Embracing the 3-Month Rule: A Pragmatic Approach to Scaling Your Startup
In the world of startups, the age-old advice from Paul Graham to “do things that don’t scale” often goes unexamined, especially within the context of software development. How do we apply this principle in our coding practices?
After spending the past eight months developing my AI podcast platform, I’ve established a straightforward strategy: any unscalable shortcut has a lifespan of three months. Within that timeframe, it must either demonstrate its worth and be properly implemented or be discarded.
As engineers, we are often conditioned to think in terms of scalability from the outset. We gravitate toward design patterns, microservices, and distributed systems, treating them as the gold standard for architecture that can cater to millions of users. However, this large-scale mindset can hinder the agility and innovation needed in a startup environment.
In reality, focusing on scalable solutions too early can lead to unnecessary expenses and complications. You might waste time optimizing for challenges that don’t exist yet while overlooking the immediate needs of your users. My 3-month rule encourages me to focus on writing straightforward code that delivers results, providing invaluable insights into my users’ genuine requirements.
Current Infrastructure Hacks: Smart Choices for Learning
1. Everything Runs on a Single VM
All components of my platform — the database, web server, background jobs, and caching system — operate on a single $40/month virtual machine. Although this setup lacks redundancy and requires manual backups, it has been a game-changer in terms of understanding my resource utilization.
In just two months, I’ve learned more about my actual needs than I ever would have through traditional capacity planning. For example, my resource peak only reaches 4GB of RAM, a revelation that saved me from an elaborate Kubernetes setup that would have only ended up managing dormant containers. When issues arise — and they have — I gain critical insights into my system’s vulnerabilities.
2. Hardcoded Configuration for Simplicity
All of my configuration settings are hardcoded as constants:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This leads to straightforward deployments; every change requires a redeployment, making it easy to trace changes through git history. Although some might argue for a dedicated configuration service, I’ve only needed to change these variables
One Comment
This post offers a compelling perspective on balancing the need for quick experimentation with the long-term goal of scalability. I appreciate the emphasis on the 3-month rule as a practical timeframe to validate ideas and avoid overengineering—it’s a refreshing reminder that early agility often trumps premature optimization.
Your example of consolidating everything into a single VM and using hardcoded configurations highlights the importance of simplicity in the initial phases. It’s remarkable how much can be learned about actual system needs and user behavior by removing complexity, saving time and resources.
This approach resonates with the Lean Startup methodology—building just enough to test assumptions and iterating rapidly. As startups grow, this foundation provides invaluable insights that inform more scalable solutions down the line.
Have you encountered situations where you had to pivot away from these “hacky” solutions earlier than planned? How did you manage the transition from rapid experimentation to more robust infrastructure?