The 3-Month Rule: A Pragmatic Approach to Scalability in Software Development
When it comes to launching a startup, the wisdom of Paul Graham resonates—”Do things that don’t scale.” However, the challenge lies in translating this advice into actionable steps for software engineers. After dedicating eight months to developing my AI podcast platform, I’ve devised a straightforward framework: any unscalable solution is granted a lifespan of three months. In this timeframe, it must demonstrate its value or face elimination.
As engineers, we are often conditioned to prioritize scalable solutions, diving straight into design patterns and architectures that can support millions of users. This is the mindset of established companies, but for startups, crafting scalable solutions from the outset can often lead to unnecessary complexity and cost. My three-month rule encourages the creation of simpler code that is functional and allows me to learn precisely what my users need.
Current Infrastructure Hacks: Embracing Simplicity
1. Consolidated Operations on a Single Virtual Machine
I host everything—my database, web server, background processes, and caching—on one $40 monthly virtual machine, with no redundancy. While this approach might seem reckless, it has provided invaluable insights into my resource requirements. Surprisingly, my AI-driven platform generally peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have only managed unused containers. When the system glitches (which has happened twice), I receive immediate, hands-on data about the real issues at play—often contrary to my initial guesses.
2. Hardcoded Configuration for Accessibility
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By using hardcoded constants rather than environment variables or configuration files, I can swiftly search my codebase for any configuration change. Tracking price updates in Git history grants me a clear view of modifications. In three months, I’ve altered these constants only three times, taking just 15 minutes for redeployment, whereas developing a dedicated configuration service could take over 40 hours.
3. Utilizing SQLite in a Production Environment
Surprisingly, I’m using SQLite for a multi-user web application, and my entire database is just 47MB, efficiently handling 50 concurrent users. This experience has revealed that my access patterns lean heavily towards reads rather than writes—95% to 5%. Had I
One Comment
This post highlights a pragmatic approach that resonates strongly with the realities many startups face—prioritizing speed, learning, and simplicity over early-stage scalability. The “3-Month Rule” effectively acts as a disciplined experiment window, allowing teams to validate ideas without over-investing in overly complex solutions that may never be necessary.
Your reliance on lightweight infrastructure choices—single VMs, hardcoded configs, SQLite—illustrates how tactical decisions can provide quick feedback and a clearer understanding of user needs before scaling complexity. It’s a reminder that sometimes, “less” truly is “more,” especially in the early phases of product development.
I also appreciate the philosophy of embracing failure and iteration—glitches revealing real issues rather than anticipated ones—helping to build a resilient and adaptable foundation. As you approach scalability later, these insights will be invaluable for designing targeted, efficient solutions grounded in actual usage patterns. Thanks for sharing such a candid and thoughtful framework!