Define a schema, pass a URL, get back JSON that matches. Tabstack's extract endpoint turns any web page into structured output, no parsing code and no LLM call to maintain. generate endpoint adds AI instructions for reasoned answers, not raw fields. Both enforce your schema on every call, even when the page changes. Tune speed with effort levels, target any country with geo_target. Mozilla-backed: your data is never sold or used to train models. 10,000 free credits to start.
There's one piece of code that gets rebuilt at almost every company: the layer that turns a web page into data you can actually use. Fetch, parse, clean, pipe it through an LLM, force it into the shape you wanted. Nobody wants to own it, and it breaks the second a page changes.
That's the thing we deleted.
With Structured Extraction you define the schema, pass a URL, and get JSON back that matches. The reasoning happens inside the call, so there's no parsing code and no second LLM step bolted on after. `extract` pulls the fields you define. `generate` adds instructions on top when you want a reasoned answer, not just raw values.
It's built inside Mozilla, which matters here: the pages we fetch and the data you send are never sold or used to train models.
I'd love to know what you're stuck extracting right now: the messy site, the SPA that fights you, the schema that never holds. Drop your extraction struggle below.
The structured extraction angle is useful, especially if it keeps schema drift visible instead of just returning a 'best effort' JSON blob. Not sure if I missed it, but can teams version extraction rules per site/workflow?
Solid launch for Tabstack by Mozilla: Extract web data and automate browsers, no scraper required.. What was the hardest part to get right so far?
Do you plan to implement some user agent rotation?
For now, all requests are signed with the same user agent: Mozilla-Tabstack/1.0 (+https://tabstack.ai)
I have been using Tabstack for quite some time and loving it :)
Seems like an interesting concept. How well do you handle things like sites with heavy js rendering in them?
The "no scraper to maintain" pitch lands for anyone who's watched selectors break every time a site reships its markup. Does Tabstack lean on the rendered DOM or a model to infer structure — and how does it hold up on pages that lazy-load behind scroll?
No-scraper structured extraction solves a real pain. The challenge has always been handling dynamic content and lazy-load patterns reliably at scale. Running a full browser context per request is expensive, but lighter HTML parsing doesn't catch enough on modern SPAs. How do you handle JS-heavy pages? Do you spin up a real browser for every extraction or have a tiered approach to keep costs down?
I've built a few scraping workflows before and maintenance was always the painful part. How it handles sites that change their structure frequently
The schema first approach is what caught my eye. Scrapers usually work great until a site changes one small thing
This is amazing. I wanna build something around this. The browser automation part seems like a game changer. One question: I see there is 10k credits on the free trial . Is there a time limit?
Congrats on the launch! 🚀 Defining a schema and getting structured JSON back without maintaining scrapers sounds like a huge time saver for developers.
The structured output part is what stood out to me. Getting data is usually easy, keeping it reliable when websites change is the hard part. How often schemas need to be adjusted in real world use?
About Tabstack Structured Extraction on Product Hunt
“Extract web data into structured JSON, no scraper required.”
Tabstack Structured Extraction launched on Product Hunt on June 11th, 2026 and earned 190 upvotes and 37 comments, placing #8 on the daily leaderboard. Define a schema, pass a URL, get back JSON that matches. Tabstack's extract endpoint turns any web page into structured output, no parsing code and no LLM call to maintain. generate endpoint adds AI instructions for reasoned answers, not raw fields. Both enforce your schema on every call, even when the page changes. Tune speed with effort levels, target any country with geo_target. Mozilla-backed: your data is never sold or used to train models. 10,000 free credits to start.
Tabstack Structured Extraction was featured in API (98.2k followers) and Developer Tools (513.9k followers) on Product Hunt. Together, these topics include over 82.3k products, making this a competitive space to launch in.
Who hunted Tabstack Structured Extraction?
Tabstack Structured Extraction was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Reviews
Tabstack Structured Extraction has received 2 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
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fmerian
There's one piece of code that gets rebuilt at almost every company: the layer that turns a web page into data you can actually use. Fetch, parse, clean, pipe it through an LLM, force it into the shape you wanted. Nobody wants to own it, and it breaks the second a page changes.
That's the thing we deleted.
With Structured Extraction you define the schema, pass a URL, and get JSON back that matches. The reasoning happens inside the call, so there's no parsing code and no second LLM step bolted on after. `extract` pulls the fields you define. `generate` adds instructions on top when you want a reasoned answer, not just raw values.
It's built inside Mozilla, which matters here: the pages we fetch and the data you send are never sold or used to train models.
Get started for free with 10,000 credits →
I'd love to know what you're stuck extracting right now: the messy site, the SPA that fights you, the schema that never holds. Drop your extraction struggle below.