Best Virtual Try-On Solutions for Retailers

E-commerce virtual try-on has moved beyond novelty AR filters. For retailers, the practical question is whether a platform can represent the products they actually sell, integrate with the existing storefront, digitize a changing catalog without creating an operational bottleneck, and give shoppers enough visual information to make a more confident purchase.

That last point matters. A 2025 study published in Frontiers in Virtual Reality examined AR cosmetic try-on among 634 Italian consumers and found that perceived informativeness was an important predictor of behavioral intention, with enjoyment acting as a key mediator. The researchers also found that excessive simplicity or playfulness could increase doubt and reduce trust. In other words, retailers should evaluate virtual try-on as a decision-support experience, not simply an engagement feature.

The six platforms below approach that problem differently. Some specialize in beauty or eyewear, while others concentrate on apparel, footwear, or fit. I compared them primarily on product-category coverage, storefront deployment, product digitization, shopper experience, catalog scalability, and publicly available pricing.

Quick comparison of the best e-commerce virtual try-on solutions

Solution	Best for	Main categories	Deployment	Published entry pricing
1. Banuba	Multi-category beauty and accessories retailers	Makeup, skincare, eyewear, contacts, hair color, headwear, jewelry	Web, e-commerce integrations, mobile and enterprise deployments	From $49/month
2. Perfect Corp.	Beauty and fashion brands needing broad AI/AR capabilities	Makeup, hair, eyewear, jewelry, watches, clothing, shoes	Web, mobile SDK and enterprise omnichannel options	Varies by product/service
3. Fittingbox	Specialist eyewear retailers	Prescription glasses and sunglasses	E-commerce, web and in-store	Shopify plans reported from $59/month
4. Revieve	Beauty retailers combining try-on with personalization	Makeup and hair color	Cross-channel beauty experiences	Contact sales
5. WEARFITS	Footwear and bag retailers	Shoes, bags and related fashion categories	API, SDK, Shopify and WooCommerce	Shopify offering from $49/month
6. Style.me	Apparel retailers focused on fit and styling	Clothing, footwear and accessories	E-commerce plugins and web experiences	Contact sales

How I evaluated the virtual try-on solutions

A useful virtual try-on comparison cannot rely on AR rendering alone. Different retail categories create fundamentally different technical requirements: lipstick needs accurate facial segmentation and color rendering, eyewear requires stable face tracking and realistic frame scale, while clothing introduces body measurements, sizing, garment geometry, and fit.

I therefore prioritized category fit first. Retailers should shortlist vendors according to what they sell rather than choosing the provider with the longest generic feature list.

I also examined deployment and integration. A retailer running Shopify has different requirements from an enterprise building try-on into native mobile applications, physical stores, and a headless commerce stack, so available plugins, web modules, APIs, SDKs, and omnichannel deployment options matter.

Catalog digitization was another major consideration. A convincing demonstration with ten products does not necessarily indicate that a retailer can economically maintain hundreds or thousands of changing SKUs. The required input assets, digitization workflow, SKU limits, and ongoing catalog-management burden can therefore be as important as rendering quality.

Finally, I considered pricing transparency and scalability where public information was available. Vendors meter usage differently, including by try-on volume, SKU count, or custom enterprise contracts, so headline monthly prices are not directly comparable.

1. Banuba: Best for retailers needing multi-category AR try-on without a narrow vertical focus

Banuba provides AR and AI technology for beauty and retail applications, with its TINT virtual try-on offering covering several categories that frequently coexist in beauty, fashion-accessory, and lifestyle catalogs. Its current e-commerce offering includes makeup, skincare, eyewear, contact lenses, hair color, headwear, and jewelry.

That breadth is one reason Banuba is particularly relevant to retailers that do not fit neatly into one product vertical. Instead of deploying an eyewear-specific system and then sourcing separate technology for cosmetics or jewelry, merchants can use Virtual Try-On across multiple supported categories.

