Hello Devs! 👋 Have you ever wondered how platforms like Instagram, LinkedIn, or YouTube handle millions (or even billions) of users seamlessly? Do you think they built their highly scalable, complex architectures from day one? If you assume they had everything figured out on the first attempt, you’d be surprised.
The reality is, these platforms evolved gradually. They started simple, observed system behaviour, and scaled incrementally based on real needs, not assumptions.
As engineers, we often fall into the trap of over-engineering too early. When building a new platform, there’s a natural temptation to implement event-driven architectures, message queues, caching layers, and microservices from day one. But do we really need all of that upfront? No one gets architecture perfect on the first try.
Instagram is a perfect example. When they started, they didn't have the complex, scalable infrastructure they do today. They also failed in the beginning, manually scaling their system—before weekends, they had to add more servers manually just to handle the expected load. It was a journey of learning and evolution. As a Staff Engineer building platforms at NuShift, I find myself asking similar questions:
What will be my initial user base?
Should I build everything for scale now, or should I start simple and evolve over time?
The answer is clear: implement what you need, observe, and then scale smartly.
Instagram’s Evolution: A Lesson in Scaling
Instagram’s infrastructure journey teaches us a lot about when and how to scale:
Start simple: Instagram began as a monolithic architecture. No microservices, no complex event-driven design—just a straightforward system.
Observe system behaviour: As their user base grew, they started experiencing bottlenecks in database queries, image processing, and request handling.
Scale where needed: Instead of redesigning everything from scratch, they incrementally added caching (Redis, Memcached), optimised databases (PostgreSQL sharding), and implemented asynchronous processing (Celery, Kafka).
Continuous Evolution: Over time, Instagram moved towards a service-oriented architecture, but only when it was necessary.
Had they over-engineered from day one, they would have slowed down feature development, introduced unnecessary complexity, and wasted engineering time solving problems they didn’t even have yet.
Building for Today vs. Tomorrow: My Approach at NuShift
At NuShift, I often face a similar challenge. I have to decide:
Should I preemptively build for millions of users?
Should I implement a full-scale event-driven system before the first release?
Do I need queue-based processing, or is synchronous processing enough for now?
Here’s my approach:
✅ Start with the Basics: Get the core functionality working before adding complexity. Users will tell you where the pain points are.
✅ Observe and Measure: Use logging, monitoring, and metrics to track system behaviour. Performance bottlenecks will become clear over time.
✅ Scale Where Necessary: If database read operations are slow, introduce read replicas or caching. If too many background tasks pile up, introduce queues like SQS or Kafka.
✅ Avoid Premature Optimisation: Not everything needs microservices or Kubernetes from day one. Focus on solving real-world problems, not hypothetical ones.
Other Companies That Follow This Approach
Instagram isn’t the only one that scaled this way:
Twitter: Started as a simple Rails app, later introduced queues, caching, and distributed storage as they grew.
Airbnb: Began with a monolithic architecture, then adopted microservices when their scale demanded it.
Netflix: Started with on-premise data centers, then moved to AWS cloud infrastructure as demand exploded.
None of these companies built their final, scaled architecture on day one. They scaled as needed, when needed.
Key Takeaways for Engineers
No one architecture fits all. Start simple and evolve based on real-world demands.
Scaling should be an iterative process. Monitor your system and fix what needs fixing, instead of blindly implementing every best practice.
Premature optimisation can slow you down. Building for scale before you have users is an inefficient use of time and resources.
Learn from real-world examples. Companies like Instagram, Twitter, and Netflix scaled over time, not overnight.
Final Thoughts
If you're building a new platform, don’t get caught up in trying to perfect everything from day one. Focus on building a solid foundation, listen to your system, and let real usage guide your scaling decisions. This is what Instagram did, what major tech giants did, and what we are doing at NuShift.
Scaling is a journey of learning and evolution. The best approach? Implement, observe, refine, repeat. 🚀
References :
https://blog.bytebytego.com/p/how-instagram-scaled-its-infrastructure?ref=dailydev
Top comments (1)
This is a great read, thanks for this. Knowing that it's not going to be perfect from the onset but you'll gradually evolve into a stable architecture.