Scaling a system is one of the more challenging aspects of software engineering. Through building products like Relaticle and earlier ventures, I've learned that successful scaling isn't just about handling more traffic—it's about building systems that can evolve with your product.

Start with the Right Foundation

The biggest mistake I see (and have made) is over-engineering from day one. You don't need Kubernetes and microservices when you're serving 100 users. Start simple, but make deliberate choices that won't box you in later.

Key principles:

• Modularity over distribution: Build modular monoliths before jumping to microservices
• Database design matters: Get your data model right early; migrations are expensive
• Observability from day one: You can't scale what you can't measure

Scaling in Phases

In my experience, scaling typically happens in phases. What works at each stage is different.

Phase 1: Optimize What You Have (0-10K users)

Initially, optimization is often the most cost-effective approach. Focus on:

• Query optimization
• Proper indexing
• Caching strategies
• Connection pooling

// Simple Redis caching example
const getCachedUser = async (userId: string) => {
  const cached = await redis.get(`user:${userId}`)
  if (cached) return JSON.parse(cached)

  const user = await db.users.findUnique({ where: { id: userId } })
  await redis.setex(`user:${userId}`, 3600, JSON.stringify(user))

  return user
}

At this stage, profiling and understanding your actual bottlenecks matters more than premature optimization.

Phase 2: Add Infrastructure (10K-100K users)

This is where you start adding more infrastructure to handle load:

• Load balancing
• Session management (stateless where possible)
• Database read replicas
• CDN for static assets

The goal is to handle increased load without major architectural rewrites.

Phase 3: Consider Distribution (100K+ users)

At higher scale, you start dealing with distributed systems challenges:

• Eventual consistency trade-offs
• Background job processing
• Multi-region considerations if you have global users

Not every product needs to reach this phase, and that's fine. Build for your actual needs, not theoretical future scale.

The Performance Budget Mindset

One framework I've found valuable is the concept of a "performance budget." Just as you have a financial budget, you should have a performance budget for every feature.

Ask yourself:

• How much latency can we tolerate?
• What's our acceptable error rate?
• How many database queries is too many?

This mindset forces you to make intentional trade-offs rather than reactively optimizing.

Common Pitfalls to Avoid

1. Premature Optimization

Don't optimize based on assumptions. Use real data from production to guide your efforts. I've wasted time optimizing code paths that turned out not to be bottlenecks.

2. Ignoring the Database

The database is often the bottleneck. Invest time in understanding query patterns, indexing strategies, and when to denormalize.

3. Neglecting Monitoring

You need comprehensive monitoring and alerting before you need to debug production issues. For Laravel applications, consider:

• Laravel Nightwatch — First-party monitoring built for high-volume Laravel apps
• Sentry — Error tracking with Laravel SDK
• Laravel Pulse — Free production monitoring dashboard
• Laravel Telescope — Debug assistant for development

The key is having visibility into both application-level issues (slow queries, failed jobs, exceptions) and infrastructure metrics.

4. Cargo Cult Architecture

Just because Netflix uses something doesn't mean you need it. Choose technologies that solve your actual problems at your actual scale.

What I Use

For context, here's what I typically reach for:

• Laravel/PHP for application logic (it handles more than people think)
• MySQL/PostgreSQL with proper indexing
• Redis for caching and queues
• Cloudflare for CDN and edge caching
• Simple queue workers for background jobs

This stack has handled 100K+ users without major issues. The key is understanding your bottlenecks and addressing them specifically rather than over-architecting upfront.

Final Thoughts

Scaling is a journey, not a destination. The architecture that works for 1,000 users will look different from the one that serves 100,000.

The key is to build systems that can evolve incrementally, with clear observability and well-defined boundaries. Solve the problems you actually have, and stay ready to adapt as you grow.

Remember: the best architecture is the one that serves your users reliably while allowing your team to ship features quickly. Everything else is a trade-off.

What scaling challenges have you encountered? I'd be interested to hear your experiences. Reach out on Twitter or LinkedIn.