ProductHunt Launches & Makers Scraper

Product Hunt Scraper — Launches, Makers, Votes, Ratings & Reviews

Scrape Product Hunt at scale and turn every launch into clean, structured startup and B2B intelligence data. This Product Hunt scraper extracts the complete public profile of any product — name, tagline, external website, high-resolution logo, screenshot gallery, upvote count, comment count, aggregate 1–5 rating, topics, the makers and founders behind the launch, and real user reviews — from a product handle, a topic/category, a daily leaderboard archive date, or any Product Hunt URL. No login, no coding, and no API key required.

Export to JSON, CSV, Excel, or API, or use the built-in flattened Makers and Reviews dataset views for instant CRM and spreadsheet imports.

Built for startup analysts, VC and angel deal-flow scouts, B2B lead generation and growth teams, technical recruiters sourcing top makers, and media/newsletter operators who need a fresh, automated feed of Product Hunt data without touching the website by hand.

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⚡ At a glance

Feature	Details
Target platform	Product Hunt (producthunt.com)
Search & discovery modes	Topics / categories (topicSlugs), Product handles (productSlugs), Daily leaderboard dates (leaderboardDates), or custom Start URLs (startUrls)
Core data extracted	Product name, slug, tagline, external website URL, logo, screenshots, upvotes, comments, 1–5 star rating, topics, launch dates, makers/founders, user reviews
Built-in dataset views	Overview (1 row per launch), Makers (unwound 1 row per maker/founder), Reviews (unwound 1 row per user review)
Pagination & scale	Automatic topic & daily leaderboard archive expansion — supports 1,000+ unique launches in a single run with 0 duplicates
Authentication	None required — no Product Hunt account, OAuth token, or API key needed
Proxy support	Apify Proxy (Datacenter enabled by default; optional US Residential for very large runs)
Export formats	JSON, CSV, Excel (XLSX), HTML, XML, RSS, and REST API

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🔎 Why scrape Product Hunt?

Product Hunt is where new software, AI tools, developer platforms, and SaaS startups launch first. Every day, hundreds of makers ship products, collect upvotes, engage in comment threads, and earn verified user reviews — making it one of the highest-signal public directories of what is being built and what is gaining real market traction. However, that signal is locked inside an infinite-scroll web app with no bulk export, and official API access requires OAuth registration and strict rate limits. Copy-pasting launches, founder profiles, website domains, and vote counts one page at a time does not scale.

This Product Hunt data extractor automates the entire workflow. Point it at one or more topics, a list of product handles, a set of URLs, or historical leaderboard dates, and it returns one normalized, de-duplicated record per product. Whether you need a quick snapshot of 50 AI tools or 1,000+ results pagination across software categories and daily leaderboards, you get analysis-ready Product Hunt lead generation and market intelligence data in minutes.

Common reasons teams scrape Product Hunt:

• Track daily, weekly, and historical launches across categories to spot breakout SaaS and AI products early.
• Build a B2B startup & product database complete with taglines, company websites, categories, and launch dates.
• Source founders and makers for lead generation — extract maker names, usernames, avatars, and profile URLs for outbound sales, partnerships, or recruiting.
• Monitor competitor traction — track upvotes, comment velocity, aggregate rating scores, and review counts over time.
• Mine user reviews for voice-of-customer research — analyze star ratings and full review bodies to uncover feature gaps and buyer sentiment.
• Power newsletters, directories, and trend reports with an automated daily or weekly feed of top launches and visual assets.
• Feed AI agents, RAG pipelines, and BI dashboards with clean, structured startup launch records.

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✨ What this Product Hunt scraper does

