Figma AI vs Miro AI: Best Enterprise Design Tools 2026

The promise of AI design tools has shifted from "AI will replace designers" to something more useful: AI handles the tedious parts so designers focus on decisions that matter. After testing a dozen tools over the past three months on real client work — landing pages, app interfaces, brand assets, and marketing materials — a handful stood out as genuinely useful rather than merely impressive in demos.

Two of them dominate the enterprise conversation, and they are the reason most procurement threads exist: Figma AI and Miro AI. This guide covers both in depth — including where each stood as of the early-2026 feature updates and what actually gates adoption inside a large organisation — and then covers Midjourney, Adobe Firefly, Galileo AI, and Framer AI for the parts of the workflow those two don't reach.

Figma AI vs Miro AI: the short answer

They are not substitutes, and treating them as competing line items is the most common mistake in design-tool consolidation.

Figma AI is production tooling. It operates inside design files that already carry your components, styles, and variables, and its value is measured in throughput: fewer minutes per component, cleaner handoff, less drift from the design system. The output is an interface someone ships.

Miro AI is decision tooling. It operates on the messy input to design — research notes, workshop output, stakeholder discussion — and its value is measured in elapsed time to alignment. The output is a shared understanding, plus low-fidelity artefacts that feed the Figma stage.

	Figma AI	Miro AI
Primary job	Produce and refine shipped UI	Converge a group on what to build
Operates on	Design files, components, design-system tokens	Boards, research notes, workshop output
Strongest feature	Generation that respects your own design system	Clustering and summarising unstructured input
Handoff path	Dev Mode → engineering (incl. MCP for AI coding agents)	Board → brief → Figma file
AI commercial model	Bundled into paid seats, usage limits by plan	Metered credits, pooled across the organisation
Enterprise controls	SAML SSO, SCIM, admin AI policy toggles	SAML SSO, SCIM, eDiscovery/DLP tier, data residency options
Buy it when	Production throughput is the bottleneck	Discovery and alignment are the bottleneck

Most organisations of any size end up with both, because the two bottlenecks are rarely the same quarter's problem. The consolidation question worth asking is not "Figma or Miro" — it is which of your other eleven tools those two absorb.

Quick comparison

Tool	Price	Best for	Quality	Speed	Learning curve
Figma AI [AFFILIATE:figma]	Included in paid plans	UI design assistance within existing workflows	High	Fast	Low (if you know Figma)
Miro AI	Included in paid plans, metered by credits	Research synthesis, workshops, early concepting	High	Fast	Low
Midjourney [AFFILIATE:midjourney]	$10-60/mo	Concept art, mood boards, visual exploration	Excellent	Medium	Medium
Adobe Firefly [AFFILIATE:adobe-firefly]	Included with CC / $5-10/mo standalone	Photo editing, asset generation, brand-safe imagery	High	Fast	Low
Galileo AI [AFFILIATE:galileo]	$19-49/mo	Full UI generation from text descriptions	Good	Very fast	Low
Framer AI [AFFILIATE:framer]	Included with Framer plans	Complete website generation and iteration	Good	Very fast	Low

Figma AI — the assistant that lives where you already work

Figma's AI features arrived gradually throughout 2025 and have matured significantly. Unlike standalone AI tools that generate designs in isolation, Figma AI operates inside your existing design files with full awareness of your components, styles, and design system. This context awareness is its primary advantage.

What works well:

The auto-layout suggestions are the feature I use most. Select a group of elements and Figma AI proposes layout structures based on the content — proper spacing, alignment, and responsive behavior. It saves five to ten minutes per component, which adds up across a full page design.

Rename layers is a small feature that matters more than it sounds. Figma AI can rename an entire file's layers based on their content and purpose. "Frame 47" becomes "Hero Section > CTA Button." This makes handoff to developers dramatically smoother and makes your own files navigable months later.

The "Make a design" feature generates UI components and layouts from text prompts, but — and this is important — it uses your existing design system tokens. If you have defined color styles, typography, and component variants, the generated designs respect them. This is a fundamental difference from standalone generators that produce beautiful but off-brand designs.

What doesn't work well:

Complex multi-screen flows still require human orchestration. You can generate individual screens, but Figma AI doesn't understand navigation patterns, user journeys, or information architecture across screens. Each generation is independent.

The generated designs tend toward safe, conventional patterns. If you're designing something intentionally unconventional — an editorial layout, an experimental interaction pattern — Figma AI steers you back toward standard SaaS UI conventions.

