Figma Make vs Lovable (2026): Which AI App Builder Wins?

0.0

Переглянути сайт

BEST OVERALL

4.8

Переглянути сайт Зекономте 20% на всіх планах Lovable (лише для учасників)

---

• Free plan includes 30 credits per month
• Collaborate in real time with multiplayer editing and AI assistance
• Fully managed hosting, domains, SEO, and updates in one platform

I ran real builds on both. Lovable is the overall winner for teams building web applications. Figma Make is the right call for designers already inside the Figma ecosystem who want to move faster from concept to prototype.

The Most Important Thing to Understand Before Reading Further

Quick Summary

Figma Make is an AI layer built on top of the world’s most popular design platform, aimed at making Figma designers more productive and moving faster from concept to interactive prototype. Lovable is a standalone specialist: describe a web app, get a fully deployed full-stack product with a live database, authentication, and payment processing. There is overlap, but the audiences are different, and the right choice depends on your starting point.

1. Prices and Plans Comparison

Lovable’s Unlimited-Team Rate Beats Figma Make’s Per-Seat Model for Any Team Not Already Paying for Figma

Figma Make

Figma Make’s pricing cannot be separated from Figma’s pricing; they are the same subscription. Here is what that means in practice:

The cost depends entirely on where your team starts:

Already on Figma? Make costs nothing extra. The AI credits (3,000/month on Professional, 3,500 on Organization) are shared across Figma AI features, but full seats are already paid for.

Not on Figma? You are buying the world’s leading design platform to access one feature. One Professional full seat is $16/month, or $80/month for five people. That buys Figma Design, FigJam, Slides, Buzz, Sites, Draw, and Make: a full design toolkit most app builder users do not need.

The credit system is worth understanding before you commit:

• Starter (free): 150 AI credits per day, 500 per month. Enough to evaluate; not enough for sustained building.
• Credits are shared: Across all Figma AI features, not just Make. Image editing, Design AI tools, and Make all draw from the same pool.
• Mid-session warnings: A “Check your AI credit balance” notification can appear if you have been actively building; plan accordingly.

Seat types matter. Figma Make requires a full seat. Dev seats ($12/month) and Collab seats ($3/month) do not include full Make access. If only some team members need to build with Make, you can mix seat types, but every person who creates with Make needs a full seat at $16/month or above.

Lovable

Lovable’s model is structurally simpler than any per-seat system: one subscription, unlimited users, one price.

• Free ($0): 5 daily credits, 30 per month cap. Enough to explore the interface and test one build; not enough for sustained production development.
• Pro ($25/month): Unlimited users on one subscription. Includes credit rollover to the next billing cycle, custom domains, badge removal from published apps, on-demand credit top-ups, and multiplayer workspaces (Lovable 2.0). Students with a valid academic email receive up to 50% off.
• Business ($50/month): Everything in Pro plus SSO (for teams using Google Workspace, Okta, or similar identity providers), role-based access controls, a security center dashboard, and priority support. Still covers unlimited users.
• Enterprise: Custom pricing for dedicated support, advanced compliance documentation, custom infrastructure, and SLA guarantees.

There is no per-seat counting, no distinction between builders and viewers, and no credit pool that divides unevenly as your team grows. A two-person founding team and a forty-person product organization pay the same $25/month on Pro.

When someone new joins and needs access, they join the workspace with no new seat to purchase, no billing admin approval, and no headcount to track.

Annual billing applies a discount on paid plans. On-demand credits are purchasable mid-cycle if the team exhausts the monthly allocation before the next reset.

Winner Snapshot: The honest answer depends on context. For a designer already on Figma Professional, Figma Make is essentially free: add it to an existing workflow at zero incremental cost. For a team not on Figma, choosing Make means paying a minimum of $16/seat/month, which rises to $80/month at 5 people, versus Lovable’s fixed $25/month. For any team of six or more who are not already Figma subscribers, Lovable is significantly cheaper and carries no design-platform overhead.

2. AI Capabilities & Features Comparison

Figma Make’s Model Selector, Ecosystem Embedding, and Self-Healing Code Set It Apart in This Category

Figma Make

Figma Make’s most distinctive AI feature is one no other platform in this series offers: you choose the model before each prompt.

