thunderbit.com

What is Thunderbit?

Thunderbit is a browser-based web scraper that uses AI agents to turn webpages into structured, spreadsheet-ready data. You open a page, click once, and its AI works out which fields to extract instead of requiring you to configure selectors or write code. It runs as a Chrome extension, so the workflow stays inside the browser, and the site describes it as built for non-technical sales and operations teams.

What it actually does

• One-click extraction with AI: point it at a page and let the agent identify fields, rather than mapping elements by hand.
• Templates for common sites: for popular sources such as Amazon, eBay, and Google Maps, pre-built templates return the same fields every time and, per the site, don't consume AI credits. This matters because repeatable, fixed-schema scraping is usually more reliable and cheaper than re-running an AI pass each time.
• AI post-processing during scraping: summarize, categorize, or translate content as part of the output, and reformat or calculate values so exports arrive in the shape you need.
• Long-form text: articles and transcripts can be pulled quickly, which suits research and content monitoring.
• Exports: send results to Google Sheets, Airtable, or Notion, or copy and paste elsewhere.

Who it suits

The page frames its audience around sales and ops work: pulling contact details from sites, PDFs, and images; tracking buying signals like hiring or funding changes; turning directories and marketplaces into lead lists; and enriching contacts. It also lists real estate use cases—aggregating listings from Zillow, Realtor.com, and MLS into a sheet, or monitoring new open houses—plus marketing and general operations.

Trade-offs to weigh

The template-versus-AI distinction is the main decision point. Templates are the better choice for well-known sites where you need consistent columns; AI extraction is more useful for unfamiliar layouts or when you want summaries and translations baked in. A practical example: a sales team scraping a trade-show exhibitor directory once would benefit from AI extraction, while a team monitoring the same marketplace weekly should build or use a template so results stay comparable over time. For heavier or recurring jobs, a dedicated service such as Apify or a no-code tool like Octoparse may fit better; for spreadsheet-native workflows, Import.io is another reference point. Check the official pricing page at Thunderbit for current plan limits before committing a team to it.

How do I scrape a website with Thunderbit without any coding skills?

Install the Thunderbit Chrome extension, open the page you want to scrape, and click the scrape button. Thunderbit is designed so you describe what you want in plain language or pick a pre-built template, and it returns the data as a table you can send to Google Sheets, Airtable, or Notion. No scripts or selectors are required.

A simple workflow

1. Install the extension from the Chrome Web Store and pin it to your toolbar.
2. Open the target page—a directory, search results page, marketplace listing, or article.
3. Choose a template or describe the fields. For popular sites like Amazon, eBay, or Google Maps, Thunderbit's page says templates return the same fields every time and don't use AI credits. For other pages, tell the AI what columns you need (for example, "company name, email, phone").
4. Run the scrape and review the preview. Check that each column maps to the right content before exporting.
5. Export to Google Sheets, Airtable, or Notion, or copy the table.

Practical tips for non-coders

• Start with one page, then paginate. Confirm the fields are correct on a single page before running across many results.
• Use templates where they exist. They are faster and more consistent than free-form AI extraction for well-known sites.
• Ask for transformations in the same step. Thunderbit's page notes you can add summaries, categorization, translation, or reformatting to the output, which saves cleanup later.
• Check public data only. Scraping contact details, listings, or articles is the stated use case; respect each site's terms and privacy rules.

When to choose a template vs. AI

If you need a broader comparison before committing, look at Thunderbit's own templates and pricing page, and compare with established no-code scrapers such as Web Scraper or Octoparse. A useful next step: pick one page you scrape manually today, run it through Thunderbit once, and compare the exported sheet to your hand-built list—that tells you quickly whether the field mapping is reliable enough for regular use.

When should I use a pre-built template instead of AI scraping to save credits?

Use a pre-built template when the site is well known and you want the same fields every time. Thunderbit's own page states that for popular sites like Amazon, eBay and Google Maps, a template returns identical fields instantly and uses no AI credits. AI scraping is the better fit when the page structure is unfamiliar, the fields vary between pages, or you want restructuring such as summaries, categorization or translation added to the output.

A simple decision rule

1. Is the site a common, high-volume target? Start with a template. Repeatable columns and zero credit cost matter most when you scrape the same source weekly.
2. Is the page unusual or changing? Use AI scraping, then save the working setup as a template if Thunderbit lets you reuse it.
3. Do you need derived fields? If you need summaries, categories or translated text as columns, AI is doing real work, not just locating elements, so spending credits is justified.

