How a SaaS keeps each customer's data separate

A tenant is one customer of the SaaS product, together with their users and data. A company that uses your platform is one tenant, and its people see the product as if it were their own. In reality, the same software serves dozens or hundreds of tenants at the same time.

That is why tenant isolation matters so much. The AWS Well-Architected SaaS Lens calls it a foundational topic that every SaaS provider must deal with. It adds that a breach of this boundary, in any way, would be a serious and probably irreversible event for a SaaS business. A customer who sees another customer's data loses trust in a single moment.

In the silo model, each tenant has their own dedicated resources. AWS gives two examples: a completely independent set of infrastructure for each tenant, or a separate database for each one. When some or all of a tenant's resources are dedicated to them, that is silo.

Even then, AWS points out that identity, onboarding and operations stay shared by everyone. This is what sets a SaaS apart from a service where each customer runs their own copy of the product. The gain is strong separation, because the data sits in a different place. The cost is more work to run the system, because every change has to reach every set of resources.

In the pool model, tenants share the same resources. AWS calls it the most classic meaning of multi-tenancy: shared, scalable infrastructure that brings economies of scale, easier management and speed of change. The shared resources can be computing power, storage or message queues.

Here the separation does not come from the structure. It comes from the code, which has to filter by tenant in every query to the database. If a filter is missing from one screen, data from another customer can show up. That is why pool needs systematic checking, with tests that look for exactly this mistake.

Bridge is the combination of the two. AWS notes that SaaS products are not always purely silo or pool, and that many systems are a mix. For example, one service of the system can be silo and another can be pool.

What leads to silo? AWS names the regulatory profile of the data and the "noisy neighbor" effect, where one tenant with a heavy load slows down the others. What leads to pool? Flexibility, the way the system is accessed, and cost. In practice, data with higher requirements stays separate, while the other features share resources.

We choose based on the requirements of the project, not on a preference for a technology. In the first stage we write down three things: what data is stored, how many tenants are expected, and what your customers ask of you. With those, the comparison becomes simple.

The result is written into the system design that you approve. This way you know from the start what each option costs, and the discussion does not happen later, when the code already exists.

In our own product, Praion AI, each customer has their own database. We describe it in detail in the Praion AI case study. It holds conversations, contacts and private notes, data that a customer wants to know belongs only to them. That is why the separation was built into the structure and not left to the attention of whoever writes the code.

The choice has a cost, and we accepted it knowingly: every update has to reach all the databases. In another project, with less sensitive data and many small customers, the same reasoning could lead to pool. This is the logic we follow, not one fixed recipe. The next question, which user inside each customer sees what, is explained in the article on roles and permissions. Together, the two decisions define who has access to which data.