• ✅ Four flexible ways to target launches — pick from 20+ popular topics (artificial-intelligence, productivity, developer-tools, saas, fintech, etc.), list specific product slugs/handles, pass daily leaderboard dates (YYYY/M/D), or paste any Product Hunt URLs. Combine multiple modes in a single run.
• ✅ Unlocked 1,000+ deep pagination & archive expansion — when you request a large maxRecords target (e.g., 500 or 1,000+), the actor automatically harvests products from your chosen inputs and seamlessly expands across topics and daily leaderboard archives until your target is reached. Every product is de-duplicated by slug so you never pay for duplicates.
• ✅ Complete product & launch metadata — captures identity (name, slug, productUrl, websiteUrl, tagline), rich media (image logo + screenshots array), engagement (votesCount, commentsCount, aggregateRating), taxonomy (topics, applicationCategory, operatingSystem), and timestamps (datePublished, dateModified).
• ✅ Founder & maker extraction — collects every maker credited on the product (name, username, profile, image), giving you a ready-made founder lead list.
• ✅ Optional user reviews — extracts individual user reviews (rating, body, author, authorProfile, authorImage, datePublished). Toggle scrapeReviews: false whenever you want faster, lighter runs focused purely on launch and maker metadata.
• ✅ Zero setup, no API key — works out of the box with no login or developer token.
• ✅ Bulk export JSON, CSV, Excel & API — includes three tailored dataset views (Overview, Makers, Reviews) so you can download flat maker or review spreadsheets with one click.

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🎯 Use cases

Persona / Team	How they use Product Hunt data
VC & Angel Scouts	Monitor new launches in AI, Developer Tools, Fintech, and SaaS; rank by votesCount and aggregateRating to spot high-momentum startups before Series A.
B2B Sales & Lead Generation	Extract websiteUrl and makers[] from newly launched SaaS tools to reach founders right as they go to market and need infrastructure, marketing, or compliance tools.
Product & Competitive Intelligence	Track competitor product pages and reviews[] to benchmark rating trajectories, feature complaints, and positioning taglines.
Technical Recruiters & Talent Sourcers	Use the unwound Makers view to discover prolific builders, indie hackers, and founding engineers behind top-voted launches.
Directory & Newsletter Operators	Schedule daily or weekly runs to curate "Top AI & Productivity Launches" digests complete with logos, taglines, websites, and upvote counts.
Market & Academic Researchers	Analyze category saturation, multi-topic tagging, launch seasonality, and user rating distributions across thousands of software products.

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📥 Input

All input fields are optional. Leave everything empty (or use the prefilled defaults) and the scraper collects a fast sample of launches so you can inspect the schema immediately.

Field	Type	Default / Prefill	Description
topicSlugs	array (select)	Prefill: ["artificial-intelligence", "productivity"]	Pick one or more Product Hunt topics (categories) to discover launches from — e.g. artificial-intelligence, productivity, developer-tools, saas, marketing, design-tools, no-code, fintech, chrome-extensions, crypto.
productSlugs	array (strings)	Prefill: ["notion", "linear", "vercel"]	Optional. Look up specific products by their Product Hunt handle — the last segment of /products/<slug>.
scrapeReviews	boolean	Default: true (Prefill: false)	Include user reviews (rating, body, author, datePublished) on each product record. Turn off for faster runs.
maxRecords	integer	Default: 100 (Prefill: 30)	Maximum number of products to collect. Set 0 for unlimited, or set 1000+ to trigger automatic multi-topic and daily leaderboard archive expansion.
startUrls	array	[]	Advanced. Paste any Product Hunt URLs — product pages (/products/...), topic pages (/topics/...), or daily leaderboards (/leaderboard/daily/...).
leaderboardDates	array (strings)	[]	Advanced. Pull top products launched on specific days in YYYY/M/D format with no leading zeros (e.g. 2025/5/15, 2025/5/14).
requestDelay	integer	800	Advanced. Pause between page requests in milliseconds.
maxConcurrency	integer	4	Advanced. Number of product pages fetched in parallel.
proxyConfiguration	object	{ "useApifyProxy": true }	Apify Proxy configuration. Datacenter proxy is used by default; switch to US Residential if needed on massive runs.

Example input — fast topic & product lookup

{
  "topicSlugs": ["artificial-intelligence", "productivity"],
  "productSlugs": ["notion", "linear", "vercel"],
  "scrapeReviews": false,
  "maxRecords": 50,
  "proxyConfiguration": { "useApifyProxy": true }
}

Example input — 1,000+ launches deep bulk extraction

{
  "topicSlugs": [
    "artificial-intelligence",
    "developer-tools",
    "saas",
    "productivity",
    "marketing",
    "fintech"
  ],
  "scrapeReviews": true,
  "maxRecords": 1000,
  "maxConcurrency": 5,
  "requestDelay": 500,
  "proxyConfiguration": { "useApifyProxy": true }
}

