By Mehdi HassanWhat is the difference between a prototype and an MVP?
A prototype is a simulation of a product, and an MVP is a minimal working product. A clickable prototype is usually a set of Figma screens linked together so someone can tap through the main flow; nothing behind it is real. An MVP has real accounts, real data and real logic, so people can use it to do their actual job and, ideally, pay for it.
The two answer different questions. A prototype answers whether people understand the idea and want it. An MVP answers whether they come back. Founders get into trouble when they build the expensive one to answer the cheap question. Our MVP design and development guide covers what happens once you commit to building.
Prototype vs MVP vs proof of concept: how do they compare?
A proof of concept, a prototype and an MVP each reduce a different risk: technical, usability and market. The table compares them on what they prove, who they're for and what they typically cost in effort.
Proof of concept | Clickable prototype | MVP | |
|---|---|---|---|
Question it answers | Can this be built? | Do people get it and want it? | Will people use it and pay? |
Main audience | Founders, technical advisors | Users, investors | Real customers |
What it is | Throwaway code or a script | Linked designed screens | Working software |
Real data | Sometimes, narrow | No | Yes |
Relative effort | Low to medium | Low | High |
Changes are | Rewrites | Quick, in design tools | Developer time |
Ends with | A yes or no on feasibility | A validated flow and pitch asset | Retention and revenue data |
A proof of concept matters most for deep-tech and AI products where it's genuinely unclear whether the model, hardware or data pipeline can work. For most software startups, feasibility isn't the risk; demand is.
What do investors want to see at pre-seed: a prototype or an MVP?
At pre-seed, investors generally want evidence that you understand a real problem and can ship, and a clear clickable prototype backed by customer conversations often provides that. A working MVP with paying users is stronger, but a half-built MVP with no users is weaker than a sharp prototype and a list of people waiting for it.
A clickable prototype for investors works because it makes the product concrete in a two-minute demo. It shows the one moment of value without asking anyone to imagine it. If you're building one specifically for a raise, our guide to investor demo prototypes covers how to structure the click path so the demo lands.
Which should you build first? A stage-based verdict
Build a prototype first if you haven't yet proven people want the thing, and build an MVP first if they're already asking for it. The table maps common pre-seed situations to the build that fits.
Your situation | Build first | Why |
|---|---|---|
Idea stage, no customer conversations | Neither yet: interviews, then a prototype | You don't know what to prototype |
Problem validated, raising pre-seed | Clickable prototype | Cheapest asset that makes the pitch concrete |
Uncertain whether the tech works | Proof of concept | Feasibility is the risk |
Design partners or pilots signed | MVP | Users are waiting; retention is the next question |
Already raised, small team | MVP, from the tested prototype | Money is for learning from real usage |
Existing audience or waitlist | MVP, scoped tightly | You can measure use immediately |
Once you're building the MVP, the next decision is how much to cut. Our guide to how to scope an MVP covers that, and MVP design explains where design polish should go.
How do you make a prototype that turns into the MVP?
Make the prototype's core flow realistic enough that it can serve as the MVP's spec. That means real copy instead of lorem ipsum, the states a real user would hit and a structure built from components a developer can map to code.
Write the words first
Draft the headline promise, the button labels and the empty-state text before designing. Copy forces decisions the visuals let you dodge.
Prototype one flow end to end
From sign-up to the moment of value. Skip settings, team management and anything not on that path.
Include the states
Add the empty screen, a loading moment and one error. They make the demo believable and they're what developers need later.
Build on a component kit
Use components that exist in code, so the prototype maps directly to the build instead of being redrawn.
Test with a handful of target users
Watch them click through without help. Fix what confuses them before showing investors.
Hand the tested flow to development
The validated prototype becomes the MVP's first sprint, with decisions already made.
Whether to prototype in Figma or in code depends on how much the experience relies on real data and interaction. We compare the two in Figma prototype vs code prototype, and the wider question of fidelity is in prototyping for product teams.
Hypothetical example: one founder, two paths to a pre-seed round
For example, suppose a founder is building an AI tool that drafts insurance claim responses for small brokers.
Path A: she spends her savings on a coded MVP. It handles one claim type, has bugs in document upload and has no users when she starts pitching. Investors ask about traction and she has none.
Path B: she interviews brokers, prototypes the upload-to-draft flow in Figma with realistic copy and sample claims, and tests it with six brokers. Three agree to pilot. She pitches with the prototype and the pilot commitments.
Path B costs a fraction of Path A and gives a stronger story. After the raise, the tested prototype becomes the first build sprint.
When is a prototype not enough?
A prototype stops being enough once the open question is about behavior over time: retention, habit, willingness to pay or performance with real data. People are polite about prototypes. They say they'd use it, and only working software shows whether they actually do.
- Retention. You can't measure whether people return to a set of screens.
- Pricing. Stated willingness to pay is weaker than a card entered at checkout.
- Data-dependent value. If the value comes from the user's own data, sample data in a prototype hides the hard part.
- AI output quality. A prototype can fake a perfect answer; real users will see the real model.
- Later rounds. By seed and Series A, investors expect usage data, not demos.
If you're choosing a build approach for that MVP, compare no-code, low-code and custom code, and use the MVP development cost guide to budget it.
What mistakes do founders make with prototypes and MVPs?
The most common mistakes are building an MVP too early, polishing a prototype that answers the wrong question and treating the prototype as throwaway. Each one wastes either money or the learning you paid for.
- Coding before talking to customers. An MVP built on a guess is an expensive prototype.
- Prototyping every feature. A twenty-screen prototype is harder to test and to demo than a five-screen one.
- Fake perfection. A prototype with impossible AI results sets expectations the MVP can't meet.
- Ignoring the brand. A prototype that looks unfinished undercuts the pitch; see branding on a budget for a lightweight fix.
- Throwing away the learning. If the tested flow doesn't become the build spec, you'll repeat the same decisions twice.
