Embracing the Imperfect: The 3-Month Rule for Scaling an AI Startup
In the world of startups, you often hear the mantra from tech luminary Paul Graham: “Do things that don’t scale.” While this advice is familiar, implementing it effectively can be a challenge, especially for software engineers.
After dedicating eight months to developing my AI podcast platform, I have formulated a straightforward approach: every unscalable hack is granted a trial period of three months. At the end of this timeframe, the hack must either demonstrate its value and be elevated to a more robust solution or be discarded altogether.
The Shift in Mindset
As engineers, we are often conditioned to create scalable solutions from day one. The allure of intricate architectures—like microservices and distributed systems—can be hard to resist, particularly with the prospect of serving millions of users. However, for a startup, this focus on scalability can inadvertently lead to costly delays and preemptive optimizations for non-existent users. My three-month rule encourages me to craft simple, straightforward code that delivers real value, ultimately clarifying user needs.
My Current Simplified Tech Stack
Here are the key elements of my infrastructure and the reasoning behind these unorthodox choices:
1. Single Virtual Machine Infrastructure
All crucial components, including the database, web server, background tasks, and caching, operate on a single $40/month virtual machine. This setup, which lacks redundancy and relies on manual backups, has yielded invaluable insights about my resource requirements. In just two months, I learned that my platform’s peak demand is only 4GB of RAM. The extensive Kubernetes setup I was contemplating would have only managed underutilized resources. Moreover, the system crashes I’ve experienced provided real feedback on genuine vulnerabilities rather than hypothetical scenarios.
2. Hardcoded Constants for Configuration
Configuration values, such as pricing tiers and maximum users, are hardcoded within my codebase, eliminating the need for external configuration files or environment variables. While this approach may seem rudimentary, it has its advantages: it allows for rapid searches through the code for any crucial value and ensures that every configuration change is logged. Building a dedicated configuration service would have consumed an entire week, and given that I’ve only made three changes in three months, I saved considerable engineering time.
3. Using SQLite in a Multi-User Application
Yes, you read that correctly—I’m employing SQLite for a multi-user application, with a database size