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📤 Output

Each scraped product is saved as a structured JSON object in the Apify Dataset. Here is a real record from a verified run:

{
  "slug": "notion",
  "productUrl": "https://www.producthunt.com/products/notion",
  "name": "Notion",
  "tagline": "all-in-one workspace that combines note-taking, project management, and task organization. It allows users to create customized databases, documents, and calendars…",
  "websiteUrl": "https://www.notion.so",
  "image": "https://ph-files.imgix.net/ff3e2acf-884a-4f4c-a383-6edfe3de0d88.png?auto=format",
  "screenshots": [
    "https://ph-files.imgix.net/6a8f48a7-77f9-406e-9b73-79108150f9e1.png?auto=format&fit=crop",
    "https://ph-files.imgix.net/a9e7425a-7510-46a8-a663-d3b858590075.png?auto=format&fit=crop"
  ],
  "applicationCategory": "Productivity",
  "operatingSystem": "Web",
  "datePublished": "2020-02-20T18:53:57.194-08:00",
  "dateModified": "2026-11-01T11:01:28.006-08:00",
  "aggregateRating": {
    "ratingValue": 4.83,
    "ratingCount": 1416,
    "bestRating": 5,
    "worstRating": 1
  },
  "votesCount": 288,
  "commentsCount": 96,
  "topics": [
    { "slug": "productivity", "name": "Productivity" },
    { "slug": "artificial-intelligence", "name": "Artificial Intelligence" }
  ],
  "makers": [
    {
      "name": "Simon Last",
      "image": "https://ph-avatars.imgix.net/731624/original.jpeg?auto=format&crop=faces&fit=crop&h=100&w=100",
      "profile": "https://www.producthunt.com/@simon_last"
    }
  ],
  "reviews": [
    {
      "rating": 4,
      "body": "I've evaluated dedicated project management software like Jira or Asana, alongside text-heavy document storage tools…",
      "author": "Ulykbek Asylbek",
      "authorProfile": "https://www.producthunt.com/@ulykbek11",
      "authorImage": "https://ph-avatars.imgix.net/9426488/54e00b10-6d23-4bd0-b703-517d79aa0b2e.jpeg?auto=format&crop=faces&fit=crop&h=100&w=100",
      "datePublished": "2026-07-10T07:47:11.986-07:00"
    }
  ],
  "scrapedAt": "2026-11-01T23:37:03.766Z"
}

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📋 Output fields & coverage reference

🏷️ Product Identity & Links

Field	Type	Coverage	Description
slug	string	🟢 High (100%)	Unique Product Hunt handle (last segment of the product URL, used for de-duplication).
name	string	🟢 High (100%)	Official product / launch title.
tagline	string	🟢 High (100%)	Short product value proposition / tagline (~200 chars).
productUrl	string	🟢 High (100%)	Canonical Product Hunt page URL (https://www.producthunt.com/products/<slug>).
websiteUrl	string	🟢 High (99%+)	External company or product website URL — ideal for domain enrichment and B2B lead gen.
image	string	🟢 High (100%)	Product logo image URL on the CDN.
screenshots	array<string>	🟢 High (98%+)	High-resolution product gallery screenshot URLs.

📈 Engagement, Ratings & Categorization

Field	Type	Coverage	Description
votesCount	integer	🟢 High (100%)	Upvote count recorded on the product launch (0 on older archived pages without a live counter).
commentsCount	integer	🟢 High (100%)	Total discussion comments on the launch.
aggregateRating	object / null	🟡 Conditional (~79%)	{ ratingValue, ratingCount, bestRating, worstRating } — populated whenever the product has ratings.
applicationCategory	string / null	🟢 High (99%+)	Primary software category label (e.g. Productivity, DeveloperApplication).
operatingSystem	string / null	🟡 Conditional (~55%)	Supported OS/platform tag (e.g. Web, iOS, Mac) when specified by the maker.
topics	array<object>	🟢 High (99%+)	Array of { slug, name } Product Hunt topic tags attached to the product.
datePublished	string	🟢 High (100%)	ISO 8601 timestamp when the product was first launched on Product Hunt.
dateModified	string	🟢 High (100%)	ISO 8601 timestamp when the product page was last updated.
scrapedAt	string	🟢 High (100%)	ISO 8601 timestamp of when this record was extracted.

