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Fastenal Products Scraper

A category page in,
a parts list out.

Point it at a Fastenal product page or a whole category - anchors, say - and get the catalogue back as rows: name, price, currency, availability, brand, and the identifiers a procurement system actually joins on, sku, mpn and gtin. Fifteen columns, one row per product. Two things to know before you start: Fastenal is one of the retailers that needs your own residential proxy, and the fields come from the page's own structured data - so read the note about blanks before you plan around any single column.

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500 free rows, one-time$0.002 per row after that15 columnsCSV · XLSX · JSON
How it works

Give it a URL,
get the catalogue.

One input and one control. A line can be a single product or an entire category, and a category comes back expanded into its products.

  1. STEP 1Sign in to the platform.
  2. STEP 2Open the Fastenal Products Scraper.
  3. STEP 3Paste Fastenal product URLs or category URLs, one per line - or upload a CSV, XLSX or TXT file.
  4. STEP 4Configure a residential proxy - Fastenal is one of the retailers that blocks datacentre IPs or renders products client-side.
  5. STEP 5Set a limit per query, or leave it empty to take everything, then click Get Data.
  6. STEP 6Download the result as CSV, XLSX or JSON.

The limit is per query, so five category URLs with a limit of two hundred is two hundred products from each - not two hundred altogether.

Why teams use it

The identifiers,
not just the price.

Built for catalogue matching

sku, mpn and gtin sit on the same row as price and availability. Those are the fields that let an export line up against your own item master or a competitor's list - matching on product names is guesswork, matching on a manufacturer part number is not.

A category is a valid input

You do not need a list of product URLs to start. Give it the category you care about and it comes back expanded, one row per product - which is how the tool's own example for Fastenal is written, pointing at a whole section rather than a single item.

This retailer needs your own proxy

Most services on this pipeline go out through our shared pool, never your real address. Fastenal is one of the handful the tool names as an exception - it blocks datacentre addresses or builds its product pages in the browser - so this one runs through a residential proxy you supply. Residential is enough; it does not have to be a US one.

What you get

Fifteen columns,
one row per product.

Each row is one item: what it is called, what it costs and in what currency, whether it is available, who makes it, the identifiers that pin it down, and how it is presented.

The column list below is the header row of real run exports - forty-five of them, all identical - corroborated by the column array the run screen renders. The names are as solid as they get. What comes after the table matters more than usual here, though, because this service is one of several sharing a single pipeline and we have never pointed that pipeline at Fastenal ourselves. The note says exactly what that does and does not let us tell you.

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Data dictionary

Fifteen columns,
and an honest ceiling.

The names are exact and doubly confirmed. The descriptions say what each column is for - not what it will contain, because that is the retailer's decision rather than ours, and the note below explains why.

query
The URL you submitted for this row - the product or category page it came from. Written by the scraper, so it is there on every row.
service
Which retailer this row came from. This scraper shares one pipeline with several other product services, and the one you picked is stamped onto every row - useful if you keep more than one retailer in the same table.
name
The product name as the page declares it.
price
The price the page declares.
currency
The currency that price is in, as a separate column - so you are not parsing a symbol out of the price field.
availability
The stock status the page declares.
brand
The brand the page declares.
sku
The retailer's own stock code for the item.
mpn
The manufacturer part number. Along with gtin, this is what makes an export joinable to a catalogue that is not Fastenal's.
gtin
The global trade item number - the barcode identifier.
rating
The product rating, if the page publishes one. No scale is stated here; we have not seen a value.
reviews
The review count. A number of reviews, not the review text - there is no review body in this export.
image
The product image the page declares.
url
The product link.
description
The product description the page declares.

Two things about the evidence behind this page, both worth your time. First, the mechanism: products are read from the JSON-LD that a page publishes about itself - the structured data retailers embed for search engines. That sets a ceiling this page will not pretend past. Every column after service exists only if Fastenal declares that field, so a blank means not published, not run failed. Second, the gap: we have never run this against Fastenal. We hold forty-five exports from the shared pipeline behind this service and not one is a Fastenal run - forty-four came back empty and the only populated one is a different retailer. That export is worth one honest data point about the tool rather than about Fastenal: query and service filled on every row, because the scraper writes them itself - and six of the fifteen were empty on every single row. Expect gaps here too, expect them to fall differently because a different retailer publishes different structured data, and let your first free-tier run tell you which. That is also why this page prints no example values and no formats: with no Fastenal run, we would be inventing them.

Run one category free
Run controls

One dial,
and a list of links.

A line is a product page or a whole category. Beyond that there is a single number to set and one way to hand over a longer list.

