Building a SaaS application that can scale from your first hundred users to millions is one of the most challenging aspects of software development. At Lumon Studios, we've built dozens of SaaS products, and we've learned what works and what doesn't when it comes to creating systems that grow with your business.
Start with a Modular Architecture
The foundation of any scalable SaaS application is a well-thought-out architecture. We recommend starting with a modular monolith rather than jumping straight to microservices. This approach gives you clear boundaries between components while avoiding the operational complexity of distributed systems.
Each module should have its own data, business logic, and API surface. This makes it easier to extract modules into separate services later when you have specific scaling requirements.
Database Design for Multi-Tenancy
Multi-tenancy is a core concern for SaaS applications. We've seen three main approaches: shared database with tenant ID columns, separate schemas per tenant, and completely isolated databases. Each has trade-offs in terms of cost, complexity, and data isolation.
For most applications, we recommend starting with a shared database model using tenant ID columns. This keeps operational overhead low while still providing logical isolation. Add row-level security policies to ensure tenants can never access each other's data.
Implement Proper Caching Strategies
Caching is essential for SaaS applications, but it needs to be done thoughtfully. We use a multi-layer caching strategy: application-level caching for computed values, Redis for session data and frequently accessed objects, and CDN caching for static assets.
Always include the tenant ID in your cache keys to prevent data leakage between customers. Implement proper cache invalidation patterns from day one, as this becomes much harder to add later.
Background Job Processing
Any operation that takes more than a few hundred milliseconds should be moved to a background job. This includes sending emails, processing uploads, generating reports, and syncing with external systems. We typically use a combination of job queues (like BullMQ or Sidekiq) with proper retry and dead-letter handling.
Design your jobs to be idempotent from the start. Network failures and process restarts are inevitable, and your jobs need to handle being run multiple times without causing data corruption.
Observability and Monitoring
You can't scale what you can't measure. We instrument every application with distributed tracing, structured logging, and metrics collection. This gives us the visibility we need to identify bottlenecks before they become critical issues.
Set up alerting based on business metrics, not just technical metrics. Track things like user signup completion rate, time to first value, and feature adoption alongside your server response times and error rates.
Plan for Horizontal Scaling
Even if you're starting with a single server, design your application to run as multiple instances from day one. This means using external session storage, avoiding local file storage for uploads, and ensuring all background jobs can run on any server.
Container orchestration platforms like Kubernetes make horizontal scaling straightforward, but only if your application is designed for it. Test running multiple instances in your development environment early and often.
Conclusion
Building scalable SaaS applications is about making good architectural decisions early and maintaining them over time. Start simple, measure everything, and scale the components that need it. If you're building a SaaS product and need help with architecture or implementation, we'd love to hear from you.