BUSINESS INTELLIGENCE
Product Hunt data scraping service.
Product launches, upvote velocity, maker data, and category trends from Product Hunt. For VC scouts, PMs, and developer tool companies.
The problem.
WHY THIS IS HARDER THAN IT LOOKS
VC scouts, product managers, and developer tool companies track Product Hunt to identify breakout products, monitor competitor launches, and understand market trends. The platform's search and browsing tools are designed for discovery, not structured analysis at scale.
For VC scouts running early-stage deal flow pipelines, Product Hunt is a signal source that is hard to systematically monitor without structured data. A scout tracking the developer tools or AI infrastructure category needs to know: which products launched in the last 30 days, how their upvote velocity compared to products in the same category from prior months, who the makers are and what they built previously, and whether any of the top-ranking products in a given week share investors or advisors with portfolio companies. That kind of cross-launch analysis is not something you can do by browsing the site. The structured dataset I extract covers launch metrics, maker profiles, category rankings, and historical performance in a format that can be loaded into a spreadsheet or a deal flow database for systematic review.
Product managers at developer tool companies use Product Hunt data differently. They monitor competitor launches to understand positioning: what tagline the competitor chose, which topics they tagged, how the community responded in comments, and whether the launch was supported by a maker network or organic. Pulling this data manually for every relevant launch is slow. Structured extraction delivers it automatically for any category or keyword filter.
The comment data on Product Hunt launches is an underused qualitative signal. Comment threads on highly upvoted products show what early adopters find compelling, what they find missing, and what alternative products they mention in comparison. For product teams doing discovery, comment extraction from the top 20 launches in a category over the past year surfaces the vocabulary real users use to describe their problems, which is more reliable input for positioning and messaging than internal brainstorming.
This service extracts structured product data, launch metrics, maker profiles, and category trends from Product Hunt. The ScrapeBase API at scrapebase.io has Product Hunt endpoints for self-serve access.
Is this right for you?
GOOD FIT IF ANY OF THESE SOUND LIKE YOU
You are a VC scout tracking breakout product launches for early-stage deal flow
You are a PM monitoring competitor launches and category trends
You are a developer tool company tracking the competitive landscape
You need structured PH data for market research beyond what the site's browsing tools provide
You are a product team analyzing comment threads on competitor launches to understand user language and feature priorities
You are a developer tool company that wants automated weekly alerts for new launches in your category
What you receive.
EXACT FIELDS, DELIVERED IN YOUR FORMAT
Sample record.
{
"product_name":"Devin by Cognition",
"tagline":"The first AI software engineer",
"votes_count": 4821,
"comments_count": 387,
"launch_date":"2025-03-12",
"maker_name":"Scott Wu",
"topics": ["AI","Developer Tools","Productivity"],
"website_url":"https://cognition-labs.com/devin",
"extracted_at":"2026-04-14T10:00:00Z"
}PREFER TO SELF-SERVE?
Or access our Product Hunt API yourself
3 ready-to-use endpoints. Pay per successful request. Built for developers.
Straightforward pricing.
SCALE DETERMINES PRICE · NO HIDDEN FEES
Category snapshot
Products in a category with launch data. 2-3 days.
- Up to 1,000 products
- Upvote + comment data
- CSV or Google Sheet
Launch monitoring
Daily tracking of new launches in your categories.
- Category filters
- Velocity alerts
- Weekly digest
Custom
Maker network analysis, trend research.
- Full PH database
- Custom analytics
- Scoping call required
Frequently asked questions.
EVERYTHING YOU NEED TO KNOW
Product profiles (name, tagline, website, topics), launch metrics (upvotes, comments, daily rank, featured status), maker profiles with prior launch history, comment threads, and category listings by date range. The ScrapeBase API has endpoints for category browsing and product search, and the managed service delivers structured datasets for any scope of historical or ongoing research.
Yes. For launch monitoring, the pipeline tracks upvote growth over the first 24-48 hours to identify breakout products before they reach the top of the daily ranking. This early signal is valuable for VC scouts and corporate innovation teams who want to engage with founders at the moment of launch rather than after the product has already circulated through their network.
Yes. Each product record includes the maker profiles linked to the launch, including their Product Hunt profile, prior launches, and products they have upvoted or commented on. For VC scouts, this maker network data helps identify repeat founders and individuals who consistently launch in a specific category before they become well known.
Yes. The historical research tier extracts all launches in a category going back as far as the data is available, with upvote counts, comment counts, and daily ranking position. This is used to benchmark what a successful launch in a specific category looks like and how performance benchmarks have shifted over time as the platform's user base and category composition have changed.
Category snapshot starts at $199. Launch monitoring starts at $499 per month.