Embracing Unscalable Solutions: The 3-Month Framework for startup Success

In the world of entrepreneurship, especially in the tech industry, there’s a popular mantra attributed to Paul Graham: “Do things that don’t scale.” While this advice is widely quoted, the challenge lies in understanding its practical application, especially when it comes to coding.

Over the past eight months spent developing my AI podcast platform, I’ve established a straightforward framework: any unscalable solution gets a fair trial of three months. At the end of that period, we evaluate its effectiveness and either enhance it or eliminate it altogether.

As engineers, we often default to creating scalable architectures from the outset—embracing design patterns, microservices, and distributed systems that can supposedly support millions of users. While such approaches are vital in larger enterprises, they can frequently lead to wasted resources and time in a startup environment. My 3-month rule compels me to prioritize expedient, rudimentary code that actually gets released and provides insight into user needs.

Insights from My Current Infrastructure Hacks

1. All Services on One Virtual Machine

I’ve consolidated my database, web server, background jobs, and caching on a single, $40/month virtual machine without redundancy or automated backups.

You may see this as reckless, but it’s proven to be invaluable. In the past two months, I’ve gained far deeper insight into my resource requirements than any theoretical planning document could provide. It turns out that, despite being an “AI-heavy” platform, I only reach a peak of 4GB RAM. The complex Kubernetes setup I almost implemented would have involved managing empty containers.

When the system has crashed (twice, so far), I’ve received authentic data about what truly causes these issues. Spoiler alert: it’s never what I initially thought.

2. Static Configuration Management

Instead of deploying sophisticated configuration files or environment variables, I simply use hardcoded constants like:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

This approach means that any change necessitates a straightforward redeployment, but it brings a hidden advantage: I can easily grep my code for configuration values. Each adjustment is documented in Git’s history, and every update goes through a code review—albeit just me assessing my own pull request.

Creating a dedicated configuration service would consume a week of time.