I’ve been thinking a lot about multi-tenant SaaS architecture lately, especially how to build systems that securely serve multiple customers from a single codebase while keeping their data completely isolated. The challenge isn’t just technical—it’s about building trust through proper security measures while maintaining performance and scalability. Let me share what I’ve learned about implementing this with NestJS, Prisma, and PostgreSQL’s Row-Level Security.
Setting up the foundation starts with understanding your database strategy. Are you using separate databases, separate schemas, or a shared database with RLS? Each approach has trade-offs, but RLS with a shared database often provides the best balance of security, performance, and maintainability for most SaaS applications.
Did you know that PostgreSQL’s RLS can enforce data access policies at the database level, making it nearly impossible for tenants to access each other’s data, even if there’s a bug in your application code? This defense-in-depth approach is crucial for building trustworthy multi-tenant systems.
Here’s how I typically configure the database schema with Prisma:
model Tenant {
id String @id @default(cuid())
name String
subdomain String @unique
users User[]
projects Project[]
}
model User {
id String @id @default(cuid())
email String
tenantId String
tenant Tenant @relation(fields: [tenantId], references: [id])
projects Project[]
@@unique([email, tenantId])
}
The real magic happens in the database policies. Here’s how I set up RLS:
CREATE POLICY tenant_isolation_policy ON users
USING (tenant_id = current_setting('app.current_tenant_id'));
But how do we ensure each request gets the right tenant context? That’s where middleware comes in. I create a tenant context middleware that extracts the tenant identifier from the request—whether it’s from a subdomain, JWT token, or custom header—and sets it in the database session.
@Injectable()
export class TenantMiddleware implements NestMiddleware {
constructor(private readonly prisma: PrismaService) {}
async use(req: Request, res: Response, next: NextFunction) {
const tenantId = this.extractTenantId(req);
if (tenantId) {
await this.prisma.setTenantContext(tenantId);
}
next();
}
}
What happens when you need to perform operations across multiple tenants, like system-wide analytics? For these special cases, I create separate service methods that bypass RLS temporarily, but only with proper authorization checks and audit logging.
Testing is another critical aspect. I always write tests that verify data isolation by creating multiple tenants and ensuring they can’t access each other’s data:
it('should not allow cross-tenant data access', async () => {
const tenant1Data = await service.getData(tenant1Context);
const tenant2Data = await service.getData(tenant2Context);
expect(tenant1Data).not.toEqual(tenant2Data);
});
Performance considerations are vital too. Proper indexing on tenant_id columns and connection pooling strategies can make a huge difference as your tenant count grows. I’ve found that using PgBouncer in transaction pooling mode works well with RLS.
Have you considered how you’ll handle tenant-specific customizations? I typically use a JSONB column for tenant settings and extend the RLS policies to include tenant-specific access rules when needed.
The beauty of this approach is that once you set up the foundation, adding new features becomes much simpler. Each new service automatically inherits the tenant isolation, and you can focus on building value rather than worrying about data leaks.
Remember that security is an ongoing process. Regular security audits, monitoring for unusual access patterns, and keeping dependencies updated are all part of maintaining a secure multi-tenant system.
I’d love to hear about your experiences with multi-tenant architectures. What challenges have you faced, and how did you solve them? If you found this helpful, please share it with others who might benefit, and feel free to leave comments with your thoughts or questions.