Wildberries Scraper — Product, Price, Discount & Stock Data

Wildberries scraper (парсер Wildberries) that extracts structured product data from wildberries.ru, Russia's largest online marketplace. Search by keyword and get clean rows with current price, original price, discount percentage, stock quantity, rating, review count, brand and seller — at $0.001 per product, and never billed twice for the same SKU.

Unofficial tool. This Actor is not affiliated with, endorsed by, or connected to Wildberries or its operators in any way. "Wildberries" is used solely to describe the publicly accessible website this Actor reads. All trademarks belong to their respective owners.

Cheap by design: 100 products per request, straight from JSON. No headless browser, no per-product page fetches.

Throughput is deliberately throttled rather than fast. Wildberries returns HTTP 429 to parallel requests from datacenter IPs, so the defaults are 1 concurrent request at 6 per minute — about 600 products per minute, so a 1,000-product run takes roughly two minutes. Raising those defaults on datacenter IPs does not make it faster, it makes it return nothing. With residential proxies you can raise both.

What you get

Field	Description
sku	Wildberries product ID
title	Product name
brand	Brand name (null when the seller left it blank)
price	Current selling price
originalPrice	Pre-discount list price, when genuinely marked down
discountPct	Discount percentage, computed from the two prices
currency	RUB
inStock	Availability as a boolean
stockQuantity	Units available — not just in/out
rating	Average review score
reviewCount	Number of reviews
supplier	Selling entity
url	Canonical product URL
scrapedAt	ISO timestamp

categoryPath, image and description are always null in Wildberries mode — the search endpoint does not return them. Image URLs are deliberately omitted rather than guessed: Wildberries shards images across numbered CDN baskets whose ID ranges shift over time, and a plausible-looking dead link is worse than an honest null.

Real per-SKU stock (optional)

Most Wildberries scrapers report the search endpoint's quantity field as if it were stock. It isn't. Measured across three runs (100 headphones, 200 smartphones, 200 sneakers) it returns one repeated value for 65–95% of rows — 46 in one run, 48 in two others — with only a handful of genuine numbers. A "stock" column that says the same thing about two thirds of the catalogue cannot tell you anything about any single product.

Switch on Verify real stock per product and each search page makes one extra batched request to Wildberries' product-card endpoint, which does hold real per-SKU quantities. Measured on the same 100 headphones:

	Search field	Card endpoint
Distinct values per 100 rows	7	22
Distinct values per 300 rows	—	42
Genuine zero-stock rows found	0	10 per 100
Low-stock numbers (1, 2, 13, 14…)	almost none	yes

So out-of-stock detection and low-stock alerts actually work — which is the question sellers are really asking.

The ceiling, stated plainly. Wildberries clusters a large share of rows on a single high value, and that value moves between runs. Measured: one run topped out at 46 with 61 of 100 rows on it; a larger run topped out at 52 with 157 of 300 rows on it — same region, same kind of query. Below the ceiling the numbers vary genuinely (42 distinct values in that 300-row run) and include real zeros.

So treat a row sitting on the run's maximum as "at least this many", and anything below it as a real count. Every run's log prints the ceiling it actually observed, e.g.:

stock ceiling observed this run: 52. Wildberries clusters a large share of rows
on the run maximum, and that maximum moves between runs (46 and 52 both seen)...

There is deliberately no hardcoded cap and no per-row "capped" flag. An earlier version asserted the ceiling was always 46; on the very next larger run that mislabelled 161 rows. Reporting the observed maximum per run cannot go stale in that way.

Every row also carries stockSource:

Value	Meaning
card	Quantity verified against the product card
search-fallback	Card lookup was requested but did not cover this SKU — the search value is kept, unverified
search	Verification was off; this is the unreliable listing value

Cost. One fetch-detail event per batch of 50 products, not per product — so verifying 1,000 products costs 20 extra events, and the per-product price is unchanged. Off by default, because you should not pay for it unless you want it.

Sample output

Real rows from a live run, unedited — search term наушники, 100 products, stock verification on:

title	brand	price	was	disc %	stock	src	rating	reviews
Наушники проводные совместимы…	STIMAXON	358	960	62.7	52	card	4.5	14325
Наушники проводные Type-C совм…	null	296	2100	85.9	2	card	4.9	3359
Наушники проводные	Borofone	203	798	74.6	0	card	4.7	617

Those three rows are chosen to show the range: one sitting on the run's ceiling (52, read as "at least 52"), one genuinely down to its last 2 units, and one actually out of stock. That is the difference the card lookup makes — without it all three would have read the same placeholder.

That run: 100 rows, 100 unique SKUs, 0 duplicates, 0 invalid discounts, 100/100 relevant to the query, 24 distinct stock values, 9 genuine zeros. Every field populated except brand, which 25 sellers left blank at source.

One complete row, every field the Actor emits:

{
  "sku": "337343445",
  "title": "Наушники проводные совместимы с iPhone с микрофоном",
  "brand": "STIMAXON",
  "price": 358,
  "originalPrice": 960,
  "discountPct": 62.7,
  "currency": "RUB",
  "inStock": true,
  "stockQuantity": 52,
  "stockSource": "card",
  "rating": 4.5,
  "reviewCount": 14325,
  "supplier": "STIMAXON",
  "categoryPath": null,
  "url": "https://www.wildberries.ru/catalog/337343445/detail.aspx",
  "marketplace": "wildberries.ru",
  "country": "russia",
  "image": null,
  "description": null,
  "extractedVia": "wb-api",
  "scrapedAt": "2026-08-04T17:05:41.092Z"
}

Note the discount depths. Wildberries sellers routinely inflate the list price, so a 95% markdown is the source's number, not a real saving. The Actor reports both prices and computes the delta; judging whether the "was" price was ever real is left to you.

