Give it a Product Hunt username, an @handle or a profile URL and get that person back as a row: display name, headline, avatar, how many follow them and how many they follow, the products, posts, collections, reviews and stacks on their profile, whether Product Hunt marks them as a maker and as verified, and the X handle they chose to link. One row per profile.
The input is the person. You name the accounts you want, in whatever form your list already holds them, and the job returns one row each.
One row per profile, tagged with the query it came from - so a run over a few hundred handles still reconciles against your input list.
A profile URL, an @handle and a bare username all resolve to the same profile. We ran all three against one account and every column came back identical except query, which echoes what you typed - so a spreadsheet nobody normalised works as-is.
Followers, following, products, posts submitted, collections, reviews and stacks arrive as separate fields. That is the difference between a shortlist you can rank and a set of pages somebody has to read.
Requests go out through rotating addresses rather than yours. From your end it is a form or an API call - you never assemble a pool, and a block is never something you have to notice.
Everything a public Product Hunt profile displays, flattened into a single row: who the person is, what they have shipped, how large their following is, and the two badges Product Hunt puts on the profile itself.
One caveat we would rather state than hide. This page names the columns and says what each one holds; it does not tell you how often any of them carries a value. That varies with the accounts you pull and with when you pull them, so any figure we printed would be describing somebody else’s list rather than yours. Take a small free-tier run over your own handles - that export is the honest answer for the accounts you actually care about.
The header row of real exports, in export order. Read the note underneath before you write code against the counts - they are not numbers.
Every value in this export is a string, including all seven counts and both flags. followers comes back as "0" and that is a real zero rather than a missing value, so parse before you sort or sum, and compare is_maker and is_verified against "Yes" rather than treating them as booleans. Three columns are newer than the rest: avatar_url, stacks_count and is_verified appeared on 2026-07-03, so an export taken before that date has fourteen columns, not seventeen - worth knowing if you are appending to an older file. And unlike some of our other scrapers, the JSON and the XLSX carry exactly the same set: there is no JSON-only extra field here.
Three ways to name a person and four ways to hand over the list. All three input forms resolve to the same profile, so nothing has to be normalised first.
A few examples of what a table of maker profiles is actually good for.
Take the makers behind a season of launches and rank them by products shipped, posts submitted and the maker flag rather than by who happened to trend. A list of builders, sorted, instead of a feed to scroll.
Pull the profiles attached to products in your category and read the counts side by side. Following, stacks and collections say a lot about who is embedded in a space and who has just arrived in it.
If your product lives on Product Hunt, the people reviewing and stacking it are your most engaged users. Exporting their profiles turns a list of handles into something you can segment and thank.
No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.
Every new account, one-time. No credit card required. Per-query limits, file upload and every export format included.
Roughly $2 per 1,000 profiles. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for continuous tracking or very large one-off pulls. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.A Product Hunt profile names a person and what they have shipped. These two carry on from there.
The professional side of the same shortlist, up to 1,000 profiles a run. Personal data with a stricter set of permitted uses - read them before you start.
Product Hunt publishes no addresses, so reaching a maker means finding their site first. Point this at the domains once you have them and it returns emails, phones and socials.
Short answer: yes for the public profile - but unlike most of our exports this one describes identifiable people, and that changes what you may do with it.
Everything collected here is shown on a public profile to any visitor, signed in or not: the display name, the handle, the headline, the avatar, the counts, and the X handle the person chose to link. Nothing sits behind a login, and nothing is inferred - if a field is in the row, the profile owner published it.
That does not make the output neutral, and we would rather say so than let you discover it later. A row here identifies a person, so the GDPR and comparable regimes apply to whatever you do next: you need a lawful basis, the purpose has to be one the person would reasonably expect, and you should not keep the data longer than that purpose needs. Aggregate research, category mapping and building a shortlist to approach are ordinary uses. Monitoring an individual's activity over time, or feeding these rows into an automated decision about someone, is not - and the same standard we set out on the LinkedIn profiles page applies here.
Product Hunt's terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the site, check it. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's Product Hunt Profiles Scraper turns a list of handles into profile data. You submit Product Hunt usernames, @handles or profile URLs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - set a limit per query if you want one, and download the result as a clean CSV, Excel or JSON file. One row per profile.
Each row carries what the public profile displays: the username and display name, the headline under it, the profile and avatar URLs, and seven separate counts - followers, following, products, posts submitted, collections, reviews and stacks. Two more columns carry the badges Product Hunt puts on the profile itself, marking whether the account is a maker and whether it is verified, and a final pair carries the linked X handle and Product Hunt's own numeric user id.
Scouts and investors use it to rank the people behind a season of launches by what they have actually shipped rather than by what trended. Competitive teams pull the profiles attached to products in their category and read the counts side by side. Community and growth teams export the people reviewing and stacking their own product, because those are the most engaged users they have and a list of handles is not something you can segment.
Two things are worth knowing before you start. Every value comes back as a string, including the counts and the two flags, so parse before you sort. And this export describes identifiable people, which makes it personal data under the GDPR and comparable regimes - aggregate research and building a shortlist to approach are ordinary uses; monitoring an individual over time is not. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.