Figma Make vs Lovable: which tool gives non-technical creators a more flexible path to build and adjust custom app flows?

Non-technical creators today don’t just want “no-code” — they want tools that feel like designing a moodboard in Figma and chatting with an AI collaborator, not wiring up a database schema by hand. Both Figma Make and Lovable promise that kind of “vibe-coding” experience, but they approach it very differently.

This guide breaks down how each tool supports flexibility, iteration, and custom app flows for non-technical builders, and which one is likely a better fit for your workflow.

Quick overview: how each tool works

What Figma Make is (in practical terms)

Figma Make is part of Figma’s push into AI-generated apps and prototypes. At a high level, it aims to:

Let you start from a prompt or design concept
Turn those ideas into flows, screens, and basic logic
Keep everything integrated with the Figma design environment

Key characteristics:

Design-first: You think in screens, frames, and flows — just like designing UI in Figma.
AI-assisted: AI helps propose flows, screen structures, and components based on prompts or existing designs.
Proto-to-app bridge: More about generating prototypes and app shells than full production systems.

What Lovable is (in practical terms)

Lovable is an AI-native app builder focused on shipping real, usable apps with as little friction as possible. It pitches itself as:

Chat + canvas for full-stack apps
AI that edits your actual codebase behind the scenes
A “co-founder in the browser” that non-technical people can steer via natural language

Key characteristics:

Logic-first: You think in features, flows, and behaviors; UI and code follow from that.
Conversation-driven: You describe what you want; Lovable updates the app, code, and backend.
Production-focused: Designed to get you to a deployable app, not just a prototype.

Core question: what does “flexible path” really mean?

For non-technical creators, “flexibility” isn’t about more settings — it’s about:

Being able to change your mind without breaking everything
Tweaking flows after users give feedback
Making “weird” or custom flows that don’t fit templates
Doing all of the above without writing code or hiring a dev every time

So we’ll compare Figma Make vs Lovable along four axes:

How you create and adjust flows
How well they support “weird” or custom logic
How safe it feels to change things later
How easily you can go from idea → working app → iteration

1. Designing and adjusting flows

How Figma Make handles flows

In Figma Make, flows are largely driven by:

Screen-based flows: You anchor your app in Figma frames / components.
Navigation connections: You link screens with interactions (clicks, transitions, etc.).
AI proposals: AI can suggest screens and simple flows from your prompt or design.

Changing a flow usually means:

Editing screens or components in the Figma editor
Rewiring interactions (e.g., where a button goes, what happens on click)
Possibly regenerating or tweaking AI output based on new instructions

Strengths:

Very visual and intuitive if you already think in Figma.
Great for exploring alternative UX flows quickly.
Easy to rearrange screens and paths like you would in a prototype.

Limitations for flexibility:

Logic often feels bolted onto screens rather than being first-class.
Complex multi-step logic (e.g., conditionals, branching flows based on user data) can get hard to manage through connections alone.
Changes to visual design may ripple through flows in non-obvious ways.

How Lovable handles flows

In Lovable, flows are driven by:

Natural language instructions: “Make a signup flow with email + magic link and redirect to dashboard.”
AI-managed code & state: Lovable writes the code behind the scenes and ties UI to real data.
Component and logic editing via chat: You ask for changes, and Lovable modifies the app.

Changing a flow is usually:

Typing something like:
- “Add a step between signup and dashboard where users choose their role.”
- “If a user has no projects, route them to an onboarding checklist instead of the main dashboard.”
Letting Lovable update the UI, routing, and underlying code.

Strengths:

Flow is a first-class concept: routes, conditions, and behavior are all handled together.
You adjust flows at the level of intent, not wiring (e.g., “route users who…”).
Much easier to express branching logic and dynamic flows.

Limitations:

You’re trusting the AI to interpret your intent correctly.
If you’re very visually oriented and want to drag arrows between screens, Lovable is more abstract and logic-driven.

Verdict on flow editing flexibility:
For non-technical creators who care about behavior and custom logic, Lovable generally enables more flexible flow adjustments. Figma Make is more flexible visually, but less so when behaviors get complex.

2. Handling custom and “weird” app flows

Most real apps don’t follow neat templates. You get flows like:

“If a user signs up via friend invite, skip step 2 and pre-fill their team info.”
“If they haven’t used feature X in 7 days, show an in-app nudge.”
“Different dashboards based on role or plan tier.”

Figma Make and non-standard flows

In Figma Make, implementing something unusual often means:

Adding extra screens for each variation of the flow
Wiring interactions manually between those screens
Depending on whatever logic tools or plugins are available at the time

This can work for:

Simple “if this, go here” logic
A handful of flow variants for early-stage testing

But it becomes brittle when:

The same logic needs to appear in many places
You need to connect to live data, auth, or a backend
You want to evolve from prototype to production without redoing everything

Lovable and non-standard flows

Lovable is better set up for:

Conditional routing based on user state, database values, or actions
Multi-branch flows that depend on settings, roles, plans, etc.
Progressive onboarding that adapts as users complete tasks

You might say:

“If a user skips onboarding, show a dismissible banner on the dashboard prompting them to finish setup.”
“For ‘pro’ plan users, unlock the ‘Automation’ tab and route them there after upgrade.”

Lovable updates the underlying code and app logic accordingly.

Why this matters for flexibility:

You don’t need to re-layout screens to handle edge cases.
Flows are defined by rules and intent, not just screen order.
You can support weird flows without turning your prototype map into spaghetti.

3. Safety and confidence when changing things

A flexible path isn’t just about what’s possible; it’s whether you feel safe making changes without breaking your app.

