One Row Per Product
Give it a search term, a category page, or a list of ASINs and product links mixed together. They all come back on the same columns.
The product data you need is already on Amazon's pages. AllyHub gets it off them and into something you can actually work with.
In order: what a row is, what it holds, how far it splits, what never makes it in, where it works, and where it goes next.
Give it a search term, a category page, or a list of ASINs and product links mixed together. They all come back on the same columns.
Hand it ASINs or product URLs and every record carries the product's Best Sellers Rank. Run a keyword search or a category page instead and that figure is not on the page to take.
Size, color, and pack options each get their own row with their own price, availability, and image — not the parent listing's defaults standing in for all of them.
Name your conditions with the scope — under $50, four stars and up, Prime only — and AllyHub skips everything else as it goes. Nothing arrives that you would have deleted.
amazon.com, .co.uk, .de, .co.jp and the rest take the same instruction and return the same shape. Nothing to reconfigure when you switch markets.
Download JSON or CSV, or read the results straight from the API. No Amazon API credentials to request, no developer approval to wait on.
From a search term, a category URL, or an ASIN list to a structured Amazon product dataset.
Describe the scope in plain language — a keyword, a category page, a pile of ASINs. No input schema to fill in, no template to build first.
It captures title, ASIN, price and list price, rating, review count, brand, availability, images, features, seller name and rating, fulfillment, and delivery — paginating as it goes.
Sort it, pivot it, or drop it into your warehouse. Save the run as a Playbook and it repeats on whatever schedule you set.
The alternatives all work one way: give the tool what it wants, take what it gives back.
Before the competing tools return a single row, you are filling in a JSON input schema, wiring a spreadsheet formula, or building a point-and-click template. The tool becomes the project. AllyHub takes the request the way you would say it out loud.

Most of these tools eat exactly one shape of input — this one only keywords, that one only URLs. Cover a search and a competitor's ASIN list and you have run two tools, then spent the afternoon aligning two different output formats by hand.

The lightweight extensions hand back a title, a price, a link, and an image. That reads like enough until you try to filter by rating, check who is actually selling it, or find out whether it is even in stock.

Priced per thousand results, run one and run one hundred cost the same. AllyHub keeps the layout it worked out on the first pass, so later runs start at the data instead of the structure — it builds on what it already knows, and every token produces more value.

Sellers, researchers, merchandisers, and data teams who need Amazon product records rather than an opinion about them.
You need to know what your category actually charges and how many reviews it takes to be seen, and checking listings by hand runs out of patience long before it runs out of products. Pull the result set into sortable columns and the entry bar stops being a guess — it becomes a number you can point at in a launch review.
The finding is solid but the evidence is a folder of screenshots nobody can cite. Run the same scope on a fixed cadence and the analysis rests on a dataset with a date on it — something a client or a review board can check for themselves.
The competitive spec sheet was accurate the week someone built it by hand. Re-run the same pull and the comparison refreshes itself, so the roadmap argument quotes this quarter's catalog instead of last year's.
The in-house scraper breaks whenever Amazon touches a template, and it always breaks on a Friday. Point AllyHub at the same scope and the columns arrive the same way each run — no selectors to patch when the markup moves.
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Quick answers on collecting Amazon product data and what you can do with it.
An Amazon product scraper reads listing pages and returns what is printed on them — titles, prices, ratings, review counts, availability, seller details — as structured records rather than a browser window. AllyHub takes the scope you describe, extracts a record per product, and hands back a dataset you can sort, pivot, or import.
Yes. Individual lookups and small pulls cost nothing on the free plan. Once you are extracting at volume, running on a schedule, or piping results into a warehouse or BI tool, that sits on a paid plan.
Public Amazon product pages are generally treated as publicly visible information, and AllyHub only reads what a shopper's browser can already see. That said, Amazon's terms of service and robots directives still apply to you, and rules differ by jurisdiction and by what you do with the data afterward. This is not legal advice; check with your own counsel before building a commercial process on it.
When you hand AllyHub ASINs or product URLs, yes. BSR is printed on the product page itself, so it lands in every record. Keyword searches and category listings do not show it anywhere, and those runs come back without the column. Send ASINs directly when rank drives the analysis.
Yes — the conditions are part of the scope, not a cleanup pass, so a filtered run never collects the rows it would only throw away. They do have to be conditions the listing itself shows; anything narrower you filter after the pull.
The other tools hand back a dump and forget the page the moment the run ends. AllyHub keeps the run itself, not just its output — point a saved one at a different scope and there is nothing to set up a second time. It delivers better results over time.