Use cases

• Daily price monitoring — track competitor pricing and react to changes
• Discount and promotion tracking — real markdown depth, since both list and sale price are returned
• Stock and low-stock monitoring — with stock verification on, real per-SKU quantities let you spot a competitor down to their last few units, and genuine out-of-stock rows instead of a placeholder
• Repricing feeds — push live market prices into your own pricing engine
• Brand and seller monitoring — watch who is selling your brand and at what price
• Assortment research — see what sells in a category and at what price points

Input

Runs with zero configuration — press Start for a sample run.

{
  "site": "wildberries",
  "searchTerms": ["наушники"],
  "maxItems": 1000
}

Russian keywords give the best coverage. Each term is paginated until results run out or maxItems is reached. maxItems is a hard cap, so your bill stays predictable.

Reliability

Most scrapers break because they hardcode CSS class names that die on the next redesign. This one reads a JSON endpoint and validates every field on the way out:

• You are never billed for duplicate rows. Wildberries' own pagination repeats products across pages, and overlapping search terms surface the same items. Rows are deduplicated by SKU within a run before anything is emitted or charged. If you ask for 1,000 products and a keyword only has 600 unique ones, you get 600 real rows — not 1,000 rows with 400 repeats.
• Items that do not parse are skipped, never emitted half-filled
• A drop in the map-success rate raises an explicit warning naming the likely cause
• Rate-limit responses (429) are retried against fresh sessions automatically
• Every run reports its yield percentage, so a partially-degraded run is visible instead of silently returning half the data
• Fabricated responses are rejected, not sold to you. Wildberries does not only answer 429. A flagged request can also receive valid-looking JSON containing invented products — observed live: a search for наушники (headphones) that came back carrying a single evening dress while the envelope claimed 100 results. Such a payload is discarded and retried against a fresh session, so no decoy row is ever emitted or charged. Every run reports decoysRejected=N.
• Anti-bot wrappers are recovered, not thrown away. Some valid responses arrive wrapped in an injected <script> block. A 127KB response prefixed with 183 bytes of script still held 100 correct products, so the wrapper is stripped and the data kept rather than costing you a retry. Reported as interstitialsStripped=N.
• A blocked run fails instead of reporting success. If every request is rate-limited and nothing comes back, the run exits FAILED with the reason, because an empty dataset marked SUCCEEDED is indistinguishable from a healthy run. A search term with genuinely no matches still succeeds — the two cases are told apart by whether any request actually failed.
• Prices are validated: a "discount" is only reported when the list price is genuinely above the sale price

Wildberries' search endpoint is the one its own storefront uses. It is unauthenticated and stable in practice, but it is not a documented public API and carries no compatibility guarantee. The mapping layer is written to degrade into "zero products plus a clear warning" rather than to emit corrupted rows.

Also supports noon.com

Set site to noon for noon.com (UAE / Saudi Arabia / Egypt) in HTML mode. It works and yields 100% on retry, but it is far slower and more expensive per product because noon only exposes prices on detail pages and blocks aggressively — expect residential proxies to be necessary for volume. Wildberries is the recommended mode.

No personal data

Product and pricing fields only. No buyer, reviewer, or private-individual data is collected. supplier is a business trading name. robots.txt is fetched and enforced per origin before any request.

Аналитика и парсер Вайлдберриз — описание на русском

Парсер Вайлдберриз (Wildberries, ВБ) — аналитика товаров и продавцов, сбор данных с wildberries.ru через API. Поиск по ключевому слову возвращает готовую таблицу: название, бренд, текущая цена, цена до скидки, размер скидки в процентах, количество на складе, рейтинг, число отзывов и продавец. Также возвращаются SKU (артикул) и ссылка на карточку товара.

Что важно для аналитики Wildberries:

• Реальные остатки по каждому SKU — опция «Verify real stock per product». В поисковой выдаче Wildberries поле количества для 65–95% товаров возвращает одно и то же значение (46–48 в наших замерах) — это не остатки. С включённой проверкой мы дополнительно запрашиваем карточку товара: на тех же 100 товарах получилось 22 разных значения вместо 7 и 10 реально нулевых остатков вместо 0. То есть видно, у кого товар заканчивается или уже закончился. Важно про потолок: Wildberries собирает значительную часть строк на одном высоком значении, и это значение меняется между запусками — в одном прогоне максимум был 46 (61 строка из 100), в другом 52 (157 из 300). Значения ниже максимума меняются нормально (42 разных значения на 300 строк) и включают реальные нули. Поэтому строку, равную максимуму прогона, читайте как «не меньше этого», а всё, что ниже — как точное число. Максимум каждого прогона пишется в лог. Стоимость: одно событие fetch-detail на каждые 50 товаров, не за каждый товар.
• Реальная глубина скидки — возвращаются обе цены, поэтому скидка считается, а не берётся из баннера.
• Без дублей. Пагинация Wildberries повторяет товары между страницами. Строки дедуплицируются по SKU до оплаты: запросили 1000 товаров, а по запросу их всего 600 — получите 600 реальных строк, а не 1000 с повторами.
• Цена $0.001 за товар. 1000 товаров ≈ $1.
• Русские ключевые слова дают лучшее покрытие, чем английские.

Типовые задачи: мониторинг цен конкурентов, отслеживание скидок и акций, контроль остатков, репрайсинг, поиск нишевых товаров, проверка кто продаёт ваш бренд и по какой цене.

Официального публичного API продаж у Wildberries нет — динамика цен и остатков остаётся рабочим косвенным сигналом того, как торгует конкурент.

Local development

npm install
npm test          # 37 unit tests, no network required
npx apify run
npx apify push