Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the startup realm, the mantra “Do things that don’t scale,” as popularized by Paul Graham, is often thrown around. Yet, the specifics of applying this wisdom in technical environments remain less frequently discussed. I’ve been immersed in developing my AI podcast platform for the past eight months, during which I’ve crafted a straightforward methodology: any temporary, non-scalable workaround gets a three-month trial period. At the end of this period, if the solution has demonstrated its worth, it gets more robust development; otherwise, it gets retired.

As engineers, we often fall into the trap of designing for scalability from the outset. Concepts like microservices and distributed systems focus on accommodating vast user bases. However, this mindset is usually best suited for larger enterprises, whereas startups frequently find that spending time and resources on scalable architectures is often a distraction.

Implementing my 3-month rule compels me to embrace simpler, more direct solutions that deliver results in real time and provide genuine insights into user needs.

My Current Unscalable Solutions and Their Unexpected Benefits

1. Single Virtual Machine Operation

All components of my platform—including the database, web server, and background jobs—reside on a single $40/month virtual machine. No redundancy is in place, and I manually back up data to my local machine.

Counterintuitively, this approach has illuminated my actual resource requirements far more effectively than conventional capacity planning methods. Within just two months, I’ve discovered that my “AI-heavy” platform peaks at 4GB of RAM. The intricate Kubernetes deployment I considered would have involved managing unnecessary resources.

When my system encounters issues (which it has twice), I gain invaluable data about failure points—often, they are not what I anticipated.

2. Hardcoded Configuration

I keep all configurations hardcoded within my application, such as pricing tiers and user limits. This means any adjustments require a republish.

The advantage? I can quickly search my entire codebase for configuration values, making changes traceable in git history. In the last three months, I have made only three configuration alterations, saving me the considerable time it would have taken to build a dedicated configuration service.

3. Using SQLite in Production

I decided to run SQLite for a multi-user web application, which has proven efficient with a database size of just 47MB. It comfortably manages 50 simultaneous users