dMaya vs Figma AI: AI-Native vs AI-Assisted

Figma is the industry standard for design. Millions of designers use it every day. And Figma has been adding AI features, most notably Figma Make, which generates mockups from text prompts. That is a real step forward.

But there is a fundamental difference between a manual tool with AI bolted on and a platform built around AI from the start. Figma Make generates Figma layers. dMaya generates HTML/CSS/Tailwind that goes straight to coding agents. That difference shapes everything about how each tool fits into your workflow.

Figma's Approach to AI

Figma is, at its core, a manual design tool. You create frames, drag elements, set constraints, build auto-layouts. Figma Make adds AI generation on top of this. You type a prompt, and it creates design mockups and prototypes using Claude Sonnet 4 as the underlying model.

The output is Figma layers. Not code. Not a deployable app. Figma Make can pull styling from your existing design system libraries, and you can set custom rules and frame-based guidance to steer the results. That is useful if you already have a mature Figma setup.

The catch is credits. Each Make generation costs 30 to over 100 credits depending on complexity. Credit limits per seat range from 500 on Starter to 4,250 on Enterprise. The standalone Figma Make beta runs $20 per month. Overages hit through a shared pool add-on at $120 to $240 per month or $0.03 per credit. Credits can burn fast on a busy team.

Results can also feel template-like, and you get limited control over spacing and hierarchy. You still need design skills to refine what Make produces.

dMaya's Approach to AI

dMaya was built from scratch as an AI-native platform. There is no manual canvas to learn. You describe what you want in plain language, and the AI generates complete, multi-screen designs for you. If something is not right, you tell it what to change. The whole interaction is conversational.

This is what we call vibe design. You focus on the intent and the outcome. The AI handles the execution. No pixel-pushing, no manual frame setup, no learning curve measured in months.

The critical difference in output: dMaya generates HTML, CSS, and Tailwind code. Not design layers. That code goes straight to coding agents like Cursor, Claude Code, or v0. There is no extra step to translate a design file into something developers can use.

Side-by-Side Comparison

When Figma Still Makes Sense

Figma is a mature tool with a large ecosystem. If your team already has established Figma workflows, libraries, and processes, there are situations where it still makes sense to use it.

• Your team is already invested in Figma with mature libraries and workflows
• You need pixel-level manual control over every design detail
• You are a trained designer who thinks in frames, grids, and constraints
• You want the largest plugin ecosystem for design tools
• You need advanced features like auto-layout variants and interactive prototyping

Where dMaya Wins

dMaya shines when speed matters, when non-designers need to contribute, and when the output needs to be code, not design files.

• You want to generate complete designs from text, not draw them by hand
• Non-designers on your team need to create UI concepts (PMs, founders, developers)
• You need a client presentation workflow with preview links and inline feedback
• You want to generate mockups and export clean code without translating design layers first
• You want code output that goes directly to coding agents, not Figma layers

Can You Use Both?

Yes, and many teams will. The two tools serve different stages of the design process. Use dMaya for rapid concept generation and client review. Get alignment on direction quickly, collect feedback through shareable links, and iterate through conversation.

If you then need detailed manual refinement, bring the approved direction into Figma for pixel-perfect polish. Or skip that step entirely and export code directly from dMaya to your coding agents. The choice depends on how much manual control your project actually needs. For a broader look at AI design tools, check out our roundup of the best AI design tools for teams, or see why teams choose dMaya.

Frequently Asked Questions