Best for: Design teams already using Figma who want to accelerate routine work without changing their workflow. The integration advantage is real — no exporting, no context switching, no reconciling AI outputs with your design system.

Figma AI feature state as of March 2026

Figma ships AI in small increments rather than annual releases, so "what does Figma AI do" is a question with a date attached. Here is the shape of the surface as of March 2026, grouped by what the features are actually for.

In-file assistance. The unglamorous, highest-usage tier: auto-layout suggestions, bulk layer renaming, visual search that finds a component you designed eight months ago by resemblance rather than by name, background removal, and image upscaling. None of these generate a design. All of them remove a specific recurring chore, which is why adoption inside teams is near-universal while adoption of the generation features is patchy.

Design-system-aware generation. Prompt-driven generation of components and layouts that resolves against your own tokens — colour styles, type styles, component variants — rather than against a generic default. This is the capability that separates Figma AI from every standalone UI generator, and it is also the capability that degrades fastest when a team's design system is inconsistent. If your library has four competing button components, generation will confidently pick one of them.

Prompt-to-prototype (Figma Make). Introduced at Config 2025 and matured through the following year, this surface takes a prompt or an existing frame and produces a working, interactive result rather than a static mock. It is the fastest route from "what if the flow worked like this" to something a stakeholder can click, and the least suitable route to production code.

Design context for AI coding agents. Figma's Dev Mode MCP server exposes file structure, variables, and component context to external AI coding agents. Practically, this means an agent writing your front-end can read the actual spacing token instead of guessing 16px from a screenshot. For organisations already running AI coding assistants, this is the highest-leverage Figma AI feature and the one least likely to be on a designer's radar — see our ranking of the best AI coding agents for the other half of that pipeline.

The caveat that matters for planning: availability differs by plan tier and by admin policy, and generation surfaces carry usage limits that have been revised more than once. Confirm your own tier against Figma's release notes before you build a rollout plan that depends on a specific capability being present for every seat.

Miro AI — the whiteboard that became a design surface

Miro's AI story is easy to underrate because Miro's starting point was a whiteboard, and whiteboards are not where design tooling budgets go. That framing is now out of date. Miro acquired Uizard in 2024, folded its UI-generation technology into the canvas, and spent the following period turning an infinite whiteboard into the place where the fuzzy front end of design actually happens.

What works well:

Research synthesis is the killer feature and the one I would pay for on its own. Dump 40 interview notes, support tickets, or survey responses onto a board, and Miro AI clusters them into themes and names the clusters. The clustering is not magic — it is roughly as good as a competent researcher's first pass — but it is instant, and a first pass you can argue with beats a blank board. On a recent discovery engagement it turned a two-day affinity-mapping session into a two-hour one, with the saved time going into arguing about the clusters rather than creating them.

Summarising a board is the feature that survives contact with real meetings. After a 90-minute workshop that produced 200 stickies across six frames, Miro AI produces a structured summary — themes, decisions, open questions — that is good enough to send to people who weren't there. The alternative was someone spending an hour transcribing stickies into a doc, which is the task most likely to simply never happen.

Generation from prompt or selection covers diagrams, mind maps, and presentations. Describe a process and get a flow diagram; select a cluster of stickies and get a mind map or a slide. The quality is functional rather than beautiful, which is correct for the stage — these are artefacts for thinking, not deliverables.

Prototype and UI generation, inherited from Uizard, produces low-fidelity screens directly on the canvas. This is deliberately not competing with Figma at the high-fidelity end. Its job is to make a concept concrete enough to react to before anyone opens a design file.

Sidekicks — task-scoped AI helpers that carry domain context across a board — are the newest layer and the most variable in practice. When the board is well structured, they are genuinely useful. When the board is a sprawl of six months of unlabelled frames, they inherit the sprawl.

What doesn't work well:

Output quality degrades sharply with board hygiene. Miro AI reads the canvas, so a messy canvas produces a messy summary. Teams that treat boards as permanent, ever-growing surfaces get much worse results than teams that create a board per session and archive it.

It is not a production design tool and should not be sold internally as one. The generated UI is a thinking artefact. Handing a Miro-generated screen to engineering as a spec is a reliable way to ship something that looks like a wireframe.

