Last week, a friend on a sales ops team pinged me: "I need to pull 200 product listings from a supplier's website into a spreadsheet. Should I use ScrapingBee or Thunderbit?" My first question back: "Do you write code?" His answer — "Absolutely not" — basically decided it for him. But the full picture is more nuanced than that.
These two tools share a goal — getting structured data off the web — but they come at it from opposite directions. ScrapingBee hands you an API key and documentation. Thunderbit hands you a browser button and an AI that suggests what to extract. One is infrastructure for developers who want granular control. The other is a visual workflow for business users who want a spreadsheet. I'll walk through both tools on the same task, break down the real cost math (spoiler: credit counts are misleading), compare features honestly, and give you a clear "pick this if…" verdict for six common use cases. I'll also cover the scenario nobody else writes about: when it makes sense to use both. Thunderbit is an agentic web scraper.
Thunderbit is an agentic web scraper: on a compatible, authorized page, click One Click Extract and the agent detects, reads, and analyzes the page to decide what to extract. Run Now starts immediately, but if you do nothing the task starts automatically—so the default experience needs only one intentional click, with no code, selectors, or schema setup.
What Is ScrapingBee? (And Who Is It Built For?)
ScrapingBee is an API-first web scraping service designed for developers and technical teams. You send it a URL and configuration parameters via an HTTP request, and it fetches the page for you — handling proxies, JavaScript rendering, and anti-bot measures behind the scenes. You get back HTML, Markdown, plain text, screenshots, or structured JSON depending on how you configure the request.
The product has grown considerably since Oxylabs acquired it in June 2025. ScrapingBee continued as a separate product, and the team later attributed support improvements, pricing changes on Google calls, and planned infrastructure upgrades to the deal.
ScrapingBee's current feature set includes:
- Proxy tiers: Classic rotating proxies, premium proxies, and stealth proxies with country routing and sticky-IP sessions
- JavaScript rendering with waits, viewport control, and
js_scenarioactions (clicking, scrolling, filling forms, infinite scroll) - Multiple output formats: rendered HTML, original source, plain text, Markdown, screenshots (viewport, full page, or element), and JSON
- Structured extraction via CSS/XPath
extract_rulesor AI-poweredai_query/ai_extract_rulesparameters - Dedicated site APIs for Google (web, news, maps, images, shopping, AI Mode), Amazon, Walmart, YouTube (search, metadata, subtitles), and Fast Search
- SDK support for Python, Node.js, Java, Ruby, PHP, Go, and cURL, plus a CLI and official integrations with Make, n8n, and Zapier
The audience is clear: developers, data engineers, and technical teams who are comfortable constructing API requests and building extraction pipelines. ScrapingBee also has a dashboard request builder and a remote MCP server for agent workflows, so "API-first" is more accurate than "code-only" — but the mental model is still an API request, not a visual browser tool.
What Is Thunderbit? (And Who Is It Built For?)
Thunderbit is an AI-powered web scraping platform built for business users — particularly sales and ops teams. The primary surface is a Chrome/Edge browser extension that lets you extract structured data from the page you're looking at, without writing code.
The core workflow goes like this: navigate to a page, open the extension, click One Click Extract and let the agent analyze the page; Run Now starts immediately, while doing nothing lets the task start automatically, and export directly to Excel, Google Sheets, Airtable, or Notion. No API requests. No CSS selectors. No JSON parsing.
Key capabilities for business users:
- One Click Extract reads the page and proposes a table schema (columns like Product Name, Price, Rating, URL)
- Field AI Prompts let you add instructions per column — summarize, categorize, translate, format, or label data during extraction
- Subpage enrichment follows links from a list page to detail pages for deeper data
- Pagination handling for multi-page results
- Scheduled extraction for recurring monitoring tasks
- Document and image parsing — extract from PDFs and images, not just web pages
- Direct export to Excel/CSV, Google Sheets, Airtable, Notion
But Thunderbit isn't extension-only. Our team also built an Open API with Distill (clean Markdown) and Extract (structured output) operations, an MCP Server for AI agent workflows, and a CLI for terminal-based use. So developers have a programmatic path too — it's just not the primary surface for the sales rep who needs a lead list by lunch.
