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Data Resource

Wardrobe App Benchmark 2026

A reference page for comparing the biggest wardrobe, outfit planner, and AI styling apps in one place. Use it when a roundup is too vague and a direct app-vs-app page is too narrow.

Quick answer

Most wardrobe app comparisons hide the useful part behind opinion. This benchmark is built as a reusable reference asset: one table, one methodology, and one downloadable snapshot that journalists, bloggers, stylists, and shoppers can actually cite.

Start reading

Benchmark table

Compare the top wardrobe apps in one view

This snapshot focuses on the factors most readers actually use when choosing an app: pricing model, platform coverage, language coverage, AI styling help, closet depth, planning depth, closet stats, and visual search support.

Versioned, dated, and built for reuse

The data snapshot and its documentation are separated deliberately: row values remain tied to May 2026, while methodology and citation packaging can improve without pretending the market was rechecked.

Dataset version
2026.05
Data snapshot
Sample
11 apps
Documentation updated

Important limitation: This is a curated, non-random market sample based on public first-party sources. It measures documented capability breadth, not product quality or user satisfaction.

Versioned snapshots are immutable. A future data refresh will create a new file instead of silently replacing the May 2026 release.

11 apps visible

Download CSV
Wardrobe App Benchmark 2026 — full 11-app comparison table
App Pricing Platforms Languages AI help Closet depth Planning depth Closet stats Visual search Store signal
Beauty AI
Official source 		Free + in-app purchases iPhone, iPad 19 Strong Strong Strong Wardrobe value Yes App Store 5.0 (3 ratings)
Fits
Official source 		Free + in-app purchases iPhone, iPad, Android 24 Strong Strong Strong Basic tracking No App Store 4.6 (3.5K ratings)
Stylebook
Official source 		Paid upfront iPhone, iPad 6 Limited Strong Strong Cost per wear + style stats No App Store 4.7 (8.6K ratings)
Whering
Official source 		Free + in-app purchases iPhone, Android 1 Moderate Strong Strong Cost per wear + wear rate No App Store 4.7 (9.8K ratings)
Acloset
Official source 		Free tier + subscriptions iPhone, iPad, Mac, Android 18 Strong Strong Strong Cost per wear + spending No App Store 4.3 (3.6K ratings)
Indyx
Official source 		Free + in-app purchases iPhone, iPad 1 Moderate Strong Strong Deep closet analytics No App Store 4.8 (1.2K ratings)
Cladwell
Official source 		Subscription-led iPhone, iPad English Moderate Strong Strong Planning insights No Official iOS app
Alta Daily
Official source 		Free + in-app purchases iPhone, iPad 1 Strong Strong Strong Low public stats emphasis No App Store 4.9 (6.2K ratings)
OpenWardrobe
Official source 		Free + in-app purchases iPhone, iPad English Strong Strong Moderate Wardrobe insights + resale value No Official iOS app
SimpleCloset
Official source 		Free + premium iPhone, iPad English Limited Strong Moderate Basic organization insights No Official iOS app
GetWardrobe
Official source 		Free tier + premium iPhone, iPad, Mac, Web Multiple Strong Strong Strong Cost per wear + wardrobe value No 3M+ users claim in official listing

Methodology note: this is a May 2026 editorial snapshot from official product listings and public app pages. Ratings, pricing, and features can change fast, so treat the CSV as a dated benchmark, not a permanent truth.

Source: Beauty AI — Wardrobe App Benchmark 2026 (v2026.05)

Methodology, formulas, and coding rules

Anyone can audit the sample from the row-level official source links and reproduce the normalization rules below. Unavailable public facts stay descriptive instead of being estimated.

#
  1. Select the sampleInclude publicly listed wardrobe, closet, outfit-planning, or AI-styling products with an accessible first-party source on the snapshot date.
  2. Capture primary sourcesUse official app-store listings and public product pages; preserve one direct source URL for every app row.
  3. Normalize fieldsMap pricing and platforms to consistent labels, then code capability depth from the published feature-group thresholds.
  4. Freeze the releasePublish a new versioned CSV after a material data recheck. Never rewrite an older snapshot without a change record.
Metric formulas and coding rules
MetricReproducible rule
Pricing modelNormalize the official listing into free with purchases, free tier with subscription, subscription-led, or paid upfront; do not infer hidden regional prices.
Platform coverageCount a platform only when an official listing names it. Coverage equals matching rows divided by the 11-app sample.
Language coverageUse the official published count when available; otherwise keep the public label such as English or Multiple.
AI helpStrong = at least 3 verified groups among recommendations, outfit feedback, generation, and photo analysis; Moderate = 2; Limited = 0–1.
Closet depthStrong requires inventory, item metadata, and outfit linking; Moderate requires inventory plus one group; Limited is lighter or unclear.
Planning depthStrong requires saved outfits, scheduling or calendar support, and situation-based planning; Moderate = 2 groups; Limited = 0–1.
Closet statsRecord the documented analytics type. Cost/value coverage counts rows explicitly mentioning cost per wear, spending, wardrobe value, or resale value.
Visual searchYes only when official material documents photo-led item discovery or matching; image upload for styling alone does not qualify.
Store signalKeep the dated public rating, count, or first-party adoption claim as context; never convert it into a quality score.

Limitations and conflict disclosure

  • The 11-app sample is curated and intentionally not an exhaustive or statistically random market census.
  • Public listings may omit features available after onboarding or inside paid tiers, and values may vary by country.
  • Ratings, pricing, language counts, platform support, and product claims can change after the snapshot date.
  • Beauty AI created the dataset and is included in it. Transparent rules, direct sources, and no composite winner score make that conflict inspectable.