The selector appears at the bottom of the chat panel. Four options:

1. Default: Figma’s recommended balance of speed and capability
2. Claude Sonnet 4.6: Described as “Balanced and efficient,” familiar to anyone who works with Anthropic models
3. Gemini 3 Flash: “Fast and iterative”: ideal for rapid visual changes where you want quick feedback
4. Gemini 3.1 Pro: “Deep and creative”: for complex layouts and nuanced generation

 For teams where different members have different model preferences (some on Anthropic agreements, some prefer Google), this flexibility matters.

Transparency before generation. When I submitted the NexaHost analytics dashboard prompt, Figma Make explained its approach before writing a line of code.

Self-healing code. When the output contained more than ten warnings, the system offered “Fix for me.” The AI diagnosed the problem: duplicate keys in chart data causing key conflicts and animation issues. It assigned unique identifiers to each data point and disabled conflicting chart animations. The fix was surgical and accurate.

Figma ecosystem embedding. Copy a Figma Make link, paste it into any Figma Design file, FigJam board, or Slides deck, and the live interactive prototype plays directly on that canvas.

Component quality. The NexaHost dashboard used: @mui/material, @radix-ui components, lucide-react for icons, @emotion for styling, and recharts for data visualization. This is what a senior frontend engineer would use, not a simplified scaffold.

Lovable

Lovable’s AI specializes in one thing and does it completely: generating and deploying full-stack web applications from plain-English prompts with no prerequisites.

Full-stack from one prompt. On the InvoicePro build, a single prompt produced everything below, built, connected, and live in under 10 minutes:

• A Supabase database with three related tables (clients, invoices, time_entries) and correct foreign key relationships
• Authentication covering email/password and Google OAuth
• A Stripe integration with three pricing tiers, checkout links, and webhook handling
• A client-facing portal with correct per-user data scoping
• A deployed URL on lovable.app

No model to choose. No framework decision. No warning to review.

Pre-build planning. Before writing code, Lovable returns a structured plan naming every feature, tech choices, and missing dependencies (such as the Supabase connection requirement). This gives a review moment before the AI commits to an architecture, similar to Figma Make’s transparent reasoning step, but applied to the full stack.

80+ native integrations. Stripe, Supabase, OpenAI, Resend, PostHog, Cloudinary, Twilio, and more connect through the Connectors sidebar with no API keys to paste and no boilerplate to write. Figma Make’s native integration list covers GitHub, Supabase, and Google Analytics: three integrations versus eighty-plus.

Lovable 2.0 capabilities:

• Dev Mode: VS Code-style in-browser code editor, direct component editing
• Visual Edits: Click any element to adjust text, color, padding, or spacing at the CSS level
• Themes: Global design token panel (color, font, border radius) applying changes site-wide from one setting
• Multiplayer workspaces: Multiple team members can work concurrently
• AI Connectors: Pre-built paths to AI services, vector databases, and workflow APIs

Winner Snapshot: Figma Make wins AI capabilities for designers and developer teams who want model-level control. The ability to choose Claude Sonnet 4.6, Gemini Flash, or Gemini Pro per prompt (and to escalate to more powerful models when an iteration falls short) is a genuine capability that no other platform in this comparison series offers.

3. App Generation Speed & Quality Comparison

Figma Make Is Faster for Frontends; Lovable Delivers the More Complete Product

Figma Make: NexaHost SaaS Analytics Dashboard

I submitted one prompt: a full SaaS analytics dashboard for a fictional hosting company called NexaHost with a dark sidebar, KPI cards, traffic charts, and a recent activity feed.

Speed: Fully rendered and interactive in under 2 minutes from pressing submit. That includes the AI’s reasoning phase, full code generation, and canvas rendering. For the complexity of the output, this is the fastest generation time in this comparison series.

Quality: What the dashboard contained:

• A dark sidebar with the NexaHost brand, “Analytics Dashboard” subtitle, and five navigation links (Overview, Servers, Traffic, Billing, Settings), with Overview highlighted in blue immediately
• A top bar with a working search field and “Sarah Chen / Admin” user profile with notification bell and avatar
• Four KPI cards: Uptime (99.98%), Active Servers (47), Monthly Bandwidth (2.4 TB), Open Tickets (8), each with a trend indicator versus last month
• A 30-Day Traffic Trends chart rendered as a functional Recharts area visualization with actual date labels from May 11 through June 5, y-axis labels at 20k intervals, and data in the 40k-80k bandwidth range
• An MRR by Plan bar chart and server status distribution visualization
• A Recent Activity feed with contextually realistic hosting events: a server deployment success for web-prod-03, a high CPU warning at 87% on db-master-01, an SSL certificate expiry alert for api.nexahost.com, a payment received notification, and a scheduled maintenance window, all domain-appropriate, none generic

The activity feed detail is worth pausing on. Figma Make did not just populate placeholder text. It understood the context was a hosting dashboard and generated realistic operational events a hosting operations team would actually see.