Same Page, Two Tools: A Step-by-Step Walkthrough
Instead of listing features in parallel columns, I want to walk through a single concrete task using both tools: extracting product listings (name, price, rating, URL) from a public e-commerce category page.
ScrapingBee Workflow: API Key to Parsed Data
Step 1: Sign up and get your API key. Create an account on ScrapingBee, grab the API key from the dashboard. Straightforward.
Step 2: Understand the parameters. This is where the learning curve kicks in. You need to decide: render_js (on by default), proxy tier (classic, premium, stealth), output format, and extraction method. Each choice affects both the result and the credit cost.
Step 3: Build the request. You can use the dashboard request builder or write it in code. A Python snippet might look something like:
import requests
response = requests.get(
url="https://app.scrapingbee.com/api/v1/",
params={
"api_key": "YOUR_API_KEY",
"url": "https://example-store.com/products",
"extract_rules": '{"name": "h2.product-title", "price": ".price", "rating": ".stars"}'
}
)
Step 4: Send the request, validate the response. Check the JSON output, handle errors, verify the data looks right.
Step 5: Route the output. Write code to save to a file, push to a database, or pipe through an automation tool like Make or n8n to reach a spreadsheet.
Each step requires technical knowledge. Even with extract_rules or AI extraction returning structured JSON (so you're not always manually parsing raw HTML), you still own the request construction, error handling, pagination logic, and downstream delivery.
Thunderbit Workflow: Browser to Spreadsheet
Step 1: Install and sign in. Add the Thunderbit Chrome extension, sign in with your account.
Step 2: Navigate to the target page. Open the e-commerce category page in your browser. If the site requires a login, you're already authenticated in your browser session.
Step 3: Click One Click Extract. Thunderbit's AI reads the page and proposes columns — Product Name, Price, Rating, URL. It does the "what should I extract?" thinking for you.
Step 4: Review and edit. Rename columns, remove ones you don't need, add field-level instructions (e.g., "Convert price to USD" or "Categorize as electronics/clothing/other"). This review step is important — it's not a blind one-click.
Step 5: Let the task start automatically (or use Run Now). Data populates in a structured table inside the extension panel. Configure pagination if you need multiple pages.
Step 6: Export. Click export and choose your destination: Excel, Google Sheets, Airtable, or Notion. Done.
No code written at any step. You stay in the browser the entire time.
Side-by-Side Step Comparison Table
| Step | ScrapingBee (API) | Thunderbit (Extension) |
|---|---|---|
| Account setup | API key from dashboard | Install extension, sign in |
| Define target | Construct API request URL + parameters | Navigate to the page in Chrome |
| Specify fields | Write CSS/XPath selectors or use AI extraction parameter | One Click Extract proposes columns; edit as needed |
| Execute | Send HTTP request (cURL/Python/Node/CLI) | Click "Scrape" |
| Parse output | Validate JSON response; handle errors in code | Structured table in extension panel |
| Export | Write to file/DB in code, or route via automation tool | Export to Excel, Google Sheets, Airtable, or Notion |
The contrast is architectural, not just cosmetic.
ScrapingBee gives you control at every layer. Thunderbit abstracts those layers away so you can focus on the data.
Time to First Result: How Fast Can You Actually Get Data?
No existing comparison article quantifies this, and I'm not going to fabricate benchmark numbers. But I can count the steps and describe the skills required — and the difference is stark.
ScrapingBee: The Developer Path
For a developer who's comfortable with REST APIs:
- Sign up (2 min)
- Read the documentation to understand endpoint parameters, credit multipliers, and extraction options (15–30 min for a first pass)
- Write a first API request with the right selectors (10–20 min, depending on the page's DOM complexity)
- Debug, iterate, validate the response (variable)
- Write code to format and save the output (5–15 min)
A developer familiar with APIs could plausibly complete this in 30–60 minutes for a straightforward page. But that's an editorial estimate based on the workflow steps, not a timed test. For a non-developer? They likely can't complete it at all without help — or without learning to code.