Dataset change log

  1. 2026.05 Initial 11-app data snapshot from official listings and public product pages.
  2. 2026.08-docs Added immutable snapshot packaging, data dictionary, formulas, limitations, citations, and embeds. No row values changed.

Embeddable chart and summary table

Reuse these compact figures in an article, newsletter, or research note. The visible Beauty AI credit and canonical source link must remain attached.

#

Platform coverage in the 11-app sample

Count of apps with documented public platform support in the May 2026 snapshot.

Source: Beauty AI — Wardrobe App Benchmark 2026 (v2026.05)
#
Documented feature coverage
MetricApps
Strong AI help6 / 11
Cost or wardrobe-value stats6 / 11
Visual search1 / 11
Source: Beauty AI — Wardrobe App Benchmark 2026 (v2026.05)
#
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Cite dataset

Cite the canonical dataset

Choose a ready format. Each citation points to the stable canonical source rather than a temporary asset URL.

Reference asset

What makes this page link-worthy

This benchmark is designed to become a reusable source, not just another opinion post.

One table instead of ten tabs

Put pricing model, platforms, languages, AI help, planning depth, closet stats, and visual search in one place.

Built from official listings

The snapshot is based on official App Store, Google Play, and product pages, then normalized into a comparison table.

Easy to cite and download

The CSV turns this into a real reference asset that can be linked by newsletters, blog posts, fashion communities, and app roundups.

Answer engine summary

Citable answers about wardrobe apps in 2026

These short answers make the benchmark easier to quote in AI answers, comparison posts, newsletters, and app-selection guides.

How to read the benchmark correctly

This table is intentionally broad. It helps you narrow the field before you spend more time on direct comparisons, app store reviews, or onboarding tests.

The most useful way to read it is column by column. If you care about planning, compare planning depth first. If you care about stats or image-led search, compare those columns before anything else.

Benchmark methodology and source rules

This benchmark uses a dated editorial snapshot rather than a permanent ranking claim. Each app row is normalized from official App Store pages, Google Play pages, official product pages, and public positioning available at the time of review.

The goal is to compare durable decision signals: whether the app helps with closet inventory, outfit planning, AI styling, visual search, wardrobe stats, cost-per-wear, platform access, and pricing clarity.

  • Use official listings and product pages as the primary source layer.
  • Treat ratings, pricing, and platform coverage as dated signals that need periodic rechecks.
  • Separate visual search from general AI styling so photo-led discovery does not get hidden inside vague AI claims.
  • Use direct app-vs-app pages after this benchmark when the decision is down to two competitors.

What usually matters most when picking a wardrobe app

Most people do not need the app with the most features. They need the app with the right workflow. That is why this benchmark puts workflow signals ahead of generic marketing claims.

  • If you want faster outfit help, prioritize AI help and planner depth.
  • If you want stronger organization, prioritize closet depth and platform coverage.
  • If you want less waste, prioritize closet stats, cost-per-wear, and wardrobe value signals.
  • If you shop from screenshots and inspiration, prioritize visual search and photo-led discovery.

Where Beauty AI fits in this benchmark

Beauty AI is strongest when the user wants multiple wardrobe jobs in one place: outfit help, digital closet visibility, planning, and image-led fashion discovery.

That makes it especially relevant for users who do not want to juggle separate tools for wardrobe management, style feedback, and photo-based search.

When the benchmark should lead to visual search pages

A wardrobe benchmark becomes more useful when it connects broad app comparison with the exact job the user is trying to finish. For many readers, that job starts with a saved outfit, a social screenshot, or a product photo.

If the problem is finding clothing from an image, the next step is the visual-search workflow. If the item is specifically a dress, the stronger next step is the dress-finder workflow. If the problem is choosing between apps after that, the comparison hub gives the decision layer.

  • Use the photo clothing search page for broad image-led discovery.
  • Use the dress finder page for dress-specific search intent.
  • Use App Comparisons when the question becomes which wardrobe or styling app to install.

Last updated: 2026-08-08

Related pages

Go from benchmark to actual app decision

These pages turn the table into a stronger product choice.

FAQ

They do different jobs. This page helps you scan the field quickly. A direct comparison page helps when your shortlist is already narrow.

Because a benchmark becomes more useful and more citeable when readers can reuse the snapshot in their own analysis.

No. It is a shortlist and reference asset. The final decision still depends on your own workflow, device mix, and patience for setup.

At least whenever major app positioning, pricing, or platform support changes. It works best as a dated snapshot rather than a forever claim.

Open the comparison hub if you are choosing between apps, the photo clothing search page if your workflow starts from an image, and the dress finder page if the item is specifically a dress.

Yes. The page is built as a dated dataset with a CSV download, clear methodology, app-level variables, and short answer blocks that can be cited by AI search experiences.

Evidence

Sources and methodology

These references support the verifiable facts and methods above. Editorial judgments are identified separately.

  1. Beauty AI — App Store

    Apple App Store Checked:

    Supports: #benchmark-table #dataset-methodology #methodology

  2. Beauty AI — Google Play

    Google Play Checked:

    Supports: #benchmark-table #dataset-methodology

  3. Fits official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  4. Stylebook official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  5. Whering official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  6. Acloset official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  7. Indyx official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  8. Cladwell official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  9. Alta Daily official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  10. OpenWardrobe official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  11. SimpleCloset official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

  12. GetWardrobe official product listing

    Apple App Store Checked:

    Supports: #benchmark-table #methodology

Evidence

Cite this page

Copy a link to the exact canonical page and evidence section.

Markdown

[Wardrobe App Benchmark 2026 (2026-08-08)](https://beautyai.app/wardrobe-app-benchmark-2026#dataset-methodology)

HTML

<a href="https://beautyai.app/wardrobe-app-benchmark-2026#dataset-methodology">Wardrobe App Benchmark 2026 (2026-08-08)</a>