Multi-tenancy is one of the first architectural decisions you make when building a SaaS product, and one of the hardest to change later. Get it right early, and scaling is straightforward. Get it wrong, and you're looking at a painful rewrite.
We've built multi-tenant systems for over a dozen SaaS products. Here's what we've learned.
The Three Approaches
There are three common patterns for multi-tenancy in databases:
1. Shared Database, Shared Schema
Every tenant's data lives in the same tables, distinguished by a tenant_id column. This is the most common approach and the one we default to.
Pros: Simple to implement, easy to maintain, cost-efficient.
Cons: Requires discipline to always filter by tenant_id. A missing WHERE clause means data leaks.
2. Shared Database, Separate Schemas
Each tenant gets their own database schema (namespace). Tables are identical but isolated.
Pros: Stronger isolation, easier per-tenant backups.
Cons: Schema migrations are painful — you need to migrate every schema.
3. Separate Databases
Each tenant gets their own database instance.
Pros: Maximum isolation, per-tenant performance tuning.
Cons: Expensive, complex connection management, migration nightmare at scale.
Our Default: Shared Schema With Row-Level Security
For most SaaS products, shared schema with a tenant_id column is the right call. It's simple, cost-effective, and scales to thousands of tenants without issues.
The key is preventing accidental cross-tenant data access. We use two layers of protection:
- Application-level middleware: Every database query is automatically scoped to the current tenant. We use Prisma middleware or a custom query wrapper that injects the tenant filter.
- PostgreSQL Row-Level Security (RLS): As a safety net, we enable RLS policies that enforce tenant isolation at the database level. Even if the application layer has a bug, the database won't return another tenant's data.
Practical Implementation
Here's how we structure it in practice:
- Every table that holds tenant-specific data has a
tenant_idcolumn with a foreign key to the tenants table - A composite index on
(tenant_id, id)ensures queries are fast - The tenant is resolved from the authenticated user's JWT at the middleware level
- All queries go through a tenant-aware data access layer — never raw queries against tenant data
When to Consider Separate Schemas
We move to separate schemas when:
- Enterprise customers require contractual data isolation
- Individual tenants need different database configurations
- Regulatory requirements mandate physical separation
But this is the exception, not the rule. Start with shared schema, and only add complexity when you have a concrete reason.
The Takeaway
Don't over-engineer multi-tenancy. A tenant_id column with proper middleware and RLS handles 90% of SaaS products. The other 10% will know they're in that category because their customers are explicitly demanding it.