Thunderbit: The Browser Path
For anyone, regardless of technical background:
- Install the extension (1 min)
- Navigate to the target page (1 min)
- click One Click Extract; the agent analyzes the page and prepares the extraction
- Click Run Now for an immediate start, or wait for the automatic start and results (1–2 min)
- Export to your preferred destination (1 min)
The total step count is lower, and none of the steps require technical knowledge. Most users can realistically complete this in under 10 minutes — though I should note that's an estimate based on the workflow, not a controlled experiment.
Learning Curve: API Docs vs. AI Suggestions
The learning models are fundamentally different. ScrapingBee requires understanding REST APIs, HTTP methods, JSON parsing, CSS or XPath selectors, credit multipliers, and proxy configuration. The documentation is well-regarded by developers — the issue isn't quality, it's the inherent complexity of an API-first approach for non-technical users.
Thunderbit’s agent handles the hardest part for beginners: figuring out what to extract and where it lives on the page. You do not need to inspect the DOM or write selectors; the agent determines the extraction plan and starts automatically.
| Dimension | ScrapingBee | Thunderbit |
|---|---|---|
| Onboarding steps | Sign up → Read docs → Write request → Debug → Parse → Export | Install → Navigate → One Click Extract → agentic analysis → auto-start → Export |
| Technical skill required | API literacy, coding, DOM inspection | Browser navigation, table review |
| Estimated time to first export | ~30–60 min (developer) | ~5–10 min (anyone) |
| Non-developer feasible? | Not without significant help | Yes |
Thunderbit vs ScrapingBee: Feature-by-Feature Comparison
I've tried to be fair across every dimension — both tools have genuine strengths.
| Feature | Thunderbit | ScrapingBee |
|---|---|---|
| Primary interface | Browser extension + result table; Web App | REST API + SDKs; dashboard request builder |
| Target user | Sales, ops, marketing, non-technical teams | Developers, data engineers, technical teams |
| Setup model | Install extension, sign in | API key, write/configure requests |
| Coding required | No (extension); Yes (API/CLI) | Yes (API/SDK); Low-code via Make/n8n/Zapier |
| AI-powered extraction | One Click Extract + optional Field AI Prompts | ai_query, ai_extract_rules, ai_selector |
| JavaScript rendering | Browser Mode (current session); Cloud Mode; API render modes | Managed headless browser; js_scenario actions |
| Proxy/anti-bot handling | Managed proxy/anti-bot; API country/header/cookie controls | Classic/premium/stealth proxies; geo, sticky IP, headers, cookies |
| Pagination/subpages | Built-in pagination, infinite scroll, subpage enrichment | User-orchestrated URLs/actions; CLI crawl/batch |
| Scheduling | Recurring scrapers; API batches/webhooks | External scheduler/automation (no built-in hosted scheduler) |
| Export destinations | Excel/CSV, Google Sheets, Airtable, Notion | File/DB via code; Sheets/Airtable via automation tools |
| Document/image parsing | PDF and image extraction | Screenshots; page/document response features |
| Dedicated site APIs | General extraction surfaces | Google, Amazon, Walmart, YouTube, Fast Search |
| Agent integration | Official MCP Server and CLI | Remote MCP and CLI |
| Integrations | Direct exports; API/MCP/CLI | Python, Node, Java, Ruby, PHP, Go SDKs; Make, n8n, Zapier |
Where ScrapingBee Has the Edge
Credit where it's due — ScrapingBee genuinely excels in several areas:
- Granular proxy control. You can choose between classic, premium, and stealth proxies, set country routing, use sticky IP sessions, and forward custom headers and cookies. If you're scraping heavily protected targets, this level of control matters.
- Dedicated site APIs. Google Search (including AI Mode, maps, images, shopping), Amazon, Walmart, and YouTube endpoints return structured data for specific platforms. This is a real advantage for teams doing large-scale SERP monitoring or e-commerce price tracking via API.
- Screenshot capabilities. Viewport, full-page, and element-level screenshots are useful for visual monitoring and compliance workflows.
- Deep request customization.
js_scenarioactions let you click, scroll, fill forms, and execute custom JavaScript before extraction. For complex multi-step scraping, this is powerful. - Established developer ecosystem. SDKs in seven languages, extensive documentation, and a mature community of 4,000+ developers.
