Embracing Unscalable Solutions: The 3-Month Experiment for Startups
In the world of entrepreneurship, especially within the tech sphere, we often hear the advice from influential figures like Paul Graham: “Do things that don’t scale.” However, the conversation typically doesn’t delve into the practical aspects of implementing this ethos in software development.
After eight months of developing my AI podcast platform, I’ve adopted a straightforward methodology: any unscalable solution is given a three-month trial period. At the end of this period, if the approach proves its worth, it gets a more robust development. If not, it is discarded.
The Dilemma of Scalable Solutions
As engineers, we frequently gravitate towards building scalable architectures from the outset, often inspired by principles like microservices and distributed systems—frameworks designed to handle vast user volumes. This mindset aligns with larger enterprises, but in a startup context, striving for scalability can lead to unnecessary complications.
At a fledgling company, writing scalable code often becomes a form of procrastination. We expend resources on potential user bases that may not materialize, prematurely solving challenges that may never arise. By enforcing my three-month rule, I compel myself to create straightforward, albeit less-than-perfect, code that is functional and reveals the genuine needs of my users.
Current Infrastructure Innovations: Smart Hacks
1. Consolidated Operations on a Single VM
I run my database, web server, background tasks, and Redis all on a single virtual machine for just $40 a month, with no redundancy and manual backups to my drive.
Why is this a savvy decision? In just two months, I gained more insights into my resource requirements than any capacity-planning document could provide. My platform peaks at 4GB of RAM, meaning the complex Kubernetes setup I nearly implemented would have involved managing empty containers. When my system crashed—twice so far—I gathered invaluable data on failure points, revealing issues I hadn’t anticipated.
2. Simplified Configuration Management
Instead of using configuration files or environment variables, I employ hardcoded constants throughout my codebase for essential values like:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach may seem primitive, it allows me to easily search any config value using grep and track changes through git history. Each adjustment
One Comment
This post highlights a pragmatic approach that many early-stage startups can benefit from—focusing on rapid, unscalable solutions with a clear exit strategy. The three-month trial period acts as a tangible checkpoint to validate whether a solution truly adds value before investing heavily in scalability, which is often premature and resource-intensive.
I appreciate the emphasis on learning from real-world constraints—like consolidating operations on a single VM and employing simple configuration management. These “hacky” tactics can accelerate development and reduce complexity, allowing founders to gain crucial insights into actual user needs and system behavior without over-engineering at the outset.
Your method underscores an important lesson: sometimes, the most effective way to prepare for scaling is to first build simple, functional solutions that are iteratively tested and refined. It reminds me of the “fail fast” mentality—embracing unscalable solutions as experiments that can inform smarter, future growth strategies. Thanks for sharing this practical framework; it’s a valuable perspective for founders navigating the delicate balance between speed, simplicity, and growth.