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How to Scrape ProductHunt in 2026 (Daily Launches, Trending Products, Maker Data)

#webscraping #python #startup #automation

ProductHunt remains one of the most valuable data sources for anyone tracking the startup ecosystem. Whether you're monitoring competitor launches, researching trending products, or building a database of founders — scraping ProductHunt gives you a real-time pulse on what's shipping.

In this guide, I'll show you how to extract daily launches, trending products, and maker profiles from ProductHunt using a ready-made Apify actor.

Why Scrape ProductHunt?

ProductHunt aggregates thousands of new product launches every month. For competitive intelligence teams, VCs, and indie hackers, this data is gold:

  • SaaS launch monitoring — track what's launching in your niche before competitors gain traction
  • Founder research — build lists of active makers with their profiles, social links, and launch history
  • Trend detection — spot emerging product categories by analyzing upvote patterns and topics
  • Market validation — see how similar products performed at launch before building your own

Manually checking ProductHunt daily doesn't scale. Automated scraping does.

How ProductHunt's Data Works Under the Hood

ProductHunt uses Apollo GraphQL with server-side rendering. This means the full product data — names, taglines, upvotes, maker info, and descriptions — is embedded directly in the initial HTML response as structured JSON. No need to execute JavaScript or intercept API calls.

This architectural choice makes ProductHunt surprisingly scraper-friendly compared to sites that load everything via client-side JavaScript.

The Fastest Way: ProductHunt Scraper on Apify

I built ProductHunt Scraper on Apify to handle four common scraping modes:

1. Daily Launches

Pull every product launched on a specific date:

{
  "mode": "daily",
  "date": "2026-03-26",
  "maxItems": 50
}
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Returns product name, tagline, URL, upvote count, comment count, topic tags, and maker details.

2. Search Products

Search across all of ProductHunt's history:

{
  "mode": "search",
  "query": "AI writing assistant",
  "maxItems": 30
}
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3. Product Details

Get full details for specific product pages including description, media, and all associated makers:

{
  "mode": "product",
  "urls": ["https://www.producthunt.com/posts/example-product"]
}
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4. Maker Profiles

Extract maker data — their launched products, follower counts, and bio information:

{
  "mode": "makers",
  "urls": ["https://www.producthunt.com/@username"]
}
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Sample Output

Here's what a daily launch record looks like:

{
  "name": "LaunchBot AI",
  "tagline": "Automate your product launch across 50 platforms",
  "url": "https://www.producthunt.com/posts/launchbot-ai",
  "votesCount": 342,
  "commentsCount": 47,
  "topics": ["Artificial Intelligence", "Marketing", "SaaS"],
  "makers": [
    {
      "name": "Jane Smith",
      "username": "janesmith",
      "headline": "Founder @ LaunchBot"
    }
  ],
  "launchDate": "2026-03-26"
}
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Practical Use Cases

For VCs and scouts: Set up a daily scrape of new launches, filter by topic (AI, fintech, developer tools), and pipe the results into your CRM. You'll see promising startups on day one.

For SaaS founders: Monitor your product category weekly. Track which competitors are gaining upvotes and what messaging resonates.

For growth teams: Build a database of active ProductHunt makers — these are people who ship regularly and are often open to partnerships, beta testing, and cross-promotion.

Handling Proxy and Rate Limits

ProductHunt doesn't aggressively block scrapers, but if you're running large-scale daily jobs, rotating proxies help. The Apify actor uses Apify's built-in proxy infrastructure, so this is handled automatically.

For DIY setups, a proxy rotation service like ScrapeOps can help you manage residential and datacenter proxies across multiple scraping targets.

Scheduling Automated Runs

On Apify, you can schedule the actor to run daily at a specific time — say 11 PM UTC, after all daily launches are in. Results export to JSON, CSV, or directly to Google Sheets, webhooks, or S3.

This turns ProductHunt into a structured, queryable dataset that updates itself.

Wrapping Up

ProductHunt's GraphQL SSR architecture makes it one of the cleaner sites to scrape in 2026. Whether you're tracking launches for competitive intel or building founder databases, automated extraction saves hours of manual browsing.

Give the ProductHunt Scraper a try — the free tier on Apify is enough to test it out.

Part of the Scraping in 2026 series. Follow for more guides on extracting data from popular platforms.

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