Where Thunderbit Has the Edge
Now for the Thunderbit side — and yes, I'm on the team, but I've got receipts:
- No-code visual workflow. The extension-to-spreadsheet path requires zero technical skills. One Click Extract removes the need to write selectors or define extraction logic manually.
- Direct business exports. One-click export to Excel, Google Sheets, Airtable, or Notion without writing code or configuring an automation tool.
- Field AI Prompts. You can summarize, categorize, translate, format, and label data during extraction — not as a separate post-processing step.
- Subpage enrichment. Follow links from a list page to detail pages and pull deeper data, all within the extension workflow.
- Browser session advantage. Because the extension runs in your browser, it can use your existing login and session context on authorized pages.
- Document and image parsing. Extract structured data from PDFs and images alongside web pages — same tool, same workflow.
The Real Cost: Thunderbit vs ScrapingBee Pricing Compared
Most comparison articles get the pricing story wrong. They list monthly prices and credit counts side by side, and the reader thinks "250,000 credits for $49 sounds like a lot." It's not — at least not always.
ScrapingBee's Credit Multipliers Explained
ScrapingBee's credit system uses multipliers based on the features you enable per request:
| Request Configuration | Credits Per Request |
|---|---|
| Classic proxy, JS off | 1 |
| Classic proxy, JS on (default) | 5 |
| Premium proxy, JS off | 10 |
| Premium proxy + JS | 25 |
| Stealth proxy + JS | 75 |
| AI extraction | +5 added to the base |
Since render_js defaults to true, a standard request with classic proxies costs 5 credits. That means 250,000 credits on the Freelance plan gets you 50,000 default-JS requests — not 250,000. If you need premium proxies with JavaScript, you're down to 10,000 requests. Stealth + JS? About 3,333.
To put that in concrete terms:
| 250,000 Credits Gets You… | Actual Requests |
|---|---|
| Static classic (JS off) | 250,000 |
| Default JS (classic) | 50,000 |
| Premium + JS | 10,000 |
| Stealth + JS | ~3,333 |
| Default JS + AI extraction | 25,000 |
Thunderbit's Credit System
Thunderbit's no-code extension charges per output row, not per input page. One standard row = one credit. A subpage-enriched row = two credits. So a category page with 50 products costs about 50 credits (standard) or 100 credits (with subpage enrichment).
Thunderbit's API pricing is separate: Distill costs 1 unit/page and Extract costs 20 units/page, billed on a different meter.
Cost Comparison by Workload
Comparing these two pricing models directly is tricky because they measure different things (requests vs. output rows). But here's my best attempt at an apples-to-apples illustration for common workloads. Verify all numbers against each tool's current ScrapingBee pricing and Thunderbit pricing pages before making a purchase decision.
| Workload | ScrapingBee | Thunderbit (Extension) |
|---|---|---|
| 10K pages, static/classic | Freelance $49 (10K of 250K credits) | Pro 3 $125 (assuming ~10K output rows) |
| 10K pages, JS rendered (default) | Freelance $49 (50K of 250K credits) | Pro 3 $125 (same row-based cost) |
| 10K pages, premium+JS | Freelance $49 (250K of 250K credits — just barely) | Pro 3 $125 |
| 50K pages, JS rendered | Startup $99 (250K of 1M credits) | Beyond Pro 4 or use Thunderbit API |
| 100K pages, JS rendered | Startup $99 (500K of 1M credits) | Thunderbit API or custom plan |
| 100K pages, premium+JS | Business $249 (2.5M of 3M credits) | Thunderbit API or custom plan |
A few things jump out.
For static or low-protection targets at high volume, ScrapingBee's per-request cost can be very low. For moderate volumes of business data (lead lists, competitor snapshots, market research), Thunderbit's row-based pricing is predictable and doesn't fluctuate with proxy tier. At scale (50K+ pages), both tools require higher-tier plans or API access.
The key insight: ScrapingBee's effective cost depends heavily on how you scrape (proxy tier, JS rendering, AI extraction), not just how much. Thunderbit's cost depends on how many rows you extract.
