We extract daily launch leaderboards, maker intelligence, upvote velocity, and comment threads from Product Hunt. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Daily Launches objects from producthunt.com. All fields typed and schema-versioned.
"product_id": "384912", "name": "Notion AI", "tagline": "Work faster. Write better. Think bigger.", "upvotes": 8492, "rank": 1, "launch_date": "2023-02-22", "comments_count": 412
| # | product_id | name | tagline | description | upvotes | comments_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Maker Profiles objects from producthunt.com. All fields typed and schema-versioned.
"user_id": "14920", "username": "ivanzhao", "name": "Ivan Zhao", "followers_count": 14290, "made_products_count": 4, "joined_date": "2014-08-12", "twitter_handle": "ivanhzhao"
| # | user_id | username | name | bio | twitter_handle | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Comments objects from producthunt.com. All fields typed and schema-versioned.
"comment_id": "2948102", "product_id": "384912", "user_id": "84921", "body": "This changes everything about how we write documentation.", "upvotes": 142, "is_maker": false, "created_at": "2023-02-22T08:14:00Z"
| # | comment_id | product_id | user_id | body | upvotes | replies_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Details objects from producthunt.com. All fields typed and schema-versioned.
"product_id": "384912", "pricing_type": "Freemium", "tech_stack": "['React', 'Node.js', 'PostgreSQL']", "status": "Active", "badges": "['#1 Product of the Day', '#2 Product of the Week']", "video_url": "https://youtube.com/watch?v=dQw4w9WgXcQ"
| # | product_id | name | pricing_type | tech_stack | alternatives | gallery_image_urls |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Upvote Velocity objects from producthunt.com. All fields typed and schema-versioned.
"product_id": "384912", "timestamp": "2023-02-22T14:00:00Z", "upvote_count": 4520, "rank_position": 1, "velocity_1h": 840, "velocity_24h": 4520, "category": "Productivity"
| # | product_id | timestamp | upvote_count | rank_position | velocity_1h | velocity_24h |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Our Product Hunt scraper navigates Next.js hydration, GraphQL endpoints, and infinite scrolling to extract structured launch data, maker intelligence, and upvote velocity.
Extract rankings, upvote counts, and comment volumes for every product launched on a given day.
Profile data for makers and hunters, including social links, follower counts, and historical launch performance.
Track upvote accumulation over time to identify breakout products and viral trajectories.
Full comment text, author details, upvotes, and reply hierarchies for sentiment analysis.
Extract declared technology stacks, pricing models, and specific feature tags.
Map competitive landscapes by extracting related products and user-submitted alternatives.
Access deep historical archives of product launches dating back to the platform's inception.
Aggregate products by topic tags to analyse macro trends in software development.
Run one-off historical exports or configure continuous pipelines for real-time leaderboard tracking.
Brief in. Clean data out.
Provide target dates, topics, or maker IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and GraphQL query interception for producthunt.com.
Schema validation, null-rate checks, and velocity-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Product Hunt relies heavily on client-side state and GraphQL. Here is how we extract data reliably.
Product Hunt loads data dynamically via complex GraphQL queries. We bypass DOM scraping where possible, intercepting and parsing the raw JSON payloads directly from the network layer for maximum schema stability.
For initial page loads, we extract the __NEXT_DATA__ hydration state embedded in the HTML. This provides immediate access to structured product and maker data without executing JavaScript.
Leaderboards and comment threads use cursor-based infinite scrolling. Our crawlers manage cursor state natively, paginating through entire historical archives without browser memory leaks.
Product Hunt limits aggressive IP scraping. We route requests through residential proxy pools with realistic browser headers to maintain uninterrupted access.
For historical data, we maintain a hash index of last-seen values. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Venture capital firms track breakout launches and upvote velocity to identify early-stage investment targets before competitors.
Product teams monitor rival launches, pricing models, and user sentiment in comment threads to inform roadmaps.
Marketing agencies analyse historical launch data to determine optimal days, times, and strategies for future campaigns.
Recruiters identify prolific makers and engineers based on their launch history and community reputation.
Analysts aggregate topic tags and launch volumes to track macro trends in software development and AI.
ML teams use product descriptions, taglines, and comment sentiment to train specialised startup-focused language models.
"Product Hunt is the canonical record of startup launches and maker reputation — but accessing historical and velocity data requires dedicated infrastructure."
Most teams underestimate the investment required: reliable Product Hunt scraping requires residential proxies, GraphQL payload parsing, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our producthunt.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across IN/US/UK/DE regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About producthunt.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Product Hunt is generally permissible under applicable law. DataFlirt targets only public, non-authenticated launch, maker, and comment data. We do not extract personal data, circumvent authentication walls, or violate GDPR.
We intercept GraphQL payloads directly from the network layer and parse __NEXT_DATA__ hydration state embedded in the HTML. This avoids brittle DOM selectors and ensures high schema stability.
Yes. We can paginate through historical leaderboards dating back to the platform's inception in 2013, extracting full product records, maker profiles, and comment threads.
Real-time streaming pipelines achieve sub-15-minute latency for upvote and rank tracking on a defined set of daily launches.
Our smallest packages start at a defined historical export or ongoing daily tracking. For custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 100 recent launches or 50 maker profiles as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical launch dump or a continuous upvote-monitoring feed across 100K products — we scope, build, and operate the pipeline. Tell us what you need.