Credit metering changes behaviour in ways that are easy to miss. Because AI usage draws on a pooled organisational allowance rather than a per-seat entitlement, heavy users in one team can quietly consume the allowance another team was counting on. Nobody discovers this during the trial.

Fidelity handoff to Figma is weaker than Figma-native paths. You will rebuild, not import. In practice that is fine — the Miro artefact was never meant to be the starting geometry — but teams expecting a clean pipeline are disappointed.

Best for: Product, research, and design teams whose real bottleneck is upstream of the design file — too many unstructured inputs, too many stakeholders, too long to reach an agreed direction. Miro AI compresses the part of the process that Figma AI does not touch at all.

Miro AI feature state as of February 2026

As with Figma, the honest answer carries a date. As of February 2026 the Miro AI surface breaks into four groups:

Canvas intelligence. Clustering and summarising stickies, generating mind maps and diagrams from a prompt or a selection, and producing presentations from board content. This is the most-used group and the one that requires the least change to how a team already works.

Research synthesis. Thematic analysis over interview notes, survey responses, and feedback, with the resulting themes rendered as first-class board objects you can rearrange and argue with rather than as a text summary you can only accept or discard.

UI and prototype generation. The Uizard lineage: low-fidelity screens and clickable concepts generated on the canvas, positioned explicitly as pre-Figma artefacts.

Sidekicks. Task-scoped AI helpers carrying domain context across a board, aimed at recurring jobs — running a retro, building a project brief, structuring a research plan — rather than one-off generation.

The commercial detail to check first: Miro AI is metered in credits allocated by plan and pooled at the organisation level. Before rollout, find out your allowance, your current burn rate, and what happens when the pool empties. That single question has derailed more Miro AI rollouts than any capability gap.

Enterprise adoption: what actually gates a rollout

Across organisations that have tried to standardise on AI design tooling, the thing that stalls a rollout is almost never feature quality. It is one of four things, and all four are answerable in a week if you ask early.

1. Can AI be turned off, and is customer content excluded from training? Security review will ask both questions and will not accept a marketing page as evidence. Both Figma and Miro provide admin controls to govern AI processing and model-training participation, and both treat business and enterprise tiers differently from free and individual tiers. Because those defaults have changed more than once and vary by plan and region, read the current terms in the vendor's trust centre and in your own workspace admin settings, screenshot them with a date, and re-verify at renewal. This article is not evidence for a security review; your screenshot is.

2. Identity and lifecycle. SAML SSO and SCIM provisioning matter more once AI is in play, because an AI-enabled seat that outlives an employee is both a licence cost and a data-access problem. Both vendors offer SSO and SCIM on their upper tiers. The question to ask internally is not whether the vendor supports it but whether your identity team has capacity to wire it up this quarter — that is the step that actually slips.

3. Data residency and retention. The subtle failure mode: a workspace is correctly configured for a region, and then nobody checks whether AI prompts and generated outputs inherit the same residency and retention policy. Ask the question explicitly and get the answer in writing.

4. The commercial model. Figma bundles AI into paid seats with usage limits that vary by plan; Miro meters credits pooled across the organisation. Procurement will want a spend ceiling, and metered models do not naturally provide one. Model it the same way for both: current seat count, realistic generations per designer per week, and — the part teams skip — what happens when the allowance runs out. Throttle, hard stop, and overage charge are three very different answers to budget for.

What nobody can tell you: neither vendor publishes per-seat AI adoption rates, and any third-party figure claiming to is an extrapolation. If you want a real adoption number, instrument your own: measure the share of active seats using an AI feature weekly, ninety days after rollout. That number is comparable across your own tools and across time, which is the only comparison that should drive a renewal decision. For the broader security and procurement picture, see our guide to AI tools for enterprise teams; for the operational side — pilot design, credit budgeting, and what to instrument before renewal — see our 90-day enterprise rollout plan.

Figma AI vs Miro AI: which one to standardize on

If budget forces a single choice, choose against the bottleneck rather than against the feature list.

Choose Figma AI when designers are the constraint. Symptoms: the direction is agreed but screens take too long; design-system drift keeps appearing in review; engineering handoff generates a steady stream of clarification questions. Figma AI attacks all three, and if you are already running AI coding assistants, the Dev Mode MCP path compounds the benefit downstream.

Choose Miro AI when the organisation is the constraint. Symptoms: research sits unsynthesised; workshops end without written decisions; three teams hold different beliefs about what is being built. Miro AI attacks the elapsed time between input and agreement, which no amount of faster screen production fixes.