Use-Case Verdict: Pick Thunderbit or ScrapingBee If…
Not a feature dump — a decision tree.
| Use Case | Better Fit | Why |
|---|---|---|
| Quick lead list from a single site | Thunderbit extension | No code; One Click Extract + export to Sheets in minutes |
| Scraping pipeline in a production app | ScrapingBee API | Designed for developer integration, stable endpoint, proxy management, error handling |
| Price monitoring (recurring schedule) | Depends on scale | Thunderbit scheduled extraction for moderate volume; ScrapingBee + external scheduler for high-volume pipelines |
| One-off market research deep dives | Thunderbit extension | Visual, interactive; no setup overhead for ad-hoc tasks |
| Large-scale SERP data collection | ScrapingBee API (or Thunderbit Open API) | Dedicated Google Search API; API throughput matters at volume |
| Feeding data into AI/LLM workflows | Either (different surfaces) | ScrapingBee via code or LangChain; Thunderbit via MCP Server or Open API |
| Ad-hoc competitor analysis | Thunderbit extension | Browse competitor pages, extract what you see, export immediately |
Quick Lead Lists and One-Off Research
If you're a sales rep who needs 200 contacts from a supplier directory by end of day, the Thunderbit extension is the obvious fit. Navigate to the page, One Click Extract, scrape, export to Google Sheets. No API key, no code, no waiting for engineering to build something. This is the use case our team built Thunderbit around — and it's where the no-code workflow genuinely saves hours.
Production Scraping Pipelines
If your engineering team is building an automated data pipeline that runs nightly, pulls from 50 sources, handles retries, and feeds a database — ScrapingBee is the better foundation. It's designed for developer integration: stable API endpoints, granular proxy control, multiple SDK options, and the request-level customization needed for production reliability. You own the orchestration, which is exactly what you want in a production system.
Price Monitoring and Recurring Schedules
This one genuinely depends on scale. Thunderbit offers scheduled extraction (recurring scrapers) that work well for monitoring a moderate number of pages — say, tracking 500 competitor product prices weekly. For high-volume, always-on monitoring of thousands of URLs, ScrapingBee's API combined with a custom scheduler (cron job, Airflow, or an automation tool) gives you more throughput and control.
Large-Scale Data Collection and AI Workflows
For SERP monitoring or e-commerce data at scale, ScrapingBee's dedicated Google, Amazon, Walmart, and YouTube APIs are a genuine advantage — they return structured data for those platforms without you having to figure out the extraction logic. Thunderbit's Open API also supports developer workflows with AI-structured extraction, but it doesn't have the same dedicated site endpoints.
For AI/LLM pipelines, both tools have agent-compatible surfaces. ScrapingBee offers a remote MCP server and LangChain integration. Thunderbit has an official MCP Server and CLI. The choice depends on whether you want raw page access with your own extraction logic (ScrapingBee) or AI-structured extraction as part of the scraping step (Thunderbit).
When to Use Thunderbit and ScrapingBee Together
Every comparison article I've read frames this as either/or.
But some teams genuinely need both — for different jobs.
Picture a mid-size company with two very different data needs. The sales and marketing team needs quick, visual, ad-hoc extractions. Lead lists from directories. Competitor pricing snapshots. Research on potential partners. They don't code, they don't want to wait for engineering, and they need data in a spreadsheet by tomorrow. Thunderbit extension.
Meanwhile, your engineering team is building automated data pipelines. Nightly price monitoring across 10,000 SKUs. SERP tracking for SEO. Feeding structured data into a recommendation engine. They need API-level control, proxy management, retry logic, and integration with their existing stack. ScrapingBee API.
And there's a middle ground: developers who want AI-structured extraction without managing proxies themselves can use Thunderbit's Open API or MCP Server. It's a different abstraction layer — you get structured output without writing selectors, but through a programmatic interface.
I won't pretend this is the common case. Most teams will pick one tool based on their primary need. But acknowledging that these tools solve different problems for different people on the same team feels more honest than pretending one tool does everything.
