The 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Development
In the world of startup development, the entrenched wisdom from industry experts often suggests: “Do things that don’t scale.” However, the process of transforming that guidance into actionable coding practices remains largely unexplored.
Over the past eight months, while constructing my AI podcast platform, I’ve devised an effective framework centered on what I like to call the “3-Month Rule.” This concept hinges on giving every non-scalable solution a three-month trial period. At the end of this timeframe, the solution either proves its worth and is properly developed, or it is discarded.
As developers, we are frequently conditioned to prioritize scalable solutions from the outset. We delve into design patterns, microservices, and distributed systems, crafting intricate architectures aimed at accommodating millions of users. While this mindset is invaluable in larger organizations, in a startup environment, such an approach often equates to expensive delay.
In practice, the focus on scalability can lead to unnecessary complexity, diverting attention from serving immediate user needs. My 3-Month Rule compels me to favor straightforward, often “imperfect” code that delivers real results and reveals invaluable insights into user behavior.
My Current Infrastructure Strategies: Insights from Non-Scalable Choices
1. Consolidated Operations on a Single VM
All essential components—including the database, web server, background jobs, and Redis—are hosted on a single virtual machine, costing just $40 per month. There is no redundancy, and backups are manually performed.
Why is this approach effective? In only two months, I’ve gained a clearer understanding of my actual resource requirements than any theoretical planning document could provide. My platform, which I anticipated would be resource-intensive, has proven to peak at just 4GB of RAM. The complex Kubernetes structure I nearly implemented would have involved managing empty containers.
When the system crashes (which it has a couple of times), I’m able to gather genuine insights about failure points—none of which were what I initially expected.
2. Hardcoded Constants for Configuration
Configurations such as pricing tiers and user limits are hardcoded throughout the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With no unique configuration files or environment variables in play, making adjustments