This contextual intelligence in content generation is genuinely impressive and sets a benchmark for how AI-generated dummy data should look.

The warning issue. After generation, more than ten warnings appeared. “Fix for me” reduced the count to three, then subsequent edits pushed it back up to nine, then above ten again. The dashboard continued to render and function correctly throughout. But for teams planning to take the generated code into production, a codebase that self-generates new warnings with each iteration is worth testing further before committing.

Lovable: InvoicePro Build

I submitted one prompt for a Client Portal and Invoicing App covering multi-tenant dashboards, time tracking, invoicing with PDF output, Stripe payments, and a client portal backed by Supabase.

Speed: Lovable returned a build plan before writing any code, flagged the Supabase connection requirement, and began after connection. Key milestones:

• Minute 4: Landing page live with hero text and six feature cards
• Pricing section: Starter ($9/month), Professional ($29/month, “Most Popular”), Enterprise ($79/month)
• Under 10 minutes: Deployed on lovable.app with Supabase auth, database tables, and Stripe checkout all wired

Quality: What InvoicePro contained:

• Supabase database with three related tables (clients, invoices, time_entries), with foreign key relationships correct, no SQL written manually
• Authentication: email/password and Google OAuth, configured and wired
• Stripe integration: three pricing tiers, checkout links, billing portal routing, subscription sync, webhook handling for payment events
• A client-facing portal with correct data scoping per user
• Clean React/TypeScript/Tailwind with typed data arrays, named component files, and a logical folder structure
• Deployed live on a shareable URL within the 10-minute window

When a missing Supabase environment variable caused a blank preview, a plain-text error description appeared with a “Try to fix” button. One click resolved it.

Winner Snapshot:

Speed: Figma Make wins: under 2 minutes versus Lovable’s 10. For teams who need rapid iteration on frontend designs, that gap is real.

Quality of output: Figma Make’s NexaHost dashboard is visually the most impressive frontend in this comparison series. The contextually appropriate activity feed, professional component stack, and functional chart implementation represent exceptional AI-generated frontend work.

Completeness: Lovable wins. InvoicePro was a fully deployed product with authentication, a real database, and payment processing. NexaHost was a stunning frontend that required additional Supabase setup to become a real application. For a reader who needs a working product today, Lovable’s 10-minute fully-wired output is more valuable than a 2-minute beautiful prototype.

4. Ease of Use Comparison: Which Platform Is Easier to Use?

Lovable Requires No Prerequisite Platform and No Design Background; Figma Make Rewards Prior Figma Experience

Registration and Account Creation

Figma Make’s onboarding has two very different experiences depending on where you start:

• Existing Figma users: Land on figma.com, type a prompt before creating an account, sign in with Google or email, and you are inside Make within seconds. The prompt carries over automatically.

• New Figma users: First encounter a full design platform with multiple products (Design, FigJam, Slides, Buzz, Sites, Draw, Make). Navigating to Make and understanding what it does relative to the other tools adds real orientation time.

Lovable’s signup flows through a short role questionnaire. The dashboard loads immediately with no other platform to understand first.

The signup includes GitHub as an authentication option alongside Google and Apple. Lovable also remembers your last-used method, which is a small but considerate touch for repeat users.

User Interface and Dashboard

Figma Make’s workspace splits into two primary zones. The left panel handles the conversation: prompts at the bottom, AI responses and reasoning above, version history as scrollable labeled cards. The right zone is the live canvas where output renders.

The canvas toolbar offers three views that are worth understanding:

• Preview mode (eye icon): Live interactive prototype
• Code mode (angle bracket icon): Full file explorer on the left, code editor on the right
• Combined layout: Both at once

Switching between them is instant and does not interrupt generation. In code view, the full project structure is visible: App.tsx, all component files, package.json, and configuration files, and every file is directly editable.

At the bottom of the chat panel: an add button for new prompts, a model selector dropdown, a spark icon for AI settings, and a Build/Default mode toggle. These controls are where most of your workflow decisions happen.

Lovable’s dashboard opens to a warm blue-to-pink gradient with a personalized greeting. The prompt box reads “Ask Lovable to build a web app that…” with a Build mode toggle and a Connectors banner.

