MVP (Minimum Viable Product)
The smallest version of a product that delivers enough value to attract early adopters and generate validated learning about your core assumptions.
What is an MVP?
An MVP (Minimum Viable Product) is the version of a product with the minimum set of features needed to test a core hypothesis with real users. The goal is not to ship something small — it's to learn as fast as possible whether the product idea is worth building.
Eric Ries, who popularised the term in *The Lean Startup*, defines it as: "That version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort."
What an MVP is not
| Common misconception | What it actually means |
|---|---|
| A buggy half-finished product | The smallest thing that *works* for the target user |
| Version 1.0 | A learning vehicle — may not even be code |
| The same for every product | Depends on the hypothesis being tested |
Types of MVPs
- Concierge MVP — do the service manually before automating it
- Wizard of Oz MVP — looks automated to users, is manual behind the scenes
- Landing page MVP — test demand before building anything
- Prototype MVP — clickable Figma with no real backend
- Single-feature MVP — ship one core feature and measure retention
How to scope an MVP
- State the hypothesis: "We believe [user] has [problem] and will [behaviour] if we solve it with [solution]."
- List the minimum features needed to test that hypothesis.
- Cut everything that doesn't directly test the hypothesis.
- Define the success metric before you build.
Free templates for MVP
Frequently asked questions
Should an MVP be publicly launched?
Not necessarily. Some MVPs are tested with 10–20 users. The key is that they're real users with real needs — not friends and family who give polite feedback.
What's the difference between an MVP and an MMP (Minimum Marketable Product)?
An MVP tests a hypothesis — it's a learning tool. An MMP is the smallest version you'd market publicly and charge for. MMP comes after MVP once you've validated the core assumption.
Apply MVP to your real product data
PMRead ingests customer feedback, interviews, and Slack threads — and generates PRDs grounded in real evidence.
Related terms
Product-Market Fit
The degree to which a product satisfies strong market demand — characterised by organic growth, high retention, and users who would be 'very disappointed' if the product disappeared.
Product Discovery
The process of determining what to build — through user research, prototyping, and testing — before committing engineering resources to delivery.