Run both when you have more than roughly twenty people in the product organisation. At that size the two bottlenecks alternate by quarter, and the combined cost is usually smaller than the four point tools the pair replaces — whiteboarding, diagramming, research repository, and lightweight prototyping. That consolidation, rather than the AI features themselves, is where the ROI case is easiest to defend. Smaller teams should read our AI tools for freelance designers guide instead, where the maths is different.

Midjourney — still the best for visual exploration

Midjourney remains the strongest tool for generating high-quality imagery and exploring visual directions. Version 6.1 produces images that are genuinely difficult to distinguish from professional photography or illustration, particularly for lifestyle imagery, product concepts, and environmental scenes.

What works well:

Image quality is Midjourney's defining advantage. For creating hero images, concept art, mood boards, and visual identity exploration, nothing else matches the aesthetic quality and creative range. The images have a distinctive richness that makes them immediately usable in presentations, pitch decks, and early-stage design exploration.

Style consistency has improved significantly. Using the --sref (style reference) parameter with a reference image, you can maintain a consistent visual language across dozens of generated images. For a recent brand exploration project, I generated 40 images for a lifestyle brand's visual identity — all maintaining the same color temperature, lighting style, and compositional approach.

The ability to vary specific aspects of an image (composition, style, subject) while holding others constant makes iterative exploration fast. Start with a broad concept, then dial in the specific direction through variations and remixing.

What doesn't work well:

Text in images remains unreliable. If your design requires readable text — a mockup with real headlines, a social media post template — you will need to composite the text separately.

Precise control is limited compared to structured design tools. You can influence composition and style through prompting, but you cannot say "move the subject 20 pixels left" or "make the background exactly #2B4C7E." For precise asset production, Midjourney is a starting point, not a finishing tool.

The workflow is still Discord-based or web-based, which means no integration with design tools. Generated images must be downloaded and imported into your design files manually. For teams generating hundreds of assets, this friction adds up.

Best for: Creative directors, brand designers, and anyone in the visual exploration phase of a project. Midjourney excels at answering "what could this look like?" faster than any human process. See also our guide to AI image generators compared for a broader look at the image generation landscape.

Adobe Firefly — the safe choice for production work

Adobe Firefly has a specific advantage that matters for commercial design work: it was trained exclusively on licensed content, Adobe Stock images, and public domain material. This means generated content is commercially safe to use without the copyright ambiguity that surrounds other AI image generators.

What works well:

Generative Fill in Photoshop is the single most useful AI design feature across any tool. Select an area, describe what you want, and Firefly fills it with context-aware content that matches the surrounding image. Extending backgrounds, removing objects, adding elements — these edits that previously took 30-60 minutes now take seconds. The quality is production-ready in most cases.

Text effects generate stylized typography that would take hours to create manually — text made of flowers, chrome, fire, water. For social media graphics and marketing materials, this alone justifies the subscription for many designers.

The integration across Adobe apps (Photoshop, Illustrator, Express) means AI generation happens inside your existing production workflow. Generate a texture in Illustrator, extend a photo in Photoshop, create a social post variant in Express — all using the same AI engine with consistent quality.

What doesn't work well:

Firefly's standalone image generation trails Midjourney in quality and creative range. The images are good — technically competent, well-composed — but they lack the artistic quality and stylistic diversity that Midjourney achieves. For concept exploration, Midjourney is better. For production editing, Firefly is better.

The generative AI features consume credits, and the credit system is confusing. Different features consume different amounts, monthly allocations vary by plan, and it's difficult to predict how many credits a project will require. Adobe has improved transparency here, but it remains a pain point.

Vector generation in Illustrator is improving but still produces results that need significant cleanup. Simple icons and patterns work well. Complex illustrations generate with structural issues that take longer to fix than to draw from scratch.

Best for: Production designers who need commercially safe AI generation integrated into their existing Adobe workflow. If your work involves photo editing, marketing asset production, or brand content at scale, Firefly delivers the most practical daily value. For more on AI image generation specifically, see our DALL-E vs Midjourney vs Stable Diffusion comparison.

Galileo AI — UI generation that actually understands interfaces

Galileo AI takes a different approach from Figma AI. Instead of augmenting an existing design tool, it generates complete UI designs from text descriptions. Type "a settings page for a project management app with dark mode, sidebar navigation, and user profile editing" and Galileo produces a detailed, editable UI design in seconds.

