Figma prototype or AI-coded prototype: how to choose for your startup

Use a Figma prototype when you are still deciding what the product should be, and an AI-coded prototype when the idea is settled enough to test real behavior. Figma is faster for exploring layout and flow. Code is better for anything with data, logic, or motion. Most teams need both, in that order.

The real question is what you need to learn

"Figma or code" sounds like a tooling debate. It is not. It is a question about what you are trying to find out before you spend engineering time.

A prototype exists to answer a question cheaply. Does this flow make sense? Will a user understand this screen? Does this feature feel useful once real data is in it? Different questions need different prototypes, and picking the wrong one is how teams burn two weeks learning something they could have learned in two days.

Before opening any tool, write the question down in one sentence. If the sentence starts with "how should this look" or "where should this go," you are in Figma territory. If it starts with "how does this behave" or "will this hold up with real input," you need code.

When a Figma prototype is the right call

Figma still wins in the early, fuzzy phase. Three situations stand out.

You are exploring structure

When you are deciding between three navigation models or two ways to organize a dashboard, speed of iteration matters more than fidelity. In Figma you can lay out five directions in an afternoon, compare them side by side, and throw four away without feeling bad. Code makes people attached to what they built, even when it is wrong.

You need alignment before anyone builds

A shared frame everyone can comment on is hard to beat. Founders, product leads, and engineers can point at one screen and say "this, not that." If the point of the prototype is to get a team to agree, a static or lightly linked Figma file is the clearest artifact.

The product is mostly screens, not logic

A marketing site, a simple onboarding flow, or a settings area has little behavior to test. Click-through prototypes cover it well, and the extra setup of code adds cost without adding learning.

When an AI-coded prototype is the right call

AI coding tools changed what a prototype can be. A working version with real state, real inputs, and real transitions now takes hours, not weeks. That shifts the line.

Your product depends on data

Dashboards, search, filters, tables, and anything that reacts to user input look fine in Figma with perfect sample data. They look different with 4,000 rows, a long company name, and a half-empty account. A coded prototype shows you the ugly cases while they are still cheap to fix. This is also where a lot of SaaS dashboards fail, as we covered in why nobody uses your SaaS dashboard.

Your product involves AI behavior

If your feature streams responses, shows uncertainty, or handles errors from a model, a static screen hides the hard part. Latency, partial output, and failure states are the design. You can only judge them by running them. For more on that side, see how to design AI features users trust.

Motion and interaction carry the experience

Transitions, drag behavior, and micro-interactions are hard to judge from a Figma link. A coded prototype lets a founder feel the product in their hands, which changes the quality of feedback you get from users and investors.

You need to test with real users

A prototype that behaves like the product gets more honest reactions. People stop politely clicking through screens and start trying to do their actual job.

Where coded prototypes go wrong

Speed creates its own traps, and we see the same ones repeatedly.

The first is mistaking a working prototype for a finished design. A prototype that runs can look polished while hiding weak decisions in hierarchy, copy, and spacing. If you have ever looked at an AI-built product and felt something was off but could not say what, that is the pattern we describe in why your AI-built product looks vibe-coded.

The second is skipping the design system. When every screen is generated separately, you get ten slightly different buttons. Set tokens, type, and spacing first, then generate against them.

The third is treating the prototype as production code. Prototype code is built to teach you something. It is usually not built to scale, be tested, or be maintained. Decide up front whether it is a learning tool or the first draft of the real thing, and tell the engineers which one it is.

The fourth is letting the tool make product decisions. AI tools will happily build whatever you describe, including the wrong feature. They do not push back. Someone still has to decide what is worth building.

If you are stuck on any of these and want a second opinion on your prototype, send us a message and we will tell you what we see.

A simple decision guide

Use this as a starting point, not a rule.

Situation	Better starting point
Choosing between layouts or flows	Figma
Getting a team to agree on direction	Figma
Marketing site or simple onboarding	Figma
Data-heavy screens (tables, filters, dashboards)	Coded prototype
Features with AI responses or async states	Coded prototype
Motion-led or highly interactive experiences	Coded prototype
Testing with real users on real tasks	Coded prototype
Pitching an idea before it is defined	Figma, then code

Most real projects follow the last row. Explore in Figma, narrow to one direction, then build that direction in code to test what Figma cannot show.

How the handoff changes

A coded prototype changes what you hand to engineering. Instead of a spec that describes behavior, you hand over something that already demonstrates it. That cuts down on misreadings, but it only works if the design decisions are documented. Keep a short note on what is decided, what is placeholder, and what is intentionally rough. Our design handoff checklist covers what to include.

How we use both at Artone

We use AI to explore more directions and to get prototypes running in code early. The taste and product judgment stay with us. We work through the product question with the founder first, then choose the fastest prototype that can answer it. Sometimes that is a Figma file in a day. Sometimes it is a working build.

One example of where code was the right medium: for Alkera AI, we built a tool that generates brand patterns as part of their full brand identity, delivered in a fixed two-week sprint ahead of their Y Combinator launch. A static set of patterns would have been a smaller deliverable. A tool the team could keep using was more useful to them.

The pattern holds across product work. Use the cheapest medium that answers your question, then move up in fidelity only when you need to.

What to do before your next prototype

Write the question you need answered. Pick the medium that answers it fastest. Set your design tokens before you generate anything. Decide whether the result is a learning tool or the start of production code. And keep one person accountable for the product decisions, because the tools will not.

If you want help deciding what to build and how to test it, book a call with us. We will look at where your product is and tell you which kind of prototype gets you to a decision fastest. For more on scoping early work, read how much design an MVP needs.