Scaling is a complex topic, but after working at big tech on services handling millions of requests and scaling my own startup (AlgoMaster.io) from scratch, I’ve realized that most systems evolve through a surprisingly similar set of stages as they grow.
The key insight is that you should not over-engineer from the start. Start simple, identify bottlenecks, and scale incrementally.
In this article, I’ll walk you through 7 stages of scaling a system from zero to 10 million users and beyond. Each stage addresses the specific bottlenecks that show up at different growth points. You’ll learn what to add, when to add it, why it helps, and the trade-offs involved.
Whether you’re building an app or website, preparing for system design interviews, or just curious about how large-scale systems work, understanding this progression will sharpen they way you think about architecture.
Note: The user ranges in this article are rough guidelines. The exact thresholds will vary based on your product, workload, and traffic patterns.
Stage 1: Single Server (0-100 Users)
When you’re just starting out, your first priority is simple: ship something and validate your idea. Optimizing too early at this stage wastes time and money on problems you may never face.
The simplest architecture puts everything on a single server: your web application, database, and any background jobs all running on the same machine.
This is how Instagram started. When Kevin Systrom and Mike Krieger launched the first version in 2010, 25,000 people signed up on day one. They didn’t over-engineer upfront. With a small team and a simple setup, they scaled in response to real demand, adding capacity as usage grew, rather than building for hypothetical future traffic.
What This Architecture Looks Like
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