Where each approach fits

Practical example

A sales team building a prospect list from a marketplace directory would run the template first to get names, titles and companies into a sheet. If some pages need a short summary of each listing before outreach, they would switch to AI scraping for that batch only. This keeps credit spend tied to the pages that actually need interpretation.

Check before committing

Confirm on Thunderbit whether templates cover your specific site, since the page names Amazon, eBay and Google Maps as examples rather than a complete list. Also check the pricing page for how credits are counted, so you know what a template actually saves.

How can sales teams use Thunderbit to build a lead list from directories or marketplaces?

Sales teams can use Thunderbit to turn directory or marketplace pages into a structured lead sheet without manual copy-paste: open a listings or search-results page, run a scrape, and export names, titles, companies and contact details to Google Sheets, Airtable or Notion. The page describes this as one-click extraction from directories and marketplaces, with AI able to reformat or categorise the output during the scrape and to pull contact details from pages, PDFs and images.

A practical workflow

1. Pick a source you can legitimately use. A trade-association member directory, conference exhibitor list or B2B marketplace works well because entries share a consistent layout.
2. Scrape one page first and check the fields. Confirm that company, contact name, title, email and website land in the right columns before running the full list.
3. Use a template for well-known sites. Thunderbit's page says pre-built templates for popular sites return the same fields each time without spending AI credits; AI extraction is the better fit for unfamiliar or irregular directories.
4. Clean and enrich in the same pass. Ask for standardised job titles or company names, and deduplicate before the list reaches your CRM.
5. Export and segment. Send the sheet to Google Sheets or Airtable, then filter by seniority, sector or geography for sequencing.

Where it fits best

Cautions worth building in

Scraped directories often contain generic inboxes rather than named contacts, and email accuracy varies. Verify addresses before outbound sending, and keep the source and date on each record so you can re-scrape later for freshness. Check each site's terms of use and applicable privacy rules before collecting personal data, and follow your own jurisdiction's rules on cold outreach.

For teams that also want buying-signal tracking, the page describes monitoring hiring, funding and tech-stack changes — useful if your list-building is continuous rather than a one-off project.

Can Thunderbit export scraped data directly to Google Sheets, Airtable, or Notion?

Yes. Thunderbit lists Google Sheets, Airtable and Notion as export destinations, and it also supports copying and pasting results into other apps. The export is presented as part of the scraping workflow rather than a separate integration step, so the practical benefit is that a sales or ops user can collect fields such as names, emails, prices or listing details and send them straight into a working sheet or database.

Which destination fits

A concrete example

A sales team scraping a directory could export company names, titles and emails to Google Sheets for a first-pass review, then move qualified rows into Airtable to track outreach status. A marketing team collecting competitor articles might prefer Notion so summaries and source links sit alongside campaign notes.

Before choosing, check whether you need repeatable refreshes into the same destination or a one-off export; that determines whether a simple sheet or a more structured base is the better fit.

How does Thunderbit's free tier compare to its paid pricing plans?

Thunderbit confirms a free tier is available, but the supplied page evidence does not list the paid plan names, prices, or what each tier includes. So a precise free-versus-paid comparison can't be made from this material alone; the Thunderbit pricing page is the place to check current limits.

What the page does make clear is how usage is metered, which is what usually separates free from paid:

• AI credits are consumed by AI-driven extraction and processing. The page states that template-based scraping for popular sites like Amazon, eBay and Google Maps uses no AI credits, while AI features (summaries, categorization, translation, reformatting) do.
• Templates are the low-cost path. If your targets are well-known sites, a template returns the same fields every run without spending credits, which matters most on a free allowance.
• AI steps are the flexible but costly path. Free-form extraction from unfamiliar sites, PDFs or images, plus enrichment, depends on credits and is the first thing a free tier is likely to cap.

A practical way to judge the trade-off: start on the free tier with template scraping for your main sources, and note where you hit limits (credits, volume, or export destinations such as Google Sheets, Airtable and Notion). If your work is mostly AI extraction across varied sites, or you need scheduled refreshes for things like listing monitoring, that is the signal to compare paid tiers on the pricing page.