What works well:

The quality of generated UI is surprisingly high. Galileo understands design patterns — it knows that a settings page should have grouped sections, that a dashboard needs a clear data hierarchy, that an e-commerce product page follows specific conventions. The generated designs use proper spacing, alignment, and visual hierarchy without being told to.

Speed is the primary value proposition. Generating 10 variations of a landing page takes about 5 minutes. Doing the same manually takes a full day. For early-stage product design, client presentations, and rapid prototyping, this speed advantage is transformative.

The export to Figma is clean. Generated designs come through as properly structured Figma files with named layers, auto-layout applied, and components that align with common design system patterns. This is notably better than tools that export flat images or poorly structured frames.

What doesn't work well:

Generated designs are generic. They look professional and clean, but they don't have a distinctive brand identity. Every settings page looks like a settings page. Every dashboard looks like a dashboard. The designs are correct but not creative — they're a starting point, not a deliverable.

The tool has limited understanding of interaction patterns, animations, and state management. It generates static screens, not interactive prototypes. You get the "default" state of each component but not hover states, loading states, error states, or transitions.

Complex, multi-screen applications lose coherence. Generating individual screens works well. Generating an entire application with consistent navigation, shared components, and a logical information architecture requires significant human guidance and cleanup.

Best for: Product teams and startup founders who need to go from idea to visual prototype quickly. Galileo is excellent for validating concepts, creating pitch materials, and generating design starting points that a designer then refines.

Framer AI — from prompt to published website

Framer AI generates complete, responsive websites from text descriptions. Unlike design tools that produce static mockups, Framer generates functional websites with real interactions, animations, and responsive behavior. You can publish the result immediately or customize it extensively.

What works well:

The end-to-end speed is unmatched. Describe a website, get a published URL in under two minutes. For landing pages, personal sites, portfolios, and simple marketing sites, this is genuinely faster than any other approach — including using templates.

The generated sites are responsive by default and include thoughtful micro-interactions (hover effects, scroll animations, transitions) that would take hours to implement manually. The design quality is good enough for production use in many contexts.

Iteration through conversation is natural. "Make the hero section taller, change the color scheme to dark blue and gold, add a testimonial section" — each instruction modifies the existing design coherently. It feels like directing a designer rather than writing code or dragging elements.

Built-in CMS, analytics, and SEO tools mean the generated site is immediately production-ready for content-driven sites. You don't need to integrate external services for basic functionality.

What doesn't work well:

Framer AI sites look like Framer AI sites. There's a homogeneity to the designs — they favor certain layout patterns, animation styles, and typographic choices. Experienced designers can identify a Framer-generated site immediately.

For complex web applications (multi-step forms, user dashboards, data-heavy interfaces), Framer AI struggles. It's optimized for marketing and content sites, not application UIs.

Customization beyond what the AI generates requires learning Framer's design tool, which has its own learning curve. The AI gets you 70-80% of the way there. The last 20-30% requires manual work in Framer's editor.

Best for: Founders, marketers, and small teams who need a professional website fast without hiring a designer or developer. Also excellent for designers who want to skip the development phase — design in Framer, let AI handle the responsive behavior and interactions.

How these tools fit together in a real workflow

The most productive approach isn't choosing one tool — it's understanding where each fits in the design process.

Discovery and alignment: Start in Miro. Cluster research, synthesise interviews, run the workshop, and generate the low-fidelity concept that makes the direction concrete. The deliverable of this phase is an agreed brief, not a design.

Exploration phase: Move to Midjourney for visual direction. Generate mood boards, concept imagery, and style explorations. Share these with stakeholders to align on aesthetic direction before investing in detailed design.

UI design phase: Use Galileo AI to generate initial screen layouts quickly, then bring them into Figma for refinement. Use Figma AI for layout assistance, component generation, and design system alignment as you refine.

Asset production: Use Adobe Firefly for photo editing, background generation, and production asset creation. The commercial safety of Firefly-generated content matters when assets go into production.

Handoff and implementation: Dev Mode carries the design context into engineering, including to AI coding agents via the MCP server. For marketing sites and landing pages, Framer AI can take you from concept to published site in an afternoon. For complex applications, the Figma-to-development handoff remains the standard path.

