Founders often treat MVP and prototype as interchangeable. They are not. A prototype is a quick and incomplete model that answers a narrow question. An MVP is a minimal product that real customers can use to complete a job end to end. The choice between them depends on your highest risk, the next proof point you need, and the constraints around launch, compliance, and budget.
Think of a prototype as a learning device. It can be a sketch, a clickable Figma file, a small code spike, or even a facade that simulates a response. You use it to check whether users understand a flow, whether a concept makes sense, or whether a technical approach might work. It is disposable. You design it to be thrown away once it teaches you what you need. An MVP sits on the other side of the spectrum. It must be deployable, supportable, and safe. It provides value from the first session, handles errors, stores data correctly, and gives you a path to iterate without starting over.
The fastest way to decide is to isolate the riskiest assumption. If you do not know whether anyone wants the idea, run with a prototype and focus on desirability. Show five to ten target users the flow. Ask them to complete the core task. Watch where they hesitate and where they light up. If desirability looks sound and the unknowns shift to reliability or operational load, lean toward an MVP. You will learn more from real usage patterns than from staged tests once the basic value is clear.
Evidence needs also guide the choice. Angels and design partners will often accept qualitative data from prototypes and interviews. Seed investors and enterprise buyers usually prefer live usage. If the next gate in your journey requires metrics like activation and week one retention, only an MVP will create them. That does not mean you must build a large surface. It means you should pick a single job, implement it fully, and keep the rest out of scope.
Constraints matter. A fixed launch date, a marketplace submission window, or a regulatory bar can tip the decision. If you operate in a space with privacy obligations, you may need to invest in an MVP with real logging, consent handling, and simple monitoring even if the surface is small. That is a reasonable trade if the cost of a compliance surprise would be severe.
Cost, time, and risk follow predictable patterns. Prototypes often ship in days or a couple of weeks and cost little more than focused design time or a short engineering spike. MVPs take several weeks for most consumer and SaaS products, sometimes longer for regulated areas. The risk that each reduces differs. Prototypes cut product risk. MVPs cut market and operational risk. Many teams fail by dragging prototype code into production because it feels faster. It pays to separate the two. Write the MVP with basic reliability in mind and keep the prototype as a lesson, not as a code base.
Common failures come from unclear problem statements and scope creep. If you cannot state the job to be done in a single sentence, you are not ready to build an MVP. If you plan to serve multiple personas or platforms in the first release, you are also increasing the chance of delay and muddled learning. Focus on one persona, one platform, and one acquisition channel, then observe what happens to activation, time to first value, and the first retention curve. Even a small data set will teach you more than a large backlog.
A simple path that works in many cases starts with ten interviews to capture pains and existing workarounds. Turn those insights into two or three concepts, then test each with a small prototype. Pick one concept, define the end to end job, and build the smallest production ready surface that completes that job. Ship it to a narrow audience, measure activation and early retention, and collect qualitative feedback in a structured way. Use a weekly report to document changes and decisions. After four to six weeks of iteration, decide whether to expand, pivot, or stop.
Investors want to see proof that you learn quickly and cut scope intelligently. Show the chain of evidence. Start with a short summary of interview insights, then the prototype findings, then the first MVP metrics. Add a page that lists what you removed and why. Close with a small dashboard that tracks activation, day one retention, week one retention, depth of use, and a few representative quotes from users who stayed and users who churned. This builds trust in your process and in your judgment.
In the end, it is simple. Use a prototype to answer a narrow question at low cost. Use an MVP to learn from real users and gather data you can act on. Move from one to the other with intent. Keep the surface small and the learning loop tight. That path preserves capital and increases the odds that you find something people will keep using.