Product URL Category URL Search URL One per line Limit per query Empty or 0 takes all CSV upload XLSX upload TXT upload Residential proxy
Where the fields come from

The page declares it,
we report it.

This scraper reads the structured data a product page publishes about itself - the JSON-LD block retailers embed so search engines can show a price and a stock status. That is why the columns read like they do: sku, mpn, gtin, brand, availability and currency are the standard vocabulary for describing a product, not names we invented.

The practical consequence is the one worth planning around. A column is only as good as the retailer's own markup. If a field is missing from the page's structured data it comes back blank, and no amount of re-running changes that - it is not a failure, it is an absence. In the one populated export we hold from this shared tool, six of the fifteen columns were blank on every row.

If you need something the markup does not carry, that is a different job - the Universal AI Scraper reads the page itself and returns the attributes you name, rather than only what was declared for search engines.

livescraper.app · the ceiling
Read from the page's JSON-LD✓
query and service: written by us✓
The other thirteen: declared by the page✓
Blank = not published, not failed!
Check the gaps on your first run✓
Structured data is a retailer's choice, and it differs between them.
Handing over the list

Uploads add,
and don't de-duplicate.

An uploaded file does not clear the box - the lines are appended to whatever you had already typed, and duplicates are not removed. Upload the same file twice and those URLs really will be fetched twice, and billed twice.

For a CSV or TXT the whole line is taken as one URL, so a spreadsheet with extra columns is not what you want here. Only an XLSX is read column by column, and there it takes column A.

The picker lists Parquet alongside CSV, XLSX and TXT, but it cannot actually read one in the browser and will tell you so - use one of the other three.

livescraper.app · the upload
CSV · XLSX · TXT✓
Duplicates are NOT removed!
CSV and TXT: the whole line✓
XLSX: column A✓
Appended to what you typed✓
Check the box before you run - a doubled list is a doubled bill.
Common workflows

Three jobs people
run here most.

A few of the ways teams turn an industrial catalogue into something a spreadsheet can answer.

Procurement

Price your own parts list

Export the categories you buy from and join the result to your item master on mpn or gtin rather than on names. What you get is a line-by-line comparison against what you are actually paying, which is the version of that question a finance team will accept.

Procurement · Finance
Catalogue

Fill the gaps in your own data

If your product records are missing manufacturer numbers or barcodes, a category export is a source for them. Brand, sku, mpn and gtin arrive together on one row, which is usually enough to reconcile an item you already stock.

Data · Merchandising
Availability

Watch a category over time

Run the same category on a schedule and keep the exports. Price and availability moving across a whole section tells you more about a supplier than any single item does - and the query column keeps each run traceable to the page it came from.

Ops
Get started
Pricing

Pay for the products
you actually pull.

No subscription, no minimum, no recurring invoice. Your first 500 rows are on us - after that, pay as you go at the same flat rate as every other scraper here.

Free tier

500 rows free - $0

On every new account, one time. No credit card. Per-query limits, file upload and every export format included. The residential proxy this retailer needs is yours to supply.

$0 forever
Pay as you go

$0.002 per row, after the free tier

The same flat rate as every other scraper on the platform, and a row here is one product. The estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.

Most popular
Volume

Custom · high volume

Volume pricing, dedicated workers and an SLA for ongoing monitoring or very large catalogue pulls. Tell us your numbers and we will quote it.

Talk to us
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10% off your first paid run.Use code LIVESCRAPER10 at checkout.
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Pairs well with

When the markup
isn't enough.

One of these fills the fields a page never declared; the other tells you what the same part costs elsewhere.

Universal AI Scraper

This page is limited to what a product page declares in its structured data. That one reads the page itself and returns the attributes you name - the answer when the field you need was never marked up.

Explore

Amazon Products Scraper

Plenty of maintenance and repair stock is listed on both. With a manufacturer part number on each row, comparing the two catalogues is a join rather than an afternoon.

Explore
The legal bit

Is it legal to scrape
product catalogues?

Short answer: yes for public catalogue data - and this is about as low-risk as scraping gets on the privacy side, because there are no people in it.

Everything in this export is shown on a public product page to anyone who opens it: the name, the price, the currency, the availability, the brand, the identifiers, an image and a description. More than that, most of it is published in structured form precisely so that machines will read it - that is what JSON-LD is for. Collecting it for price research and catalogue work is long-settled practice, and nothing here touches a login, an account price or a checkout.

There is no personal data in these fifteen columns at all - no names, no reviewers, no contact details. That makes the usual privacy analysis short. What replaces it is commercial: prices and catalogue structure are a supplier's business information, and using them to inform your own buying is ordinary, while republishing a competitor's catalogue as your own is a different matter that no export makes acceptable.