What's changed since 2025

The most significant shift is integration. A year ago, AI design tools were standalone novelties — impressive but disconnected from real workflows. Now, Figma AI is inside Figma, Miro AI reads the canvas your team already works on, Firefly is inside Photoshop, and Framer AI produces deployable websites. The tools have moved from "look what AI can do" to "here's how AI helps you do your job."

The second shift is that AI capability stopped being the differentiator and governance took its place. In 2025 the enterprise question was "can it do this?" In 2026 it is "can I turn it off, where does the data go, and what does it cost when 400 people use it?" Both Figma and Miro have responded by shipping admin controls and policy toggles faster than they have shipped new generation features, which tells you where their enterprise customers are actually pushing.

Quality has improved across the board, but the biggest gains are in understanding context. AI design tools now recognize existing design systems, respect brand guidelines (when provided), and generate content that fits within established visual languages rather than producing generic output in a vacuum.

The gap between AI-generated starting points and production-ready designs has narrowed but not closed. Every tool tested still requires human refinement for professional output. The time savings are real — roughly 40-60% reduction in production time for routine design work — but the "AI replaces designers" narrative remains premature. What's actually happening is that designers can take on more projects and spend more time on the creative decisions that define great design.

Frequently Asked Questions

Figma AI vs Miro AI: which is better for enterprise design teams?

They solve different halves of the workflow, so most enterprises run both rather than choosing. Figma AI is the better investment when the output is a shipped interface: it generates and edits inside design files that already carry your components, styles, and variables, and it hands off to engineering through Dev Mode. Miro AI is the better investment when the output is a decision: it clusters research notes, summarises workshops, turns discussion into diagrams, and produces low-fidelity concepts that feed the Figma stage. If you genuinely must pick one, pick based on where your bottleneck is — production throughput points to Figma, alignment and discovery time points to Miro.

What Figma AI features are available as of March 2026?

As of March 2026 the shipped Figma AI surface covers four areas: in-file assistance (auto-layout suggestions, bulk layer renaming, visual search across your files, background removal and image upscaling), design generation that resolves against your own design system tokens rather than generic styles, the Make prompt-to-working-prototype surface introduced at Config 2025, and the Dev Mode MCP server that exposes design context to external AI coding agents. Feature availability differs by plan and by admin policy, so confirm your tier in Figma's own release notes before you plan a rollout around any single capability.

What Miro AI features are available as of February 2026?

As of February 2026 Miro AI covers canvas intelligence (clustering and summarising stickies, generating mind maps, diagrams and presentations from a prompt or from selected content), research synthesis over interview notes and feedback, UI and prototype generation built on the Uizard technology Miro acquired in 2024, and task-scoped AI sidekicks that carry domain context across a board. AI usage is metered with credits pooled at the organisation level rather than granted per seat, which is the detail that most often surprises teams after rollout.

What gates enterprise adoption of AI design tools?

In practice four things, and none of them is feature quality. First, whether AI processing can be turned off per workspace and whether customer content is excluded from model training by default on your tier. Second, identity and lifecycle controls — SAML SSO and SCIM provisioning so AI-enabled seats are deprovisioned automatically. Third, data residency and retention, including whether AI prompts and outputs inherit the same region and retention policy as the rest of the workspace. Fourth, the commercial model: metered AI credits make spend unpredictable, and procurement will ask for a ceiling before signing. Teams that lose six months to a security review almost always lost it on points one and three.

Does Figma or Miro train AI models on your design files?

Both vendors provide administrator controls to exclude workspace content from model training, and both treat business and enterprise tiers differently from free and individual tiers. Because these defaults have changed more than once and vary by plan and region, the only safe answer for a procurement document is the one you read yourself: check the current terms in each vendor's trust centre and in your workspace admin settings, capture a screenshot with a date, and re-verify at renewal. Do not rely on a third-party article — including this one — as the evidence in a security review.

How much do Figma AI and Miro AI cost?

Neither is priced as a standalone product. Figma bundles AI into paid seats, with heavier generation surfaces subject to usage limits that vary by plan, so the marginal cost of AI for an existing Figma customer is usually zero until a team leans hard on generation. Miro meters AI with credits allocated by plan and pooled across the organisation, so cost scales with how much synthesis work you actually run. Model both the same way: take your current seat count, estimate generations per designer per week, and check what happens when the allowance runs out — throttling, a hard stop, or an overage charge.

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If you're interested in how AI is transforming other creative workflows, see our guides to AI writing tools, AI video generators, and AI project management tools.