Scaling Systems from Prototype to Production: Real Lessons
Building a prototype is fun. Scaling it to handle millions of requests while staying profitable? That's the real challenge.
Our Journey
We started with a system that could handle 100 requests per second. Within 18 months, we needed to handle 10M daily requests while reducing costs by 40%.
What Actually Matters
Database
Most scaling problems start here. We moved from a monolithic PostgreSQL to a sharded architecture with read replicas. The cost-benefit analysis was critical.
Caching Strategy
Don't cache everything. We implemented a tiered caching strategy: Redis for hot data, CDN for static content, and database queries for cold data.
Infrastructure
We containerized everything. Kubernetes gave us the flexibility we needed, but it wasn't a silver bullet—it introduced operational complexity.
The Mistakes We Made
- Over-engineering early: We built for 10M requests when we had 10K. Premature optimization cost us 6 months.
- Ignoring observability: We couldn't see where bottlenecks were until we had proper monitoring.
- Not planning for failure: Our first production incident happened because we didn't think through disaster recovery.
What We'd Do Differently
- Instrument first, optimize second
- Understand your data access patterns before choosing a database
- Build for failure from day one
- Measure, don't guess
The Bottom Line
Scaling is about trade-offs. There's no one-size-fits-all solution. What works for us might not work for you. But understanding the principles will help you make better decisions.