The underlying workflow combines computer vision with AR rendering. For facial products, Banuba detects and tracks facial features before positioning virtual products in the appropriate location; analogous tracking technologies are used for categories such as rings and bracelets. The platform also offers AI recommendations, multi-item try-on, analytics, and product digitization capabilities.

Deployment options range from a self-service approach to Shopify and custom enterprise integration. Banuba’s public pricing currently lists a Basic plan at $49 per month for 1,000 virtual try-ons, Pro at $99 for 3,500, and Smart at $349 for 15,000, with a 14-day trial and an Enterprise tier for custom requirements. The company separately prices its embedded Shopify offering and custom deployments.

For retailers, the practical advantage is the combination of category breadth and a relatively accessible path to experimentation. A merchant can test the commercial case before committing to the type of custom computer-vision project that historically made AR commerce primarily an enterprise initiative.

What I like about Banuba

• Broad category coverage: One platform covers beauty products alongside eyewear, contacts, headwear, and jewelry, which can reduce the need to integrate separate specialist systems.
• Accessible starting point: The self-service offering begins at $49 per month and includes a 14-day trial, giving smaller retailers a practical way to validate virtual try-on before expanding usage.
• Multiple deployment paths: Retailers can start with simpler web or commerce implementations while enterprise buyers can pursue customized deployments.

Where Banuba falls short

• Usage-based limits require forecasting: The self-service tiers cap monthly virtual try-ons, so high-traffic merchants need to model expected usage rather than comparing subscription prices alone.
• Different deployment paths have different economics: Banuba’s self-service, Shopify, and custom offerings are priced differently, making it important to compare the specific implementation a retailer actually needs.

2. Perfect Corp.: Best for beauty brands building a wider AI and AR commerce stack

Perfect Corp. has developed an extensive portfolio around beauty and fashion technology. Its virtual try-on capabilities now span clothing, shoes, jewelry, eyewear, makeup, and hair, while its broader enterprise portfolio includes skin analysis and other AI-powered beauty experiences.

This makes Perfect Corp. particularly relevant when virtual try-on is only one part of a larger beauty personalization strategy. A cosmetics retailer, for example, may want makeup visualization alongside skin analysis, shade-related experiences, hair tools, and additional recommendation features rather than deploying a standalone AR widget.

Its makeup service supports categories including lip color, blush, eyelashes, eyeliner, eyeshadow, foundation, and mascara, with integrations documented for platforms including Shopify, WooCommerce, Wix, and Squarespace. Perfect Corp. also provides separate eyewear services and enterprise deployment options.

The pricing structure varies by solution. Perfect Corp.’s self-service makeup offering currently lists Essential and Premium tiers with SKU limits, while Enterprise moves to custom pricing and expands category and channel coverage. Its eyewear service similarly uses SKU and user allowances before moving to an unlimited Enterprise tier.

What I like about Perfect Corp.

• Deep beauty ecosystem: Virtual makeup can sit alongside skin, hair, face, and other beauty technologies rather than operating as an isolated product visualization feature.
• Wide category expansion: Perfect Corp. now supports virtual experiences beyond cosmetics, including eyewear, jewelry, clothing, and shoes.
• SMB and enterprise paths: Self-service web products give smaller retailers an entry point, while enterprise deployment extends into mobile, in-store, and additional channels.

Where Perfect Corp. falls short

• Plan comparison can become complex: Makeup, eyewear, and enterprise services have different pricing and capability structures, so retailers need to compare the relevant product rather than treating Perfect Corp. as one uniform subscription.
• Self-service plans impose SKU limits: Current makeup and eyewear plans limit catalog size before Enterprise, which matters for retailers with large or frequently changing assortments.
• Full omnichannel capability is an enterprise consideration: Perfect Corp. states that its Online Service is browser-based, while mobile app, in-store, and broader omnichannel support sits within its Enterprise offering.

