A SaaS is a different animal than a marketing site
A website mostly needs to look right and load fast. A SaaS product has persistent user data, accounts, permissions, and usually billing — meaning mistakes are no longer just cosmetic, they can leak or corrupt someone's data. That difference should change how you sequence the build, not just how careful you are at the end.
Start with the data model, before any UI
Before prompting for a single screen, get clear on what data your product actually stores and how it relates: what belongs to a user, what belongs to an account or team, what's shared. Getting this wrong early is expensive to fix later, because every feature you build on top assumes a certain shape. Ask your AI tool to propose a schema from a plain-language description of your data, then review it yourself line by line before writing any application code against it.
Auth and billing: don't vibe these from a blank prompt
Authentication and payments are two areas where subtle mistakes have outsized consequences — a broken permission check can expose another user's data, and a billing bug can charge someone incorrectly. Use established, well-documented authentication and payment providers rather than asking an AI to design a custom scheme from scratch, and read the generated integration code carefully rather than assuming it's correct because it compiles.
Build the core workflow first, not every feature
Pick the one thing a user does that makes your product worth using, and get that working end to end — sign up, do the core action, see the result — before adding secondary features. This gives you a real, testable slice of the product early, instead of a pile of half-finished features that don't add up to anything usable yet.
Testing matters more once there's real user data involved
A bug in a prototype is annoying; a bug that touches another user's account or data is a real incident. As soon as your app has multiple accounts, add tests around permission boundaries specifically — can user A see or edit user B's data — because these are exactly the mistakes that are easy for an AI (and a human) to introduce quietly during a fast iteration.
Security checkpoints before you let anyone sign up
Before opening access beyond yourself, walk through a basic security pass: are secrets kept server-side, is user input validated before it hits your database, are permission checks enforced on every route that touches data, not just the ones with a visible UI for it. Our dedicated guide to AI coding security best practices goes through this in more detail and is worth reading before launch, not after an incident.
A realistic build order
- Data model and core schema, reviewed by you before any UI is built
- Authentication, using an established provider rather than a custom scheme
- The single core workflow, end to end, with basic tests around it
- Permission and access-control checks for anything involving multiple users
- Billing, if applicable, using a payment provider's tested integration path
- Secondary features, added incrementally once the core is solid
- A security pass and real device/browser testing before opening signups