Fastenal's terms restrict automated access, and it is one of the retailers that enforces this at the network level - which is why a residential proxy is a requirement here rather than a suggestion. That makes this a contractual question as well as a technical one. We run no third-party trackers on the data layer, and your exports self-delete after 30 days.

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livescraper.app · principles
Public catalogue pages only✓
No logins, no account pricing, no checkout✓
No personal data in any column✓
Prices are a supplier's business information!
Exports self-delete (30 days)✓
Check Fastenal's own terms before you scale up.
FAQ

What people ask
before signing up.

The questions we get most. Anything else? Talk to us - humans write the answers, not a bot.

How do I scrape Fastenal product data?+
Using the Fastenal Products Scraper:
  1. Sign in to the platform.
  2. Open the Fastenal Products Scraper.
  3. Paste Fastenal product URLs or category URLs, one per line - or upload a CSV, XLSX or TXT file.
  4. Configure a residential proxy - Fastenal is one of the retailers that blocks datacentre IPs or renders products client-side.
  5. Set a limit per query, or leave it empty to take everything, then click Get Data.
  6. Download the result as CSV, XLSX or JSON.
Can I point it at a whole category, or only single products?+
Either. A line can be one product URL or a category or search URL, and a category returns one row per product in it. The example the tool itself puts in the box for Fastenal is a category - the anchors section - so working a category at a time is the expected way to use it.
Where does the data actually come from?+
From the JSON-LD that the page publishes about itself - the structured data retailers embed for search engines. That is worth knowing because it sets the ceiling: this scraper reports what Fastenal declares, so a column comes back blank when the page never declared that field, not because the run failed.
Will all fifteen columns be filled?+
Almost certainly not, and we will not pretend otherwise. Because the fields are read from each page's structured data, which fields are present is the retailer's choice. In the one populated export we hold from this shared tool - a different retailer, not Fastenal - six of the fifteen were empty on every row. Expect gaps, and let your first free run tell you which ones apply here.
Have you run this against Fastenal yourselves?+
No, and that is the honest answer. We hold forty-five run exports from the shared product scraper behind this service and not one of them is a Fastenal run. So the fifteen column names are solid - two independent sources agree on them - and everything about what Fastenal specifically returns is something your first free-tier run will establish before we would.
Do I need a proxy for this one?+
Yes. Requests normally go out through our shared proxy pool rather than your real address, but Fastenal is one of the retailers the tool singles out as blocking datacentre IPs or rendering products in the browser, so this service needs your own residential proxy. Residential is enough - it does not have to be a US one.
What is the service column for?+
It records which retailer a row came from. This scraper is one of several services sharing a single product pipeline, and the service you picked is sent with the job and written onto every row - so if you keep exports from more than one retailer in the same table, that column is what keeps them apart.
How much does it cost?+
The first 500 rows are free and one-time, with no credit card. After that it is $0.002 per row - the same flat rate as every other scraper on the platform. A row is one product. The residential proxy is a separate cost, and it is yours.

Your first 500 products,
on the house.

500 free rows, one time, on every new account - no expiry. After that it is $0.002 per row, pay as you go, with no card on file until you decide. The residential proxy is yours to bring.

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Scrape Fastenal product data at scale

Livescraper's Fastenal Products Scraper turns industrial catalogue pages into a spreadsheet. You paste Fastenal product URLs or whole category URLs - one per line, or uploaded as a CSV, XLSX or TXT file - set how many products to take per query, and download the result as a clean CSV, Excel or JSON file, one row per product.

Each row carries the name, the price and its currency as separate columns, availability, brand, and the three identifiers that make catalogue work possible: sku, mpn and gtin. Those last two are the reason this export is useful rather than merely interesting - a manufacturer part number joins to your item master, where a product name only approximately does. Rating, review count, image, link and description round out the fifteen.

Procurement teams export the categories they buy from and join the result to what they are already paying, line by line. Data and merchandising teams use it to fill in manufacturer numbers and barcodes their own records are missing. Operations teams run the same category on a schedule and watch price and availability move across a whole section rather than one item.

Two limits are stated here rather than buried, because both change how you should plan. Fastenal is one of the retailers this pipeline singles out as blocking datacentre addresses or building its product pages in the browser, so this service needs your own residential proxy - residential is enough, it need not be a US one. And the fields are read from the structured data each page publishes about itself, which means a blank column is a field the retailer never declared rather than a run that failed; in the one populated export we hold from this shared tool, from a different retailer, six of the fifteen columns were empty throughout. We have not run this against Fastenal ourselves, so the fifteen column names are what we stand behind and the rest is what your first free-tier run will show you. Start free: your first 500 rows cost nothing and need no card.