You have the UI in Figma and you need an interactive prototype to test it with users or present a new idea to your team. That usually sparks an internal debate about which tool or workflow should turn your static Figma designs into a prototype that helps you explore the solution.
First, ask: what is the prototype for? Map research, tone testing, validating a flow with real data, speeding up implementation, and so on.
Prototype requirements
Once the goal is clear, and before you pick a tool that claims to meet it, decide which requirements the prototype must satisfy. Some are technical; others are constraints that determine whether you can actually answer your question.
Before listing tools, lock the same criteria. Not to score every product, but to avoid stuffing different goals under the same “prototype” label.
| Requirement | What to look for |
|---|---|
| 🗺️ Scope | A single screen or many |
| ⚡ Speed | How quickly do you need it? |
| 🔗 Data | Dynamic or static data? |
| 🎮 Interaction level | Simulation, or real browser behavior? |
| 🎨 Fidelity level | Your style, tokens, and components, or a generic look and feel? |
| 📤 Shareable | Share with people inside or outside your organization? |
| 🔄 Updates | Will the design need further updates or iterations? |
With that on the table, tools stop competing with each other and group by the kind of question they answer well.
My recommendation: pick two or three requirements as the primary ones the prototype must meet. That keeps you focused on what is truly necessary. If you try to cover too many, you may embark on a journey longer than the Odyssey and burn through both time and patience.
The next step is to look at the available tools. Each one has its own features, but they can be organized into types that share common strengths and trade-offs.
Types of tools
Grouping by type avoids comparing Replit to Figma Prototype as if they solved the same problem. They are different paths with different trade-offs.
1. Native click-through
You connect designs with simple events (a mouse click or a key press) to simulate interaction. These prototypes can include basic animation as well.
When yes:
You want simple navigation or interaction using your existing designs.
When no:
You have many screens or distinct flows, you need richer interaction, or you need dynamic data.
Tools: Classic Figma Prototype
2. Advanced interaction
Advanced tools for prototypes with complex micro-interactions, custom animation, and fine-grained behavior control. They often offer timelines, conditional logic, advanced events, and sensor or hardware support, so you can simulate the visual experience in detail.
When yes:
Interaction or animation control is the main goal.
When no:
The prototype will need constant updates, requires dynamic data, or has too many interactive elements to manage.
Tools: ProtoPie, Principle, Origami
3. Prototype = Site
These turn your designs into a navigable website you can share online as-is. They usually make instant publishing, visual tweaks, tone experiments, and empty-state editing easy on the web. They work well for quick experience, copywriting, and visual communication tests, with limits for complex projects or native apps. You can think of them as an advanced version of click-through prototyping.
When yes:
Static content sites with moderate interaction, for desktop or mobile.
When no:
The prototype needs meaningful logic or dynamic data.
Tools: Figma Sites, Framer, Webflow
4. Figma to code (plugins)
These turn designs into working front-end code, exporting components or screens to frameworks such as React, Vue, or HTML and CSS. They speed up interactive prototypes and project scaffolding, though you usually need manual polish to reach production quality.
When yes:
You want to see the interface in a real browser quickly and accelerate the path toward front-end scaffolding.
When no:
You need a strict design system or real data.
Tools: Anima, Locofy, Builder.io
5. AI app builders
These generate a clickable app from a design or a prompt, often with real state, routing, and forms, in minutes. The prototype feels like a product, but the code is usually disposable: a generative monolith with weak design-system fidelity unless you invest serious time.
When yes:
You need to validate a complex flow with data, errors, loading states, or permissions now, and disposable is fine.
When no:
You need a production foundation, a strict design system, or a prototype that will scale.
Tools: Figma Make, Lovable, Bolt, Replit, v0
6. Custom build with vibe coding (IDE + Figma MCP + AI agent)
You use an IDE (Cursor, VS Code + Copilot, and so on) with Figma MCP or Dev Mode to build the prototype in your repo. You get flow fidelity similar to an AI builder, but with craft that can respect the design system. The heavy cost is the initial setup, not every prototype after that.
When yes:
You want full control and reusable code.
When no:
You only need speed, or you do not yet have a solid base to build on.
Tools: Figma MCP + Cursor / Claude Code / Claude Design
One note: the cost sits in the setup (repo and design system the first time), not in every prototype. If that foundation is already in place, this option can be very fast, with craft that lasts.
Use cases
A taxonomy only helps if you can apply it to a real situation. These examples turn each tool type into a concrete decision: goal → tool type → why.
Of course, intermediate cases will not always fit cleanly into one row. Revisit your requirements, or pick the tool type that covers them as far as possible, even if it is not a perfect 100% match.
| Prototype goal | Fitting tool type |
|---|---|
| Validate navigation in a 5-screen onboarding | Native click-through |
| Test the feel of a swipe + haptics on mobile | Advanced interaction |
| Test tone on a landing page and empty states | Prototype = Site |
| Show engineering a front-end scaffold | Figma to code (plugins) |
| Validate a flow with errors and permissions with customers this week | AI app builders |
| Prototype that reuses the product design system | Custom build / vibe coding |
After the prototype: the lifecycle
Choosing the tool is only half the work. What you do after you have the prototype decides whether you learned something useful, or just built craft headed straight for the trash.
- Separate what you measure from what you ignore. Decide in advance which feedback you will evaluate and which you will discard. In a tone test, a navigation bug or a missing interaction in a search field does not invalidate the prototype. That also helps you avoid adding more detail than you actually need.
- Document the learning. Record the prototype goal, the requirements you defined, the method you used, and what you learned. The Figma file or the repo will be forgotten; the decision should not.
- Decide what to throw away and what to reuse. Before you discard the prototype and leave it forgotten in a drawer, decide whether any part is worth keeping. Happy accidents happen. Document or save them for later when they do.
What usually goes wrong
The recurring mistakes are not about tools. They are about match:
- Jumping to Prototype = Site or AI app builders “because it’s cool” when Native click-through was enough for the map.
- Treating AI app builders as a research tool for brand, tone, or visual craft.
- Dismissing Custom build / vibe coding as “slow” when the repo or design system is already in place and a simple prompt can generate the project.
- Mixing two (or three) goals in a single test: flow + tone + micro-interaction. You end up with too much to polish and feedback that is hard to process.
- Asking for “pixel perfect” feedback where visual fidelity was never a requirement.
- Choosing Custom build / vibe coding without a ready repo or design system, and then getting frustrated by the setup cost.
- Using Native click-through or Advanced interaction for a long flow with many screens when another type would fit better.
- Confusing “feels like a product” with “validated for production.” Clickable plus data does not make a stable foundation.
- Never deciding whether the prototype should be thrown away or kept, and leaving orphaned code or files nobody wants to touch.
Takeaways
There is no winning tool. There is a match between the question you want answered and the tool type that answers it without contaminating the result.
The prototype is not the deliverable. The deliverable is the well-chosen question, and the learning you document afterward.
Quick checklist
Before you open the next tool, run through this:
- What is the single question this prototype must answer?
- What feedback will you deliberately ignore so you do not mix hypotheses?
- What are the two or three non-negotiable requirements (scope, data, fidelity, speed…)?
- Which tool type fits that question, without forcing the trendy one?
- Who will consume the prototype: research, stakeholders, engineering, customers?
- When the session ends: what gets documented, and what gets discarded?
- Should the prototype be thrown away after the test, or reused?
If you can check those boxes honestly, the logo on the tool barely matters.