Database Design Decisions That Make or Break Your SaaS
Ryan Schaller
Principal, Coolradish
Your database schema is the foundation of your SaaS product. Get it wrong, and you'll spend months migrating data instead of building features. Get it right, and your database scales effortlessly as you grow from 10 to 10,000 customers. The decisions you make in week one determine whether you're successful in year three.
Multi-Tenancy: The Make-or-Break Decision
How you handle multiple customers in your database affects everything: performance, security, compliance, and scalability. Single database with tenant_id columns is simple but risky—one bad query brings down all customers. Database-per-tenant offers isolation but operational complexity. Schema-per-tenant splits the difference. For most B2B SaaS, start with tenant_id and row-level security. Migrate to database-per-tenant only when you have enterprise contracts demanding it.
Indexing Strategy From Day One
Indexes are free performance until they're not. Every index speeds up reads but slows writes. Start by indexing foreign keys, columns in WHERE clauses, and anything used for sorting. Use composite indexes for common query patterns. Monitor slow query logs religiously—they tell you exactly what to index. But resist the urge to index everything 'just in case.' Each index costs storage and write performance.
Schema Migration Strategy
Your schema will change constantly in the first year. Use a migration tool (Flyway, Liquibase, or framework-native options) from day one. Write migrations that work forward and backward—you will need to rollback. Test migrations on production-sized datasets in staging. Never modify columns with data—add new columns and migrate gradually. The best migration is one that doesn't require downtime.
When to Denormalize
Normalization reduces redundancy; denormalization improves performance. Start normalized. Denormalize only when you have measured performance problems. Common patterns worth denormalizing: calculated fields used in every query, join-heavy queries on hot paths, and read-heavy data that rarely changes. Maintain both normalized and denormalized data with triggers or application logic. Accept eventual consistency where appropriate.
Scaling Strategies: Read Replicas and Sharding
Read replicas are your first scaling tool—they're easy and effective for read-heavy SaaS apps. Route analytics and reporting to replicas. Keep writes on the primary. Sharding comes later, usually when you have specific customers large enough to justify dedicated infrastructure. Horizontal scaling through application-level sharding (like tenant-based routing) is simpler than database-level sharding. Consider managed databases that handle scaling for you before rolling your own.
Key Takeaway
Database design isn't about using the newest NoSQL database or the most normalized schema. It's about understanding your access patterns, planning for growth, and making pragmatic trade-offs. Start simple with proven technologies (PostgreSQL is usually the answer). Measure everything. Optimize the slow queries. Scale when you have to, not when it's theoretically needed. Your database should enable your product, not constrain it.
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