Top 5 Home Depot Scrapers Benchmarked & Compared

We benchmarked five web data providers on Home Depot, each fetching the same 50 product and search pages at 5 concurrent requests, for a total of 250 requests.

Home Depot scraping benchmark

You can read more about our benchmark methodology.

• Explore our full e-commerce scraping benchmark covering the top 100 e-commerce domains.

What data you can scrape from Home Depot

Bright Data offers a dedicated scraper API for Home Depot, while Apify provides a general e-commerce actor. Because both return structured JSON, they were the only two providers whose output could be compared field by field, so we based the comparison on these two and listed the fields that are unique to each. Basic fields such as brand, description, image, and URL are scraped by both and were left out from the table.

Bright Data returned 69 data fields per product, while Apify returned 6.

Home Depot scrapers & benchmark results

Bright Data topped our Home Depot benchmark, returning valid data on 94% of requests, ahead of every other provider we tested, with an average end-to-end completion time of about 29 seconds.

Bright Data offers five Home Depot scraper APIs:

• Home Depot, collect by URL: point it at an individual product page and it returns structured product data, including title, price, availability, specifications, ratings, images, and seller details.
• Home Depot, discover by category URL: collects product records by crawling a given category page instead of a single product.
• Home Depot, discover by keyword: gathers products from Home Depot search results for a specified term.
• Home Depot, discover by UPC: looks up products by their UPC code.
• Home Depot, discover by URL: surfaces products from a supplied URL.

Two ready-made datasets for Home Depot also sit in Bright Data’s Marketplace:

• Home Depot US dataset: a pre-collected set of listings with product details, pricing, and availability, commonly used for competitive analysis, inventory tracking, and spotting trends in the home-improvement market.
• Home Depot Appliance Repair Parts dataset: product information, pricing intelligence, and customer sentiment centered on appliance replacement parts.

Product pages went through Bright Data dedicated Home Depot scraper API.

Search pages were not routed to a dedicated scraper; they were fetched through Bright Data’s Web Unlocker, which returns the fully rendered HTML.

Oxylabs routes everything through one Web Scraper API endpoint (realtime.oxylabs.io/v1/queries). For Home Depot it leaned on a single source type, the universal scraper, which takes any product or search URL and can render the page in a headless browser (render: html).

There was no Home Depot-specific source here; the same universal source handled both product and listing pages. Oxylabs came second in our test, clearing 90% of Home Depot requests with an average completion time near 42 seconds on all requests.

Decodo handles Home Depot with its Site Unblocker, a proxy endpoint (unblock.decodo.com) that fetches any product or search URL straight through the proxy and returns the page.

JavaScript rendering: the unblocker can run the page’s JavaScript and return the fully rendered result, toggled with the X-Smartproxy-Js-Render header.

There is no Home Depot-specific scraper but Decodo also offers a Universal Web Scraping API, a target-based endpoint that can extract data from any general webpage without a custom-built parser or a site-specific template

Zyte uses one endpoint for everything, the Zyte API (api.zyte.com/v1/extract), called the same way for every Home Depot page. Each request carried the target URL, the extraction type, and browserHtml: true so the page was rendered in a headless browser before extraction.

The two page types were handled with two extraction modes on that same API:

• Product pages asked for product extraction (extractFrom: browserHtml, ai: true).
• Search/listing pages asked for productList extraction (extractFrom: browserHtml, ai: true).

Zyte returned valid data on 36% of Home Depot requests, with an average end-to-end completion time of about 84 seconds.

Nimble was reached through its Nimble Web API (api.webit.live/api/v1/realtime/web), one call for every page. Each request passed the URL along with render: true (headless rendering), parse: true (structured parsing), format: json, a locale, and a country of US, since Home Depot is a US-only site.

Product and search pages used the exact same payload, differing only in the URL; Nimble was given no separate mode for product versus search.

Nimble did not return usable data on any Home Depot request in our benchmark.

Apify ran on a single actor, the E-commerce Scraping Tool (apify~e-commerce-scraping-tool), called synchronously via /v2/acts/apify~e-commerce-scraping-tool/run-sync-get-dataset-items. The same actor covered every Home Depot page.

Product URLs were passed in detailsUrls and search URLs in listingUrls, with scrapeMode: AUTO and a countryCode of us. Apify succeeded on 28% of Home Depot requests, with an average end-to-end completion time of roughly 37 seconds on successful requests.

How Home Depot scraping methods differ

Scraping Home Depot generally comes down to three approaches, and the main difference between them is how much of the work the provider does versus how much you do:

• Dedicated scraper APIs (structured datasets). You send a product or search URL (or a category, keyword, or UPC) and receive ready-made structured fields such as price, specifications, ratings, and availability. The provider handles rendering, anti-bot handling, and parsing, so the output is clean and consistent. The trade-off is flexibility: you get the fields the provider chose to extract, and little control over how the page is read.
• Universal scraping APIs with rendering. You pass any URL to a single general-purpose endpoint that loads and renders the page, then returns parsed data. This covers a wider range of pages than a site-specific scraper and still spares you the anti-bot and rendering work, but the parsing is more generic, so the field set is usually shallower than a dedicated scraper’s.
• Proxies and web unblockers. These fetch the page through a proxy layer that bypasses bot protection and can execute JavaScript, then return the raw rendered HTML. You get maximum flexibility over what to extract, but you also take on all of the parsing and long-term maintenance yourself.

One difference shows up clearly on search and listing pages. A dedicated scraper will try to collect every result across the full search unless you set a maximum-results (max items) parameter, which can quietly burn through a lot of credits on broad queries. A method that returns HTML has no such risk: it captures whatever is on the first results page and stops there, so the scope is naturally limited to what a single page load returns.

You can also build your own scraper, and there are several different methods for doing so; see our guide to AI web scrapers for more details.

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Home Depot scraping benchmark methodology

Each provider received the same fresh set of Home Depot URLs, split evenly between product and search pages. Where a provider offered a dedicated Home Depot scraper we used it; otherwise we fell back to its universal source scraper or web unblocker. All providers ran from a single server location, so no one gained a geographic edge.

A 200 status on its own was not treated as a win. We accepted HTTP responses in the 200-399 range, plus 404, as valid responses. When a request came back with HTTP 200, we then ran a content check against ground truth defined in advance: on a structured (JSON) response we verified that the target field was valid, and on an HTML response we verified that the expected CSS selector was present. A request counted as a success only when that content check passed. A block page, a captcha, or an empty shell scored zero even with a 200 status.

End-to-end response time is reported over successful requests.

Cite this research

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{ipi2026,
  author = {Şipi, Nazlı},
  title  = {{Top 5 Home Depot Scrapers Benchmarked & Compared}},
  year   = {2026},
  month  = jul,
  howpublished    = {\url{https://aimultiple.com/home-depot-scraping}},
  note   = {AIMultiple. Retrieved July 16, 2026}
}

Nazlı Şipi

AI Researcher

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Nazlı is a data analyst at AIMultiple. She has prior experience in data analysis across various industries, where she worked on transforming complex datasets into actionable insights.

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