The Three-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the ever-evolving landscape of startups, one piece of advice frequently resonates: “Do things that don’t scale.” While many embrace this philosophy, few delve into practical methods for applying it, particularly in the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve outlined a straightforward approach: any unscalable workaround is granted a lifespan of three months. If it succeeds in demonstrating its worth, it will be transformed into a structured solution; if not, it will be discarded.

As engineers, we often feel pressured to create scalable solutions from the outset. We’re trained to consider complex architectures featuring design patterns, microservices, and distributed systems to manage vast user bases. However, this mindset may be better suited for established companies than for nimble startups.

Focusing on scalability too early can lead to unnecessary complications and delays, as we often find ourselves optimizing for users who may not even exist yet. This is where my three-month rule comes into play, encouraging me to craft straightforward, even “imperfect,” code that can be released quickly. This practical approach allows me to genuinely understand user needs.

Current Infrastructure Strategies: Why They Work

1. Consolidated Resources on a Single VM

Currently, my entire setup—database, web server, background jobs, and caching—resides on a single virtual machine costing $40 per month. This approach may appear risky due to the absence of redundancy and reliance on manual backups, yet it has provided invaluable insights.

Within just two months, I’ve gained a clearer understanding of my actual resource requirements. My platform, which I initially anticipated would demand significant resources, has shown to peak at a mere 4GB of RAM. The complex Kubernetes infrastructure I nearly implemented would have only been managing idle resources. When failures occur (and they have), I receive concrete data regarding the issues, often revealing surprises about what truly breaks.

2. Simple, Hardcoded Configurations

My configurations exist as constants scattered throughout the codebase—no elaborate config files or environment variables here. Adjusting any settings requires redeploying, yet this straightforward method has its perks.

I can quickly search my entire codebase for any configuration value, streamlining the tracking of every price change through version control. While developing a configuration service would have taken considerable time, in the past three months, I’ve only modified these values a handful of times. This