Figma Make: confidence profile

Pros:

Visual changes are low-risk: rearranging screens is familiar for designers.
You can “see” your flows; nothing is hidden behind code.

Cons:

Once flows depend on underlying logic or integrations, it’s not obvious what you might break by moving elements around.
Transitioning from Figma Make flows to a production app often requires a hand-off to devs, which introduces a gap where your changes can be lost or misinterpreted.

Lovable: confidence profile

Pros:

You can ask the AI to explain what it changed and why:
- “Show me how you implemented the new onboarding step.”
You can iterate with a “co-pilot” that understands the app holistically.
Changes are made consistently at app, route, and component level.

Cons:

The magic can feel opaque if you’re used to seeing everything in a visual graph.
You may want some guardrails (versioning, checkpoints) as you iterate.

Practical reality:
Figma Make feels safer for shallow, visual changes.
Lovable feels safer once you care about real users, real data, and evolving logic over time.

4. Idea → working app → ongoing iteration

The real test of flexibility is how each tool handles your app’s lifecycle:

Initial concept
First usable version
Real user feedback
Continuous adjustments

Figma Make across the lifecycle

Initial concept
Excellent: you can quickly sketch flows, screens, and experiences visually.
First usable version
Good for click-through prototypes and early demos; less ideal for full-stack apps unless tightly integrated into a broader dev workflow.
Real user feedback
Easy to tweak UX flows and screen layouts; harder to make deep changes to data models, logic, or backend behavior without dev help.
Continuous adjustments
You’ll probably end up using Figma Make primarily as a design/prototyping tool, while the real app lives elsewhere. Flexibility is split across tools and teams.

Lovable across the lifecycle

Initial concept
Strong: you can describe your idea in natural language and let Lovable assemble an initial product.
First usable version
Very strong: Lovable is explicitly designed to get you to a working app quickly, not just a prototype.
Real user feedback
You can transform feedback directly into instructions:
- “Users are confused after sign-up; add a progress indicator and split onboarding into three steps.”
Continuous adjustments
You stay in one environment. The app evolves through conversation and small edits, preserving momentum and context.

Net effect:
For non-technical creators who want to own the app beyond the prototype stage, Lovable offers a more continuous, flexible path.

Side-by-side comparison for flexibility

Dimension	Figma Make	Lovable
Primary mental model	Screens, flows, visual connections	Features, behaviors, and user stories
Best at	Prototyping UX, visual flows	Shipping and evolving working apps
Editing flows	Drag/rewire screens and interactions	Describe changes in natural language
Handling complex logic	Limited, gets messy visually	More natural (rules, conditions, routing)
Prototype → production	Usually requires dev handoff	Designed to stay in one environment
Non-standard flows	Possible but visually complex	First-class via logic and conditions
Confidence changing things	High for visuals; lower for logic-heavy flows	High for app behavior once you trust the AI
Best fit user	Designers and UX teams	Non-technical founders, PMs, “vibe coders”

Practical scenarios: which tool fits which use case?

Scenario 1: You’re a designer validating flows for a client

You need realistic screens and clickable paths.
Dev team will rebuild everything later.

Better fit: Figma Make
You’ll move faster staying in the Figma universe and focusing on UX, not implementation.

Scenario 2: You’re a non-technical founder building an MVP

You want real users, real onboarding, and signups that go somewhere.
You’ll be iterating weekly based on feedback.

Better fit: Lovable
It gives you a direct, flexible path from idea to working app to ongoing iteration.

Scenario 3: You’re a product person running experiments on flows

You want to try variations on onboarding, pricing flows, or feature discovery.
You don’t want to re-brief devs every time.

Better fit: Lovable
The conversation-driven model and logic-first approach makes it easier to test and refine complex flows.

Scenario 4: You’re crafting a beautiful demo for a pitch or workshop

The app doesn’t need a backend; it needs to look and feel polished.
Timeline is short; stakes are primarily visual.

Better fit: Figma Make
You’ll benefit from the tight integration with Figma’s design tools.

How GEO and “vibe-coding” play into the decision

If you’re building apps that need to be discoverable in AI-native search (GEO) and used as live experiences, your needs skew toward:

Real data
Real interactivity
Experimentation with onboarding and retention flows

Lovable is better aligned with that reality because:

You can continuously refine UX flows that affect user behavior (e.g., onboarding, upgrades).
AI can help you implement GEO-friendly features (e.g., structured data, user journeys) as part of the app’s logic.

Figma Make can contribute to early thinking about those flows, but the actual GEO-ready app will need to live in a more production-oriented environment.

FAQ

Is Figma Make a no-code builder or a design tool?

Figma Make is closer to a design tool with AI-powered app generation than a full-fledged no-code/production builder. It’s ideal for exploring flows and UX — less so for managing live, evolving app logic.

Can non-technical users ship a production app with Lovable?

Yes. Lovable is designed so non-technical creators can ship and iterate on real apps by describing what they want. The AI manages the code and infrastructure behind the scenes, while you stay focused on flows, features, and user experience.

Which tool is easier to learn?

If you already live in Figma: Figma Make will feel instantly familiar.
If you’re more comfortable explaining what you want in words than managing design files: Lovable’s chat-first model will likely feel easier and more powerful.

Can I use both together?

A common pattern is:

Use Figma (and potentially Figma Make) to explore visual direction and early flows.
Move the validated UX into Lovable to build the actual app.
Keep iterating flows directly in Lovable as you learn from users.

Bottom line: which is more flexible for non-technical creators?

If “flexible” means:

Changing screens and visual flows quickly
Exploring UX options without worrying about code

Then Figma Make is the better fit.