👥 Makers & User Reviews

Field	Type	Coverage	Description
makers	array<object>	🟢 High (99%+)	Founders and makers credited on the launch: { name, image, profile } (plus username / avatarUrl when available).
reviews	array<object>	🟡 Conditional (~77%)	User reviews ({ rating, body, author, authorProfile, authorImage, datePublished }) when scrapeReviews: true and the product has reviews.

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🔁 Pagination & 1,000+ results scale

You do not need to manage cursors or pagination manually:

1. Multi-source discovery: The actor crawls every topic in topicSlugs, every date in leaderboardDates, and every listing page in startUrls to harvest /products/<slug> handles, merging them with any explicit productSlugs.
2. Automatic deep expansion for 1,000+ runs: Because a single topic landing page or single daily leaderboard surfaces roughly 15–30 launches, setting maxRecords to a high number (such as 500, 1000, or 0 for unlimited) automatically triggers multi-topic and daily leaderboard archive expansion. The scraper walks across 30+ software categories and steps backward day-by-day through daily leaderboards until it discovers enough unique product slugs to fulfill your maxRecords target.
3. Strict de-duplication: Every discovered handle is normalized and de-duplicated in memory before product pages are fetched, guaranteeing 0% duplicate records even when the same product ranks across multiple topics and leaderboard days.

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⚙️ How to use

1. Open the actor in Apify Console and select your targeting mode (topicSlugs, productSlugs, leaderboardDates, or startUrls).
2. Choose your volume: Keep maxRecords: 30–100 for a quick run, or set maxRecords: 1000 (or 0) for deep bulk extraction.
3. Toggle reviews: Leave scrapeReviews off for maximum speed, or enable it when you need review text and reviewer profiles.
4. Click Start and watch structured launch records populate the dataset in real time.
5. Export or integrate: Download JSON, CSV, or Excel, switch between the Overview, Makers, and Reviews dataset views, or consume the dataset via the Apify REST API, Python/JS SDK, Make, Zapier, n8n, or Google Sheets.

Calling via Python SDK

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "topicSlugs": ["artificial-intelligence", "developer-tools", "saas"],
    "scrapeReviews": True,
    "maxRecords": 250,
    "proxyConfiguration": {"useApifyProxy": True},
}

run = client.actor("haketa/producthunt-launches-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} ({item['votesCount']} votes) -> {item['websiteUrl']}")

Calling via JavaScript / Node.js SDK

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('haketa/producthunt-launches-scraper').call({
    topicSlugs: ['artificial-intelligence', 'productivity'],
    scrapeReviews: false,
    maxRecords: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Extracted ${items.length} Product Hunt launches.`);

Calling via cURL (REST API)

curl -X POST "https://api.apify.com/v2/acts/haketa~producthunt-launches-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "topicSlugs": ["artificial-intelligence", "developer-tools", "saas"],
    "scrapeReviews": false,
    "maxRecords": 500
  }'

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📊 Dataset views & export workflows

This actor ships with three pre-configured dataset views in Apify Console so you can export relational data into flat CSV/Excel files without writing custom scripts:

Dataset View	Row Granularity	Key Columns Included	Best Use Case
Overview	1 row per launch	image, name, tagline, productUrl, websiteUrl, applicationCategory, votesCount, commentsCount, aggregateRating, datePublished, topics, makers, reviews	Startup databases, competitive intelligence, market landscape tables
Makers	1 row per (product, maker)	slug, productUrl, name (maker), username, profile, image (avatar)	Founder & builder lead generation, CRM imports, technical recruiting
Reviews	1 row per (product, review)	slug, name (product), productUrl, rating, author, authorProfile, authorImage, body, datePublished	Sentiment analysis, voice-of-customer research, LLM fine-tuning

Supported Topics in the Dropdown (topicSlugs)

Topic Slug	Category Name	Topic Slug	Category Name
artificial-intelligence	Artificial Intelligence	chrome-extensions	Chrome Extensions
productivity	Productivity	analytics	Analytics
developer-tools	Developer Tools	social-media-tools	Social Media Tools
saas	SaaS	e-commerce	E-Commerce
marketing	Marketing	education	Education
design-tools	Design Tools	health-and-fitness	Health & Fitness
no-code	No-Code	games	Games
fintech	Fintech	crypto	Crypto
software-engineering	Software Engineering	web-app	Web App
ios	iOS	android	Android

Need a topic not in the dropdown (such as open-source, api, cybersecurity, remote-work, or sales)? Simply pass https://www.producthunt.com/topics/<slug> inside startUrls.

