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
One Comment
This is a fantastic and pragmatic approach to balancing rapid iteration with foundational robustness. The 3-month rule effectively emphasizes the importance of getting feedback quickly without over-investing in premature scalability—something that can stall early-stage innovation.
Your use of simple infrastructure such as a single VM and hardcoded configurations aligns well with the current Lean and Agile principles—prioritizing learning and flexibility over perfection. It also reminds us that code simplicity and infrastructural minimalism can reveal critical insights about usage patterns and failure points that might be obscured by overly complex systems early on.
One addition I’d suggest is documenting these “messy” solutions and their rationales, so as the project grows, you can better evaluate which shortcuts to keep, optimize, or replace. Also, considering how to gradually abstract and transform these workarounds as the product matures ensures that needed scalability does not get deferred indefinitely.
Overall, your approach underscores the value of intentional constraints and thoughtful experimentation—great insights for any startup engineer tackling the challenge of balancing speed and scalability!