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We benchmarked 4 web scraping providers on etsy.com. The results were filtered from our e-commerce scraping benchmark covering 100 e-commerce domains. A total of 200 requests were sent to Etsy at concurrency 5.

Etsy scraping benchmark

You can read more about our Etsy scraping benchmark methodology.

Performance by page type

What data you can scrape from Etsy

You can scrape either product or search pages and get full product details (title, price, description, images, seller, variations, reviews, shipping/return terms, and more). Only Bright Data and Apify returned structured JSON fields directly; for the other providers we parsed the fields ourselves from the raw HTML.

Bright Data returned 59 JSON metadata fields per Etsy product, while Apify returned 8.

The table below lists the fields that are unique to each provider, grouped by category; shared basics such as asin, brand, title, price, image, url, and rating are left out.

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Etsy scraping providers

Bright Data was the top performer on Etsy succeeding on 88% of requests, the highest success rate of any provider tested, with an average  completion time of 112 seconds.

Bright Data has three scraper APIs available for Etsy:

  • Etsy, Collect products by URL (etsy.com): targets individual product listing pages and pulls structured product data, including title, price, seller and shop information, item details, product specifications, variations, shipping and return policies, ratings, and top reviews.
  • Etsy, Discover by keywords (etsy.com): gathers product records from Etsy search results for specified keywords rather than a single URL.
  • Etsy, Discover by shop URL (etsy.com): collects the full product catalog listed by a given Etsy shop.

Ready-made datasets for Etsy also live in Bright Data’s Dataset Marketplace:

  • Etsy US Products dataset: a pre-built collection of Etsy listings with product details, prices, ratings, and descriptions. Common use cases are pricing intelligence, competitor monitoring, and market research.

Bright Data handled Etsy’s two page types differently. Product pages were run through Bright Data’s Etsy, Collect products by URL scraper API, which returned structured JSON with 59 listing-level fields (title, price, seller, ratings, variations, item specifics, shipping and return terms). Search/listing pages were not routed through a dedicated Etsy scraper; instead they went through Bright Data’s Web Unlocker, which returned raw rendered HTML that we then parsed ourselves.

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Apify’s Etsy successs rate is 56% , taking around 57 seconds on average to finish a request. All URLs went through one Apify actor, the E-commerce Scraping Tool (apify~e-commerce-scraping-tool), invoked synchronously via /v2/acts/apify~e-commerce-scraping-tool/run-sync-get-dataset-items.

We fed the two Etsy page types into two separate input arrays on the same actor: product URLs went into detailsUrls, search/listing URLs into listingUrls. Every run used scrapeMode: AUTO and a countryCode set to us for all Etsy requests.

Product pages landed at 80% success , while search pages fell to 32% , which is what pulls the overall figure down to 56%.


On Etsy, Zyte landed in second place. 80% of requests succeeded, and the average completion came in around 40 seconds. That performance came from a single API surface, api.zyte.com/v1/extract, which we hit for every Etsy URL, always with browserHtml: true so the page was rendered in a headless browser before any data was pulled out.

What changed between the two page types was only the extraction type in the payload:

  • product extraction for listing detail pages (extractFrom: browserHtml, ai: true).
  • productList extraction for search results (extractFrom: browserHtml, ai: true).

Same endpoint, same auth, same rendering pipeline. The only variable was which schema Zyte’s IA was asked to fill in.

For Etsy, Nimble ran on its real-time endpoint api.webit.live/api/v1/realtime/web. Every request carried the Etsy URL, render: true, parse: true, format: json, and a locale/country pinned to US. Product and search pages were sent with identical payload shapes; the only thing that differed request to request was the URL itself, since Nimble has no page-type-specific extraction mode.

Nimble was the fastest provider on Etsy overall (16 seconds average) and cleared 96% of product pages, but it failed on every single search/listing request. That drags the combined success rate down to 48%.

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Etsy scraping benchmark methodology

We took the Etsy results from our larger e-commerce scraping benchmark, which tested 6 providers across 100 domains at 3 concurrency levels (5, 100, and 5000 parallel requests).

The full method is in the e-commerce scraping benchmark: how we picked and validated URLs, which scraper product we used for each provider, and how we decided whether a request counted as a success.

Perguntas frequentes

Yes for most reliable results. Etsy relies on client-side JavaScript for product variations, dynamic pricing, and listing search. Providers that render pages in a headless browser (Zyte, Nimble, Bright Data Web Unlocker) outperformed plain-HTTP unblockers in our benchmark.

Etsy’s /search endpoint uses stricter bot detection and dynamic rendering than individual listing pages. In our benchmark, Nimble failed on 100% of Etsy search requests, while other providers cleared the majority of product URLs.

Cite esta pesquisa

Escolha o formato adequado ao local onde você vai publicar. Colar a versão com link no seu CMS preserva o backlink.

Nazlı Şipi (2026) - "Etsy Scrapers: Benchmarked Top 4 APIs". Publicado on-line em AIMultiple.com. Acessado em 11 Agosto 2026, em: https://aimultiple.com/etsy-scraper [Recurso on-line]

Şipi, N. (2026, 11 Agosto). Etsy Scrapers: Benchmarked Top 4 APIs. AIMultiple. https://aimultiple.com/etsy-scraper

@misc{sipi2026,
  author = {Şipi, Nazlı},
  title  = {{Etsy Scrapers: Benchmarked Top 4 APIs}},
  year   = {2026},
  month  = aug,
  howpublished    = {\url{https://aimultiple.com/etsy-scraper}},
  note   = {AIMultiple. Acessado em 11 Agosto 2026}
}
Nazlı Şipi
Nazlı Şipi
Pesquisadora de IA
Nazlı é analista de dados na AIMultiple. Ela tem experiência anterior em análise de dados em vários setores, onde trabalhou na transformação de datasets complexos em insights acionáveis.
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