Multi-Tenant SaaS Development
Multi-tenant SaaS means multiple customer organizations share the same infrastructure while their data stays strictly separated. We design tenant isolation deliberately, row-based isolation across shared tables on a documented PostgreSQL platform, and we have direct experience finding and fixing a structural tenant-isolation flaw in an inherited codebase.
The scope, and what has to get right.
- Tenant isolation as a deliberate design decision. Row-level isolation across shared tables, or schema-per-tenant, chosen for the specific product rather than assumed by default.
- Domain-separated schema design. Distinct areas of the system get their own tables and rules before a query is written, so tenant boundaries are enforced structurally, not just by convention.
- Auditing existing multi-tenant systems. Reviewing an inherited platform's actual isolation model for structural flaws before adding new capability on top of it.
- Per-tenant configuration without per-tenant code. Company-specific settings and rules handled through configuration data, not a forked codebase per customer.
Inside the system, not bolted on.
Isolation model decided before schema design
Row-based isolation across shared tables, versus schema-per-tenant, is a deliberate architectural choice made early, not a default nobody examined.
EF Core for standard access, Dapper where it matters
Standard CRUD through Entity Framework Core, with Dapper used for performance-sensitive queries where the overhead of an ORM is not worth paying.
Structural review, not just feature review
When we take over an existing multi-tenant platform, we look specifically at how tenant boundaries are enforced, since that is where the costliest bugs hide.
Maintaining and extending a lone worker safety SaaS platform
- Found and fixed a structural multi-tenant flaw: Identified a structural issue in how the inherited codebase isolated data between tenant companies, and corrected the isolation model so each company's safety and account data stays properly separated on shared infrastructure.
- Automated missed-checkout detection: A background process continuously watches for missed checkouts with no manual oversight, triggering escalation in near real time rather than waiting for someone to notice.
- Added an automated phone check-out flow: Built a phone-based IVR flow through SignalWire: the system calls the worker, plays safety prompts, and confirms safety through a keypress, alongside the existing push and SMS channels.
- Subscription billing kept self-correcting: Chargify handles recurring billing with card capture fully delegated to its hosted pages, and a scheduled job syncs subscription state daily, deactivating employees at expired companies automatically.
- Added a background-check integration: Layered in PeopleConnect background-check lookups with per-user monthly usage caps, extending the platform's screening capability without disrupting the core monitoring engine.
Common questions.
Row-based isolation or schema-per-tenant, which is better?
It depends on scale and compliance needs. Row-based isolation across shared tables keeps infrastructure simpler and is what we have used in production; schema-per-tenant gives stronger separation at the cost of more operational complexity.
Can you audit our existing multi-tenant system for isolation flaws?
Yes. We have direct experience identifying and fixing a structural tenant-isolation issue in a live, inherited multi-tenant platform, and the same review approach applies to any existing system.
Does each tenant need its own deployment?
No, that defeats the purpose of shared infrastructure. Our approach keeps tenants on shared infrastructure with isolation enforced at the data layer, not through separate deployments per customer.
What database do you use for multi-tenant platforms?
PostgreSQL is our documented choice for at least one production multi-tenant platform, though the same isolation principles apply on SQL Server.
How confident are you in your current tenant isolation?
Tell us how your system separates customer data today and we will tell you honestly whether it holds up.