A SaaS product is a specific kind of web application, and the parts that make it distinctly "SaaS" — subscription billing, multiple customer accounts sharing one codebase, and churn as an ongoing business metric — aren't covered by general web app development advice. This fills in that gap specifically, assuming the general stack and MVP timeline questions are already covered elsewhere.
Building subscription billing logic from scratch — proration, failed payment retries, plan upgrades/downgrades, tax handling — is a surprisingly deep problem that most SaaS products should not build themselves. Stripe Billing (or a similar managed billing platform) handles this correctly out of the box, and the engineering time saved by not rebuilding it is almost always worth the platform's percentage fee, especially pre-scale. Custom billing logic is worth considering only once volume and specific pricing-model needs genuinely exceed what a managed platform supports.
| Model | Description | Best for |
|---|---|---|
| Shared database, shared schema | All customers' data in the same tables, separated by a tenant ID column | Early-stage products prioritizing simplicity and fast iteration |
| Shared database, separate schemas | Each customer gets their own schema within one database | Products needing more data isolation without full infrastructure separation |
| Separate databases per tenant | Each customer has an entirely isolated database | Enterprise customers with strict compliance/data isolation requirements |
Most early-stage SaaS products should start with the simplest shared-schema model — it's faster to build and sufficient for the vast majority of use cases. Migrating to stronger isolation later, once a specific enterprise customer's compliance requirement demands it, is a real but manageable engineering project; over-engineering isolation from day one for hypothetical future enterprise customers delays getting an actual product to market.
Every SaaS product needs a marketing site and dashboard shell around the custom logic. UIXDraft's SaaS templates cover exactly that scaffolding, so development time goes toward your actual product differentiation.
See the Templates →Unlike a one-time-purchase product, a SaaS product's health is measured by whether customers keep paying, not just whether they initially bought. Monthly churn of 5% sounds small but compounds to roughly 46% annual customer loss if unaddressed — meaning nearly half your customer base needs replacing every year just to stay flat. Early product decisions (onboarding quality, time-to-first-value, whether the product delivers on its core promise quickly) affect churn more than almost any feature added later, which is why a tight, well-executed MVP core feature usually matters more than a broad feature set at launch.
Per-seat, usage-based, and flat-tier pricing each fit different product types — per-seat works well when value scales with team size, usage-based fits products where cost should track actual consumption, and flat tiers suit products with a clear, bounded value proposition regardless of team size. Changing pricing models after launch is possible but disruptive to existing customers, so it's worth deliberately choosing based on how your product's value actually scales, rather than defaulting to whichever model is most common in your specific market.
Almost never at the start. Managed platforms like Stripe Billing handle proration, failed payments, and plan changes correctly out of the box — the engineering time saved is worth the platform fee until volume and needs genuinely exceed what it supports.
A shared database with a tenant ID column is the simplest and fastest to build, and sufficient for most early-stage products. Stronger isolation models are worth adopting once a specific enterprise customer's compliance requirement genuinely demands it.
SaaS revenue depends on customers continuing to pay every period, not just an initial purchase. Even modest monthly churn compounds significantly over a year, making early product decisions that affect onboarding and time-to-value critical to long-term revenue.