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💡 Pro tips for best results

• Fastest B2B lead generation: Set scrapeReviews: false when your primary goal is extracting company websites (websiteUrl), upvote traction (votesCount), and founder profiles (makers[]).
• Historical trend analysis: Pass an array of leaderboardDates (e.g., ["2025/5/1", "2025/5/2", "2025/5/3"]) to reconstruct daily launch cohorts with zero overlap.
• Unwound CSV exports for CRMs: In the Apify Dataset tab, switch the view from Overview to Makers before clicking Export to CSV/Excel — you will get one flat row per founder with their name, Product Hunt profile link, and the product they built.
• Proxy best practices: Default Apify Datacenter proxy works reliably out of the box. If you schedule multi-thousand-record crawls at high concurrency, switching to US Residential proxy provides maximum resilience.

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⚠️ Honest limitations

• Archived vote counts: On some older products that have not launched a recent update, Product Hunt's public product hub omits the live upvote badge; in those cases votesCount is reported as 0 while aggregateRating and reviews remain fully populated.
• Data-dependent fields: Approximately 20–23% of newly launched products do not yet have user reviews or star ratings (aggregateRating: null, reviews: []), and ~45% do not specify an operatingSystem tag.
• Public profiles only: Maker records contain public Product Hunt profile URLs, names, usernames, and avatars; personal email addresses are not publicly displayed by Product Hunt (pair websiteUrl with a website contact scraper for email enrichment).

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❓ FAQ

Do I need a Product Hunt account or API token? No. This Product Hunt scraper extracts publicly accessible product pages without any login, cookies, or OAuth developer keys.

How does this compare to the official Product Hunt GraphQL API? The official API requires developer registration, OAuth tokens, and imposes strict complexity rate limits. This actor requires zero setup, includes automatic proxy rotation, and exports directly to JSON, CSV, Excel, or API with pre-built flat views for Makers and Reviews.

How can I scrape 1,000+ launches in a single run? Simply set maxRecords: 1000 (or 0 for unlimited). When your initial topics or dates have fewer than 1,000 products, the actor automatically expands across all major Product Hunt topics and walks daily leaderboard archives until 1,000+ unique launches are collected.

Why did a specific productSlugs handle return no record? If a handle is misspelled or redirects to a removed page that lacks product metadata, the scraper logs a warning, skips that handle cleanly, and continues with the rest of your queue.

Can I schedule daily runs to monitor new AI or SaaS launches? Yes. Use Apify Schedules to run the actor every morning with topicSlugs: ["artificial-intelligence", "saas"] and forward the dataset to Slack, HubSpot, Airtable, or a webhook.

How do I export makers or reviews as a flat spreadsheet? Open the run's Storage → Dataset tab in Apify Console, select either the Makers or Reviews view from the dropdown, and download as CSV or Excel.

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🔗 Related B2B & developer intelligence scrapers

Pair this actor with other scrapers from our portfolio for complete startup and founder enrichment:

• GitHub Developer Scraper — Find Devs by Location & Language
• GitHub Repository Scraper — Search Repos by Topic & Stars
• Hacker News Scraper — Stories, Show HN & Who's Hiring
• npm Package Scraper — Downloads, Maintainers & Emails
• Firefox Add-ons Scraper — Users, Ratings & Developer Leads
• G2 Reviews Scraper — B2B Software Ratings, Pros & Cons

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📋 Changelog

• v1.2 (2026-11) — Unlocked 1,000+ launches pagination via automatic multi-topic and daily leaderboard archive expansion when maxRecords exceeds single-page discovery; tuned prefill (maxRecords: 30, scrapeReviews: false) for fast < 60s test runs; expanded README documentation with coverage indicators and SDK examples.
• v1.1 (2026-11) — Verified across 281 unique products (20 topics + 5 leaderboard dates) with 100% core field fill rate and 0 duplicate slugs; refreshed sample JSON output.
• v1.0 (2026-09) — Upgraded HTTP transport and inline topic/maker extraction for 100% reliability; added unwound Makers and Reviews dataset views.