way directly enhances your AI tools.

Unified Context is King: The AI can see everything from the route to the schema. This unlocks powerful, high-level prompts like, "Add a last_login_at timestamp to the user model, update the controller to set it, and display it on the profile page", and it can actually do it.

Whole-Repo Refactoring: Need to rename a core concept? An AI agent can trace every usage across the entire monolith. In microservices, that's a manual, multi-repo nightmare that you wouldn't even attempt.

Better Test Generation: AI is amazing at writing tests... when it can see the code it's testing and the data models it interacts with. A monolith makes this trivial.

When to Actually Split: Earn Your Complexity.

You don't avoid microservices forever. You earn them.

Don't split based on "vibes." Split when you hit real, objective pain points.

1. Team Pain: You've actually hired, and two independent teams are constantly stepping on each other's toes in the same modules.
2. Scale Pain: One specific part of your app (like video processing or batch analytics) is genuinely starving the rest of the app for resources, and you can't scale it independently.
3. Regulatory Pain: You have a new enterprise customer that requires a specific part of their data to live in a totally separate box (e.g., GDPR, HIPAA).

When you hit one of these, extract one service. Not ten.

Stop Reading FAANG Blog Posts.

Shopify runs one of the largest monoliths on earth. GitHub and Basecamp scaled to massive heights on a monolith.

Stop reading architecture blog posts from FAANG companies that have problems you don't. Their 1,000-engineer-organization-chart-as-an-architecture is not your blueprint. It's a trap.

Build a clean, modular monolith. Give your AI assistant the context it needs to be a true partner. Respect your runway. Respect your own brain.

And most importantly, ship. 🚀