February 1, 2026
Scale tales & spicy fails
How to Scale a System from 0 to 10M+ Users
From 0 to 10M users? Readers say the math is wild—and maybe AI wrote it
TLDR: A scaling guide says start simple and add complexity as users grow. Commenters blast the user limits as wildly low, dunk on autoscaling as cloud price hype, and suspect AI co-writing—turning a practical roadmap into a lively debate about real-world capacity and credibility.
Big-tech vet Ashish Pratap Singh drops a simple gospel: don’t over-engineer, start on one server, add complexity only when real people show up. He charts seven steps to scale, name-drops Instagram’s scrappy beginnings, and points to his startup AlgoMaster.io as proof. But the crowd isn’t just reading—they’re roasting the numbers. Multiple commenters say his user estimates are way too low, arguing modern machines can handle 100–1000x more. One summed it up as: basically, your “limit” is just your laptop laughing.
Then the drama hits: autoscaling (computers adding more power automatically when traffic spikes) gets dragged as cloud price theater, with claims it solves problems the cloud created. And a skeptic throws a grenade: this post “reads like AI”, pointing to the author’s public AI repo, igniting a side-thread on whether that’s a red flag or just 2026 reality. There are friendly voices (“Nice read”), but the spicy chorus wins. Jokes fly about multiplying everything by 1000, and the classic line “Is your site slow?” becomes the comment section’s new catchphrase. In short: the guide is neat, but the real show is the audience calling out math, money, and machine-written vibes.
Key Points
- •The author advocates scaling systems through defined stages, starting simple and evolving as bottlenecks appear.
- •Stage 1 recommends a single-server architecture where the app, database, and background jobs run on one VM, often behind Nginx.
- •Example components include Django/Rails/Express/Spring Boot, PostgreSQL/MySQL, and Sidekiq/Celery; hosting via a $20–50/month VPS.
- •Benefits include fast deployment, low cost, quick iteration, easier debugging, and full-stack visibility.
- •Signals to move on include slow peak-time queries, sustained 70–80%+ resource usage, deployment-caused downtime, background job crashes affecting the web tier, and intolerance for any downtime.