Scaling Custom Software: A Practical Guide
Premature optimization is the root of all evil — but when it's time to scale, here's exactly what to do.
Most custom software doesn't need to handle millions of users on day one. But it does need to scale gracefully as your business grows, without requiring a complete rewrite every time you 10x your user base.
This guide covers practical scaling strategies at each stage of growth. We'll tell you what to optimize, when to optimize it, and — just as importantly — what to leave alone until it actually becomes a problem.
Stage 1: Don't Optimize Yet (0-1,000 Users)
At this stage, your biggest risk isn't performance — it's building the wrong thing. Focus on product-market fit and feature velocity. A single server with a managed database handles 1,000 concurrent users comfortably for most applications.
The only "optimization" worth doing here is writing clean code with reasonable database queries. Use indexes on columns you filter and join by. Don't do N+1 queries. Use connection pooling. These are just good practices, not optimization.
Do
Write clean code, use database indexes, avoid N+1 queries, use connection pooling.
Don't
Add caching layers, microservices, message queues, or distributed databases. You don't need them yet.
Stage 2: Add Caching and Monitoring (1,000-10,000 Users)
At this stage, you'll start noticing slow queries and occasional latency spikes. The first step is always monitoring — you can't optimize what you can't measure. Add APM (Application Performance Monitoring) to identify your actual bottlenecks.
Redis caching is usually the single biggest performance improvement you can make. Cache database query results, session data, and computed values. A well-implemented caching layer can reduce database load by 80-90%.
Also consider a CDN (CloudFront, Cloudflare) for static assets and potentially for API responses. CDN caching can eliminate server load entirely for cacheable content.
Stage 3: Horizontal Scaling (10,000-100,000 Users)
Once a single server isn't enough, you need horizontal scaling — running multiple instances of your application behind a load balancer. This requires your application to be stateless (no local file storage, no in-memory sessions).
Database scaling is usually the bottleneck at this stage. Options include: read replicas (route read queries to replicas, writes to primary), connection pooling (PgBouncer for PostgreSQL), query optimization (EXPLAIN ANALYZE your slow queries), and table partitioning for large tables.
Don't jump to microservices. A well-designed monolith handles 100,000 users easily. Microservices add enormous operational complexity and should only be adopted when you have clear service boundaries and the team size to manage them.
Stage 4: Specialized Architecture (100,000+ Users)
At this scale, you need specialized solutions for specific bottlenecks. This might include: message queues (RabbitMQ, SQS) for async processing, Elasticsearch for search and analytics, dedicated services for real-time features (WebSocket servers), and potentially a move to microservices for independently scaling subsystems.
This is also when you should consider hiring (or contracting) a dedicated DevOps/Platform engineer. The operational complexity at this scale requires specialized expertise in monitoring, alerting, auto-scaling, and incident response.
Frequently Asked Questions
Should I build for scale from the start?
When should I switch from a monolith to microservices?
How do I know what to optimize?
How much does scaling cost?
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Whether you're hitting your first performance walls or planning for 100x growth, we can help. Book a free consultation to discuss your scaling strategy.