Are you trying to build a SaaS application?

Are you trying to build a SaaS application?
Imagine you are building a SaaS application like Zoom, Shopify, or Canva. All customers use the same app, but their data must stay separate, secure, and private.

How do we achieve that?
And the answer is Multi-Tenancy

Here, we'll explore what multi-tenant systems are, why they matter, and the three most common approaches - separate database, separate schema, and shared schema. By the end, you'll know the trade-offs of each and when to use them.

What is Multi-Tenancy?
Multi-Tenancy means a single application serves multiple customers (tenants), while keeping each tenant's data isolated.

Think of a Hotel:
The hotel building is the application.
Each room is a tenant’s data space.
Guests (tenants) share the same infrastructure (elevators, lobby, services), but rooms are private and isolated.

This approach makes SaaS platforms:
Cost-efficient (one codebase, many customers)
Easier to maintain (updates are global)
Scalable (can serve thousands of tenants)
But of course, it also comes with some challenges!

1. Separate Database
In this approach, each tenant is assigned its own separate database. This provides the strongest level of data isolation, as each tenant’s data resides entirely within its own database instance. As a result, there’s no risk of data leakage or accidental access between tenants, making it a highly secure and reliable architecture for multi-tenant applications.
Having separate databases also makes it easier to manage tenant data independently. For example, backup, restore, or maintenance tasks can be performed on a single tenant’s database without affecting others. Moreover, it enables independent scaling — if one tenant experiences higher traffic or needs additional resources, only that tenant’s database can be scaled up without impacting the performance of others.
However, this approach can become expensive and complex as the number of tenants increases, since each tenant requires a separate database instance. Managing multiple databases introduces additional overhead for deployment, monitoring, and database migrations, which can be challenging in large-scale systems.
To implement this architecture, we typically use database routers in Django. A database router dynamically determines which database to connect to based on the current tenant’s identity. For example, if the active tenant is tenant_A, the router will direct all database queries to the tenant_A_db database.
This database-per-tenant model is therefore best suited for applications that require strong data isolation, per-tenant performance control, and high security, even if that means higher operational cost and maintenance effort.


2. Separate Schema
Here, all tenants share a single database, but each tenant has its own dedicated schema within that database. A schema can be thought of as a logical container or namespace inside a database that holds a set of database objects such as tables, views, indexes, and functions. In simple terms, it helps organize and separate data within the same database.
This model offers a good balance between data isolation and cost efficiency. Since all tenants use the same database, it significantly reduces infrastructure and maintenance costs compared to the database-per-tenant model.
Each tenant’s data is stored in its own schema, ensuring logical isolation. For instance, if Tenant_A has a schema named a, then its tables can be accessed as a.users, a.products, and so on. This makes it easy to manage tenant-specific data while still benefiting from shared database resources.
However, this model also introduces some migration overhead. When database schema changes are required (such as adding a new column), the same change must be applied to all tenant schemas, which can become complex as the number of tenants increases. Additionally, not all database systems handle multiple schemas efficiently — databases like PostgreSQL can manage thousands of schemas, while others may face performance or management limitations.
Overall, the schema-per-tenant approach is best suited for mid-sized SaaS applications with a moderate number of tenants, where you need a balance between cost, performance, and data isolation.

3. Shared Schema
This is the simplest multi-tenant approach, where all tenants share the same database and the same schema. Tenant data is distinguished using a tenant identifier (for example, a tenant_id column) in each table.
Since there is only one database and one schema to manage, this model is cost-effective and easy to implement. It requires minimal setup and maintenance, making it a great starting point for small-scale applications or projects with limited resources.
However, this approach provides the weakest level of data isolation because all tenant data resides together in the same tables. Any mistakes in query filtering could result in data leakage between tenants, so extra care must be taken when handling queries.
To implement this model, you can include a tenant_id field in all tenant-specific tables. For example:
User table → id, name, email, tenant_id
Product table → id, product_name, tenant_id
Every query should then filter data based on the tenant_id to ensure that each tenant only accesses their own data.
Overall, if your priority is low cost and fast development, the shared schema model is the best choice for your project.

So if we conclude,
Separate databases give maximum isolation but at high cost.
Separate Schemas strike a balance between efficiency and separation.
Shared Schema is the simplest and cheapest, but demands careful handling to avoid leaks.

So which approach will you choose for your next SaaS project?