Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of startups, the mantra to “do things that don’t scale” often surfaces, especially in discussions about product development. However, there’s less emphasis on how to practically apply this advice within the coding landscape. After eight months of building my AI podcast platform, I’ve come up with a straightforward framework: any unscalable workaround is given a lifespan of three months. Within that period, it must either demonstrate its value and be reengineered or be discarded.
Rethinking Scalability
As engineers, we are often conditioned to create scalable solutions from the outset—employing microservices, distributed systems, and intricate architectures that are designed to accommodate millions of users. While this may work well for established companies, it can be counterproductive for startups. In our context, focusing on scalability too early can turn into costly procrastination. The 3-month rule compels me to write straightforward, albeit “messy,” code that can be deployed quickly, providing invaluable insights into user needs.
My Current Infrastructure Strategies: Simple Yet Effective
1. Single VM for Everything
I run my entire stack—database, web server, background jobs, and caching—on a single $40/month virtual machine. While there’s no redundancy and I have to perform manual backups, this setup has yielded key learnings about my resource usage in just two months. For instance, my platform’s peak memory demand is a mere 4GB. Had I opted for a complex Kubernetes architecture, I would have found myself managing non-existent resource demands.
When the server crashes (which has happened a couple of times), I gather real-world data about failures. It’s been enlightening to discover that the points of failure are often unpredictable.
2. Hardcoded Configurations
Instead of utilizing configuration files or environment variables, I have constants like this scattered throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach may seem simplistic, but it offers a unique advantage: I can quickly search my entire codebase for any configuration value. Changes are recorded in Git history, and each modification is reviewed meticulously. In three months, I’ve altered these values only three times, saving me roughly 40 hours of development effort in favor of