Thunderbit vs ScrapingBee: Summary Comparison Table
| Dimension | Thunderbit | ScrapingBee |
|---|---|---|
| Primary interface | Browser extension + visual table | REST API + dashboard builder |
| Target user | Sales, ops, marketing, non-technical | Developers, data engineers |
| Setup time | Minutes (install extension) | Minutes (get API key), but learning curve follows |
| Coding required | No (extension); Yes (API/CLI) | Yes (API); Low-code via Make/n8n/Zapier |
| AI extraction | One Click Extract + optional Field AI Prompts | ai_query, ai_extract_rules |
| Export destinations | Excel, Google Sheets, Airtable, Notion | File/DB via code; Sheets via automation tools |
| Proxy management | Managed (extension/API) | Classic/premium/stealth with granular controls |
| Dedicated site APIs | No | Google, Amazon, Walmart, YouTube, Fast Search |
| Scheduling | Built-in recurring scrapers | External scheduler required |
| Document/image parsing | Yes | Screenshots; page response features |
| Pricing model | Per output row (extension); per page/operation (API) | Per request with credit multipliers |
| Free tier | Free plan (6 pages/month) | Free trial (1,000 credits, no card) |
| Best for | Ad-hoc extraction, lead lists, market research, non-technical users | Production pipelines, high-volume API workflows, protected targets |
| Learning curve | Low | Moderate to high |
| API/developer access | Open API, MCP Server, CLI | REST API, SDKs (7 languages), CLI, MCP |
| G2 rating | 5.0/5 (limited reviews) | 4.8/5 (26 reviews) |
| Capterra rating | 4.8/5 (9 reviews) | 4.9/5 (137 reviews) |
(Review sample sizes differ; treat ratings as directional.)
Which Tool Fits Your Team?
The core distinction hasn't changed from the first paragraph. ScrapingBee is infrastructure for developers who want control over every layer of their scraping pipeline. Thunderbit is a ready-to-use tool for business users who want data in a spreadsheet without engineering help.
Pick Thunderbit if you're a non-technical user who needs data fast, your tasks are ad-hoc or moderate-volume, and you value direct exports to business tools. Try the free plan and see how quickly you can go from a web page to a usable spreadsheet.
Pick ScrapingBee if you have developers on your team, you're building automated pipelines, you need granular proxy control for protected targets, or you're scraping at high volume through dedicated site APIs. Their free trial gives you 1,000 credits to test the API.
Pick both if your organization has both non-technical teams doing ad-hoc research and engineering teams building production data infrastructure. Different tools for different jobs — and that's fine.
Neither tool is universally better. The right choice depends on who's using it, what they're building, and how much control they need. I've tried to give you enough detail here to make that call with confidence.
FAQs
Is ScrapingBee free?
ScrapingBee offers a free trial with 1,000 API credits, no credit card required. Those 1,000 credits translate to 200 default-JS requests (at 5 credits each) or 1,000 static requests (at 1 credit each). Paid plans start at $49/month for 250,000 credits.
Does Thunderbit require coding?
No — at least not for the browser extension workflow. The standard path is One Click Extract → agentic analysis columns → Scrape → Export. No selectors, no API calls, no parsing code. Thunderbit also offers an Open API, MCP Server, and CLI for developers who prefer programmatic access.
Can I use Thunderbit and ScrapingBee for the same website?
Yes. They serve different workflows. You might use Thunderbit's extension for quick, interactive extraction from your browser (e.g., pulling a lead list during research) and ScrapingBee's API for automated, scheduled scraping of the same site at scale in a production pipeline.
Which tool is better for scraping JavaScript-heavy websites?
Both handle JavaScript rendering. ScrapingBee does it server-side via managed headless browsers (at 5x credit cost for default JS, more for premium/stealth proxies). Thunderbit's Browser Mode renders pages in your current browser session, which also handles JS-heavy sites and benefits from any existing login or session context. Neither guarantees success on every site — results vary by target.
How do ScrapingBee's credit multipliers actually work?
Each ScrapingBee API request consumes credits based on the features enabled: 1 credit for a static classic request, 5 for JS rendering (the default), 10–75 for premium or stealth proxies, and +5 for AI extraction on top of the base. This means 250,000 credits on the Freelance plan could be anywhere from 3,333 to 250,000 actual page fetches depending on your configuration. Always calculate your effective cost based on the request types you'll actually use.
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