The left sidebar shows Home, Search, Resources, and Connectors, followed by project views and a Recents section. For a non-technical user, this dashboard is more immediately legible.

The Point and Edit System

Figma Make’s Point and Edit inline editing is its strongest usability feature. After the NexaHost dashboard rendered, I clicked on the “30-Day Traffic Trends” heading. Two things happened simultaneously:

1. The h3 tag identifier appeared in the chat input, scoping my next prompt to that exact element
2. A rich formatting toolbar appeared directly over the element in the canvas: font family, size, bold, italic, alignment, and other controls

I could either describe the change in plain English in the chat or manipulate the element directly through the toolbar without writing a prompt at all. This hybrid approach is more practical than tools that route every change through the AI, especially for small corrections: fixing a typo or changing a price does not require waiting for the AI to interpret a prompt.

A first-generation tutorial overlay walks through Point and Edit in four steps, appearing at exactly the right moment: after your first output is rendered, when you actually need the information.

Lovable’s Visual Edits work similarly: click any element in the preview to adjust text, color, padding, or spacing, but without the simultaneous formatting toolbar.

Changes in Lovable’s visual editor go through a direct CSS edit, while Figma Make’s toolbar adds the option for immediate formatting without any text input.

Overall Ease of Use Assessment

The honest split:

• Already on Figma? Make is the easier path. No switching cost, no new dashboard, output appears in Recents alongside design files, and Figma’s multiplayer extends to Make projects automatically.
• Not on Figma? Lovable is easier. No seat type decisions, no credit balance monitoring, no platform onboarding overhead. Just describe an app and it is deployed.

Winner Snapshot: Lovable wins ease of use for teams without an existing Figma workflow. For Figma-native teams, the ease of use equation flips: Make is accessible within an environment they already know, and Lovable would represent an additional platform to onboard. The deciding variable is where your team lives today.

5. Privacy and Security Comparison: Which Platform Is More Secure?

Figma Holds the Most Extensive Compliance Portfolio in This Comparison Series; But a Critical Privacy Risk Exists in Publishing

Figma Make

Figma’s compliance portfolio is the most extensive of any platform in this comparison series:

• SOC 2 Type II: Annual independent audit confirming ongoing effectiveness of security controls
• SOC 3: Public-facing report of the SOC 2 findings
• ISO 27001:2022: International standard for information security management
• ISO 27017: Cloud-specific security controls
• ISO 27018: Protection of personally identifiable information in the cloud
• ISO 27701: Privacy information management system certification
• FedRAMP Moderate: US government-grade cloud compliance, the certification level required for federal agency adoption
• GDPR: EU Cloud Code of Conduct adherence; Data Processing Addendum available
• CCPA: California Consumer Privacy Act compliance
• TISAX: European automotive industry information security standard
• C5: German Federal Office for Information Security certification for regulated and public-sector customers

For enterprise teams in regulated industries (financial services, healthcare, government, automotive), this certification breadth means Figma has done the compliance work. The Trust Center at compliance.figma.com documents all certifications publicly.

One critical privacy risk every reader must know about before publishing. The Figma Make Publish panel includes a “Show chat history” checkbox that is checked on by default. If you publish to the Figma Community without unchecking it, anyone browsing your Community listing can see your complete prompt conversation: every iteration, every rejected direction, every detail you specified.

For personal learning projects, this is harmless. For client work, proprietary product specifications, or any commercially sensitive build, this is a significant data exposure risk. It is not prominently warned in the publish flow, and it is easy to miss.

Lovable

Lovable holds three independently audited certifications. Each is worth understanding in practical terms:

• SOC 2 Type 1 and Type 2: Type 1 confirms that security controls are designed appropriately. Type 2 confirms those controls operated effectively over a sustained audit period. Holding both means the assessment covered actual operational performance, not just design intent.
• ISO 27001:2022: The international standard for information security management systems, covering cloud environments and supplier relationships. The 2022 edition reflects updated requirements around cloud services specifically.
• Full GDPR compliance: Confirmed as a platform default, not contingent on deployment configuration. EU-based teams are covered without needing to evaluate self-hosting.

Code ownership is explicit throughout: GitHub sync provides a clean exit at any time without proprietary format extraction.