3. Fittingbox: Best for eyewear retailers with large frame catalogs

Fittingbox takes a much more specialized approach. Rather than trying to support every retail category, the company focuses on eyewear, combining virtual try-on with frame digitization and optical-commerce capabilities.

Its biggest differentiator is catalog infrastructure. Fittingbox says its database contains more than 195,000 3D frame references from over 1,200 brands, which can be particularly useful for optical retailers selling products from multiple manufacturers rather than only their own private-label collection.

The virtual try-on itself supports real-time frame visualization across devices, while Fittingbox also provides pupillary-distance measurement technology and frame digitization. Its retail solutions extend across websites, e-commerce, and physical stores, making the platform relevant to optical businesses operating both online and offline.

Fittingbox’s 2026 Shopify materials list plans beginning at $59 per month, with higher tiers at $99 and $199. Retailers evaluating a larger or more customized implementation should verify pricing directly for their specific catalog and deployment requirements.

What I like about Fittingbox

• Eyewear specialization: The product is designed around the details optical retailers care about, including realistic frames, lenses, sizing, and PD-related workflows.
• Large existing 3D catalog: More than 195,000 references across over 1,200 brands can reduce the asset-creation problem for retailers whose inventory overlaps with the database.
• Omnichannel relevance: Fittingbox supports virtual eyewear experiences across e-commerce and in-store environments.

Where Fittingbox falls short

• Its specialization limits cross-category use: A retailer wanting one system for makeup, apparel, jewelry, and eyewear will need additional technology outside Fittingbox’s core optical focus.
• Catalog advantage depends on inventory overlap: The value of the existing 3D database is highest when a retailer’s frames are already represented or can be efficiently digitized.
• Broader deployments require a more detailed quote: The published Shopify entry price does not represent every implementation scenario.

4. Revieve: Best for beauty retailers combining virtual makeup with personalization

Revieve approaches virtual try-on as part of a broader digital beauty journey. Its platform combines live AR experiences with beauty analysis, recommendations, and personalization, making it particularly relevant to retailers that want to guide product discovery rather than simply place a virtual cosmetic layer over a shopper’s face.

Its Makeup Virtual Try-On supports products across face, lip, eye, and eyebrow categories and can also create more complete makeup looks. Revieve positions the experience for cross-channel deployment, allowing retailers to connect individual product experimentation with bundles, curated looks, and other beauty journeys.

That approach can be useful for retailers with large cosmetics assortments where the problem is not simply “will this lipstick shade suit me?” but “which combination of products should I consider?” Revieve’s broader platform includes skin and hair analysis and personalized routines alongside live makeup and hair-color try-on.

What I like about Revieve

• Personalization goes beyond visualization: Retailers can connect virtual makeup to product discovery, recommendations, and more complete beauty journeys.
• Full-look experiences: Support for individual products, bundles, and complete makeup looks can encourage exploration across a catalog.
• Strong beauty focus: Skin, hair, makeup, and recommendation capabilities make the platform relevant to retailers building a unified digital beauty experience.

Where Revieve falls short

• It is primarily a beauty platform: General fashion or optical retailers looking for a category-neutral AR engine will find other providers better aligned with their catalogs.
• Pricing is not prominently self-service: Retailers should expect a sales-led evaluation rather than relying on a simple public subscription ladder.
• Its broader value requires a broader project: A merchant needing only a lightweight try-on button may not need the surrounding personalization capabilities that differentiate Revieve.

5. WEARFITS: Best for footwear and bag retailers concerned about catalog scale

WEARFITS focuses on fashion categories where product geometry and scale make virtual visualization difficult, particularly footwear and bags. Its technology combines AR and 3D capabilities with AI-powered generation of 3D assets from 2D images.

For retailers, the interesting part is not simply seeing a shoe through a phone camera. Catalog operations are often the larger obstacle: if every new SKU requires an expensive manual 3D workflow, virtual try-on becomes difficult to maintain once the initial pilot ends.

