How to Build Scalable Custom Software from Day One
Don't optimize for 10M users on day one — but design so you can scale. Over-engineering wastes time. Under-engineering creates rewrites. This guide covers the key patterns we use: stateless servers, database design, caching, async jobs, and what to avoid.
Table of Contents
Key Scalability Patterns
These patterns don't add much cost upfront but make scaling possible later. We build them in from the start.
Stateless app servers (scale horizontally)
No session state on servers. Add more instances behind a load balancer. Cloud-native from day one.
Database: connection pooling, read replicas
Pool connections. Use read replicas for heavy reads. Avoid single-point bottlenecks.
Caching (Redis) for hot data
Cache frequently accessed data. Reduce DB load. Sub-millisecond response for cached reads.
Async jobs for heavy work
Background jobs for reports, exports, notifications. Don't block the request. Use queues (Bull, Celery).
CDN for static assets
Images, JS, CSS on CDN. Faster load. Less load on origin.
Avoid N+1 queries from start
Eager load. Use proper ORM patterns. N+1 kills performance at scale.
What to Avoid
- • Storing session state on the server (use Redis or stateless JWT)
- • N+1 queries (eager load, batch fetch)
- • Synchronous heavy work in request path (use background jobs)
- • Monolithic database with no indexing strategy
- • Tight coupling that prevents splitting services later
When to Optimize
Build for 100-1000 users first. Optimize when you have data: slow queries, bottlenecks, real load. Premature optimization wastes time. But use the patterns above from day one — they're low-cost and prevent painful rewrites. Our FinFlow case study was built scalable from the start and now handles 10K+ users.
Frequently Asked Questions
Do we need microservices?
Usually no for MVP. Start monolithic. Split when you have clear boundaries and a reason (team size, scaling a specific part). Microservices add complexity.
What about serverless?
Good for event-driven, sporadic workloads. For always-on apps, traditional servers or containers often cost less and are easier to reason about. We use both depending on the use case.