MVP definition and product judgment
Clarify the difference between a demo, a prototype, an MVP, and a production SaaS product.
A media lab for SaaS MVPs
AI-coded products are not automatically MVPs
AI tools can help you build something that runs, demos well, and even feels complete. But a working demo is not the same thing as a real SaaS MVP.
MVP Learning Loop
Turn a demo into a system that learns.
- 1
Hypothesize
- 2
Build
- 3
Launch
- 4
Observe
- 5
Analyze
- 6
Learn
- 7
Iterate
What a demo usually lacks
Auth, multi-tenancy, subscriptions, analytics, feedback, error monitoring, and basic security.
Our thesis
An MVP is not “code that runs.” It is a learning system.
AI coding has lowered the cost of making demos. It has not lowered the bar for product validation. A real SaaS MVP should be built around a clear business hypothesis and keep answering: who the users are, how painful the problem is, whether they will use and pay for it, and which features drive activation and retention.
Core hypothesis
Real demand
User behavior
Conversion path
Feedback system
Willingness to pay
What is saasmvp.dev?
A site that dissects real SaaS products as they become MVPs
We will use a complete example SaaS product as the lab subject and publicly break down its path from idea to MVP.
This is not just about how to write code. The deeper questions are why the product should exist, what the core hypothesis is, what version one should include, how the user journey works, how payments and analytics fit in, and how to decide whether the product is worth continuing.
What we will publish
Breaking SaaS MVPs into systems that can validate, operate, and iterate
Example SaaS lifecycle breakdown
From idea source, target users, and scope decisions to auth, multi-tenancy, payments, feedback, and launch strategy.
Tech stack and architecture analysis
Break down auth, workspaces, permissions, Stripe, webhooks, PostHog, Sentry, and the minimum admin console.
User analytics and conversion analysis
Define activation events, build funnels, observe retention, and use data to choose the next iteration.
Monetization and growth experiments
Test pricing, trials, channel quality, and the tracking foundation required before paid acquisition.
Security, reliability, and maintainability
Build the basic trust layer real users expect: permission boundaries, data isolation, backups, monitoring, and privacy pages.
Who should subscribe?
For founders turning AI demos into real SaaS products
You are building a product with AI coding tools
You have a demo but are unsure what comes next
You want to turn an idea into a launchable SaaS
You want to learn auth, multi-tenancy, payments, and subscriptions
You want to understand behavior, conversion, and retention
You plan to acquire users through content, SEO, ads, or TikTok
Join the list
Follow the public breakdown of saasmvp.dev
Leave your email or answer a few quick questions. I will use the responses to shape the first public breakdowns.