WEARFITS addresses that issue with asset-generation workflows and supports integration through API, SDK, Shopify, and WooCommerce. Its current materials also describe sizing capabilities for footwear, extending the buying experience beyond visual style toward whether the shopper has selected an appropriate size.

The company advertises a Shopify entry point from $49 per month, although enterprise retailers with substantial catalogs should evaluate the economics of their specific asset volume, channels, and integration requirements rather than extrapolating from the starter price.

What I like about WEARFITS

• Strong footwear and bag focus: The platform targets categories where specialized object tracking and 3D asset workflows matter.
• Multiple integration methods: API, SDK, Shopify, and WooCommerce support make the platform viable across different commerce architectures.
• Attention to asset scalability: AI-assisted 3D generation from 2D imagery can address one of the operational bottlenecks of catalog-wide AR deployment.

Where WEARFITS falls short

• It is not a beauty solution: Cosmetics, hair color, and facial beauty experiences require a different class of computer-vision capabilities.
• Its strongest differentiation is category-specific: Retailers outside footwear, bags, and adjacent fashion use cases may not benefit from the same strengths.
• Enterprise cost still requires workload analysis: Starter pricing does not indicate the total cost of running large catalogs across several commerce channels.

6. Style.me: Best for apparel retailers prioritizing fit, size, and outfit building

Style.me illustrates why apparel virtual try-on should often be evaluated separately from facial AR. Its virtual fitting room allows shoppers to create an avatar using measurements and body-shape selections, receive size recommendations, visualize garment fit, combine products into outfits, and add items to the cart from the experience.

This shifts the value proposition from pure visualization toward fit and styling. For apparel retailers, seeing a digital shirt is only part of the purchasing problem; shoppers also need to understand size, proportions, how garments work together, and whether an item is likely to fit their body.

Style.me handles product digitization and says its virtual fitting room can plug into major e-commerce platforms, including Shopify and Magento. The company also offers AR experiences for footwear and other fashion products, extending its technology beyond avatar-based clothing fitting.

The company does not present a simple public subscription price for the core virtual fitting solution on the pages reviewed, so retailers should request a quote based on catalog and implementation requirements.

What I like about Style.me

• Fit is treated as a core problem: Measurement-based size recommendations provide information that a purely visual AR overlay cannot.
• Outfit creation encourages discovery: Shoppers can combine garments and explore multiple products inside the fitting-room experience.
• Retailer analytics add another use case: Style.me provides data around shopper measurements, sizing, and styling behavior that can inform merchandising decisions.

Where Style.me falls short

• Digitization adds an operational step: Style.me handles garment digitization, but retailers still need to account for that process when introducing and refreshing collections.
• It is much more relevant to fashion than beauty: Retailers primarily selling cosmetics or eyewear need category-specific technology instead.
• Public pricing is limited: Buyers cannot estimate total deployment cost as easily as with vendors publishing self-service subscription tiers.

What retailers should look for in an e-commerce virtual try-on platform

Start with the product, not the technology label

“Virtual try-on” describes several different technical problems. Face tracking for lipstick, hand tracking for rings, 3D frame placement for glasses, foot tracking for sneakers, and body-based garment fitting should not be treated as interchangeable features.

Before comparing vendors, define which SKUs need try-on and what shoppers need to learn from the experience. A cosmetics customer may need realistic color and texture; an eyewear customer needs scale and frame positioning; an apparel shopper may care far more about sizing than photorealistic rendering.

Calculate the cost of digitizing the catalog

The subscription is only one component of virtual try-on economics. Retailers should determine what source assets are required, who creates them, how long each SKU takes to prepare, whether digitization carries an additional charge, and what happens when hundreds of seasonal products arrive simultaneously.