Winner Snapshot: Figma holds the more comprehensive compliance documentation: ISO 27017, ISO 27018, ISO 27701, FedRAMP Moderate, CCPA, TISAX, and C5 extend beyond what Lovable publicly confirms. For regulated-industry enterprise procurement, Figma’s Trust Center is the stronger resource. The tradeoff: the community publishing privacy risk (chat history exposed by default) is a real operational concern that Lovable does not have. Verify the “Show chat history” setting is off before publishing any non-personal Figma Make project.

6. Platform Integrations and Deployment Options Comparison

Lovable’s 80+ Integrations Including Native Stripe Win Decisively; Figma Make Lacks Payment Processing

Figma Make

Figma Make’s integration strategy is minimal by design. Three primary connections, plus escape hatches:

Supabase. Adding a real backend is a second prompt, not automatic. You describe what you need (“add Supabase Auth” or “create a database for tracking orders”) and the AI wires it in.

GitHub. Connect a repository and push generated code through the settings panel. This enables proper version control, CI pipeline integration, and handoff to a development team. The code that goes to GitHub is the same production-oriented stack that was generated: Radix UI components, Recharts, Tailwind v4, typed component files.

Google Analytics. A single field in General settings accepts a G-XXXXXXXX tracking ID. No script tag to inject, no plugin to install. Your published app gains GA4 analytics through settings alone.

Custom code injection. Four fields in General settings cover the standard HTML injection points: start of, end of, start of, end of. Any valid script or HTML tag works here: Hotjar, Mixpanel, custom fonts, tag manager scripts, any external service that loads via a tag. For integrations outside the native list, this is the escape hatch.

The “Configure your code” dialog. The package manager settings allow listing private npm packages and additional public libraries. Developers who need specific internal dependencies can add them here, a feature that no consumer-focused AI builder in this series offers.

What Figma Make does not have. No Stripe integration. No payment processing of any kind. Building a Figma Make project that collects money requires setting up Stripe separately through the custom code injection field or adding it manually after GitHub export. For teams building consumer products that need a checkout flow, this gap is significant.

There is no Vercel or Netlify integration yet. Figma Make’s deployment story is currently figma.site or GitHub. If your workflow involves deploying to Vercel, Netlify, Render, or Cloudflare Pages, there is no one-click integration for those platforms. You would need to push to GitHub first and then connect that repo to your preferred hosting provider as a separate step.

Lovable

Lovable’s integration strategy is coverage first: 80+ verified integrations that cover the most common production requirements without writing code.

Stripe (native, automatic). From one prompt on InvoicePro: three pricing tiers generated with correct checkout links, billing portal routing, and Supabase sync for subscription status. Webhook handlers for payment events (subscription created, payment failed, subscription cancelled) were included without prompting. The entire Stripe integration was wired and live before the 10-minute mark. Figma Make has no equivalent capability.

Supabase (native, automatic). Lovable creates the database schema from the first build, not as a second step. Tables are generated with correct column types and foreign key relationships. Authentication flows (email/password, Google OAuth, magic links) are wired automatically. RLS policy scaffolding is included (though correct configuration requires manual verification as noted in Section 5).

The 80+ catalog. Covers email (Resend, SendGrid, Mailgun), analytics (PostHog, Mixpanel, Google Analytics), file storage (Cloudinary, AWS S3 via Supabase), communications (Twilio, WhatsApp Business API), AI services (OpenAI, Anthropic, Cohere), and more. Each connects through the Connectors sidebar with no configuration steps and no API key management required.

AI Connectors (Lovable 2.0). Pre-built paths to vector databases, AI workflow orchestration services, and embedding APIs extend the catalog beyond standard REST integrations for teams building AI-powered features.

Deployment. One-click publishing to lovable.app with automatic DNS and SSL provisioning. GitHub sync to Vercel or Netlify is available for teams with existing hosting infrastructure. Custom domains connect on Pro and above with no manual certificate management.

For services outside the 80+ catalog, Supabase Edge Functions allow custom JavaScript-based server logic, the practical escape hatch for bespoke integration requirements that do require writing code.

Winner Snapshot: Lovable wins integrations by a decisive margin. The absence of any native payment processing in Figma Make is the most significant gap. For any web application that collects revenue (a SaaS product, a marketplace, a subscription service), Lovable’s native Stripe integration delivers end-to-end payment logic in the first build, while Figma Make requires building it separately.

Figma Make vs Lovable: The Bottom Line

Lovable wins for founders, product teams, and anyone building a web application who does not already live inside Figma. Figma Make wins for designers who want to prototype faster within an ecosystem they already know.