The 3-Month Guideline: A Pragmatic Approach to Non-Scalable Solutions in Tech
In the world of startups, the mantra to “do things that don’t scale,” popularized by Paul Graham, carries significant weight. However, the intricacies of applying this principle in the realm of coding often go unaddressed. After dedicating eight months to developing my AI podcast platform, I have established a straightforward framework: every unscalable solution is granted a lifespan of three months. If it demonstrates value within that time, it is built out properly; if not, it’s time to move on.
As engineers, we are often trained to create scalable solutions right from the start. Our education emphasizes design patterns, microservices, and distributed systems—structures designed to handle millions of users efficiently. Yet, in a startup environment, prioritizing scalability can result in costly delays and unnecessary complexity. I’ve found that the crux of meaningful development is to focus on simple, direct coding that actually delivers real functionality and gathers vital user feedback.
Current Infrastructure Strategies: Making the Most of Non-Scalable Hacks
1. Consolidation on a Single VM
Running everything—database, web server, background jobs, and Redis—on a single $40/month virtual machine may seem risky due to the lack of redundancy and reliance on manual backups. However, this approach has provided invaluable insights into my resource needs over a mere two months. Instead of spiraling into elaborate setups, I learned that my platform, which is primarily AI-driven, peaks at just 4GB RAM. Attempting to implement a Kubernetes infrastructure would have led to managing empty containers.
When system failures occurred—a total of two times so far—these incidents offered real data about actual system failures, which was frequently unexpected.
2. Hardcoded Configuration
The constants defining key values like pricing and user limits are scattered throughout the codebase rather than stored in configuration files or managed through environment variables. While this results in a necessity for redeployment with each adjustment, the sheer efficiency of this method allows for rapid searches across the entire codebase for any configuration value. Changes have only been needed three times in three months, saving considerable engineering resources.
3. Using SQLite in Production
Yes, you read that correctly: SQLite supports my multi-user web application remarkably well, with my entire database only taking up 47MB. This setup effortlessly accommodates 50 concurrent users. An early analysis revealed that 95%
