As a developer building scalable SaaS products, I’ve often grappled with the complexities of multi-tenancy. The challenge of securely isolating customer data while maintaining efficiency has led me to explore robust architectures using NestJS, Prisma, and PostgreSQL. This approach combines modern tooling with proven database strategies to create a solution that grows with your user base. Let’s explore how to build this together.

Multi-tenancy comes in different forms. The shared schema approach adds a tenant ID to each table - simple but risky. Database-per-tenant offers maximum isolation but becomes unwieldy at scale. My preferred balance is schema-per-tenant: good separation with manageable overhead. How would you handle customization needs across different customers?

Starting our project requires careful setup:

nest new saas-app
npm install prisma @prisma/client @nestjs/config
npx prisma init

We configure environment variables for our master database and tenant templates. The master database stores tenant metadata, while each tenant gets their own PostgreSQL schema. This separation keeps data isolated while sharing database resources.

Our Prisma schema defines two distinct models. The master schema tracks tenants:

model Tenant {
  id        String   @id @default(cuid())
  name      String
  subdomain String   @unique
  schema    String   @unique
  status    TenantStatus
}

Each tenant’s schema contains their application data:

model User {
  id       String @id @default(cuid())
  email    String @unique
  tenantId String
}

Notice the tenantId field? That’s our safeguard against data leaks between customers. But how do we ensure it’s always correctly applied?

The magic happens in our tenant context middleware:

@Injectable()
export class TenantMiddleware implements NestMiddleware {
  constructor(private tenantService: TenantService) {}

  async use(req: Request, res: Response, next: NextFunction) {
    const tenantId = req.headers['x-tenant-id'];
    if (!tenantId) throw new UnauthorizedException();
    
    const tenant = await this.tenantService.findById(tenantId);
    req['tenant'] = tenant;
    next();
  }
}

This intercepts every request, identifies the tenant from headers, and attaches the tenant context. Services then use this to scope database operations. What happens when a new customer signs up though?

Automated provisioning solves this:

async createTenant(name: string, plan: string) {
  const schema = `tenant_${cuid()}`;
  await this.prisma.$executeRaw`CREATE SCHEMA ${schema}`;
  await this.applyMigrations(schema);
  
  return this.masterDb.tenant.create({
    data: { name, schema, plan }
  });
}

New schemas are created instantly with migrations applied. For security, we implement row-level policies and strict connection pooling. Each request uses a dedicated database connection scoped to the tenant’s schema, preventing any cross-tenant data access.

Testing requires simulating multi-tenant environments:

describe('UserService', () => {
  beforeEach(async () => {
    await createTestTenant();
    switchToTenantContext('test_tenant');
  });

  it('creates user in correct schema', async () => {
    const user = await userService.create({ email: '[email protected]' });
    expect(user.tenantId).toEqual('test_tenant');
  });
});

We verify isolation by attempting cross-tenant operations that should always fail. For deployment, we use container orchestration with connection pooling and schema-aware monitoring. How might you handle schema changes across hundreds of tenants?

This architecture has supported my production applications through significant growth. The combination of NestJS’s modular structure, Prisma’s type safety, and PostgreSQL’s robust schemas creates a maintainable foundation. What challenges have you faced with multi-tenancy?

If you found this guide helpful, please share it with fellow developers. Your comments and experiences help us all build better systems. What aspects would you like explored deeper in future articles?