This becomes increasingly important as catalogs grow. A technically impressive experience that requires an expensive manual workflow for every new product can be commercially weaker than slightly less sophisticated rendering backed by a much faster asset pipeline.

Separate visual try-on from fit and sizing

AR visualization can answer “how does this look on me?” without necessarily answering “will this physically fit me?”

That distinction matters particularly in apparel, footwear, rings, and eyewear. Retailers aiming to reduce size-related returns should check whether a provider supplies measurement or sizing technology rather than assuming visual placement itself guarantees fit accuracy.

Test the experience on ordinary customer devices

Retailers should test camera initialization, loading time, tracking stability, low-light performance, mobile browser behavior, older devices, and what happens when shoppers decline camera permissions.

The 2025 Frontiers study is useful here because it suggests that informativeness, enjoyment, ease of use, trust, and doubt interact in more complicated ways than “more AR equals better UX.” A virtual try-on should help shoppers evaluate products while remaining credible and understandable.

Compare pricing using your own traffic and catalog

A plan charging by virtual try-ons behaves differently from one charging by SKU count. A retailer with 50 products and millions of monthly visits may prefer a completely different pricing structure from a merchant carrying 20,000 products with comparatively modest traffic.

Build a forecast using expected eligible SKUs, monthly product-page visitors, try-on adoption rate, peak-season traffic, asset-production costs, implementation work, and any enterprise support charges before comparing vendors on price.

Decide whether you need a plugin, API, or SDK

A ready-made e-commerce integration is usually preferable when the desired experience is a conventional try-on button on product pages. It reduces implementation effort and lets merchandising teams focus on catalog management.

APIs and SDKs become more important when retailers need custom interfaces, native mobile experiences, marketplaces, loyalty-app integration, physical-store displays, proprietary analytics, or other workflows that cannot be accommodated by an off-the-shelf widget.

FAQ

What is e-commerce virtual try-on?

E-commerce virtual try-on uses technologies such as augmented reality, artificial intelligence, computer vision, 3D rendering, or digital avatars to help shoppers visualize products on themselves before purchasing online.

Depending on the category, that can mean applying virtual lipstick to a live camera feed, placing glasses on a tracked face, displaying shoes on a shopper’s feet, or visualizing clothing on an avatar with personalized measurements.

Does virtual try-on require a mobile app?

Not necessarily. Several providers support browser-based virtual try-on that works directly from an e-commerce site, while others also provide native SDKs for retailers building the capability into mobile applications.

The right approach depends on where customers shop. Requiring an app download can add friction for web-first retailers, whereas native integration can make sense when an established retail app already accounts for significant customer activity.

Do retailers need 3D models for every product?

It depends on the provider and category. Some experiences require 3D assets, while other systems can create usable assets from product photography or product parameters. Perfect Corp., for example, notes that not every category requires 3D assets and that some experiences can work from 2D imagery.

Retailers should verify the asset requirements for their exact product category before signing a contract because catalog digitization can materially affect both deployment time and total cost.

What is the difference between virtual try-on and a virtual fitting room?

Virtual try-on is the broader category and often focuses on visualizing a product on the shopper through AR or an uploaded image. Virtual fitting rooms generally go further for apparel by incorporating avatars, measurements, garment sizing, outfit creation, or fit recommendations.

The distinction matters because a realistic visual representation does not automatically provide accurate sizing guidance.

Choosing the right virtual try-on solution

Retailers should choose according to the hardest problem in their catalog rather than treating every virtual try-on provider as a direct substitute.

Specialized catalogs warrant specialized comparisons. Fittingbox deserves consideration when eyewear is the core business, WEARFITS is oriented toward footwear and bag visualization at catalog scale, and Style.me addresses the more complex sizing and styling requirements of apparel.

Whichever route a retailer takes, the most useful proof of concept is not the most impressive demo. It is the one that tests real products, ordinary customer devices, realistic traffic, asset-production workload, and the commercial metrics the retailer ultimately wants the experience to improve.