Is it legal to scrape BetaList?

Scraping publicly accessible data on BetaList for research or lead generation is generally legal, provided you do not violate their Terms of Service by overwhelming their servers. Always ensure you are compliant with GDPR or CCPA if you are collecting personal identifiers like founder names.

Does BetaList have an official API?

Currently, BetaList does not offer a public, documented API for external developers. To get structured data from the platform, you will need to build or use a web scraping tool.

How do I avoid getting blocked by BetaList?

To avoid blocks, use residential proxies to rotate your IP address and a headless browser to handle the JavaScript-heavy content. Additionally, implement 'human' delays between requests to avoid triggering rate limits.

What format can I export the data into?

Using professional scraping tools, you can export BetaList data into CSV, JSON, or Excel files. Advanced users often use Webhooks to send the data directly into their CRM or Google Sheets.

Can I scrape the names of founders?

Yes, founder names and their profile links are visible on the startup detail pages. You can set your scraper to visit each startup's URL to extract this specific information.

How often should I scrape BetaList for new leads?

Since BetaList features new startups daily, a daily scraping schedule is recommended for sales teams. For market research, a weekly or monthly crawl is usually sufficient.

Does BetaList use Cloudflare?

Yes, BetaList employs Cloudflare for DDoS protection and bot mitigation. This is why standard scripts often fail and require browser automation tools like Playwright.

How to Scrape BetaList | BetaList Web Scraper Guide

Learn how to scrape BetaList to extract startup leads, founder data, and tech trends. Master bypassing Cloudflare and dynamic content for market research.

Start Scraping Free

betalist.comMedium

Coverage:GlobalUnited StatesEuropeIndia

Available Data7 fields

TitleDescriptionImagesSeller InfoPosting DateCategoriesAttributes

All Extractable Fields

Startup NameTaglineFull Product DescriptionWebsite Link (External)Twitter/X Profile URLFounder NameFounder Profile LinkCategory Tags/TopicsUpvote Count (Hearts)Submission/Launch DateLogo Image URLStartup Status (Beta, Pre-launch, etc.)

Technical Requirements

JavaScript Required

No Login

Has Pagination

No Official API

Anti-Bot Protection Detected

CloudflareRate LimitingIP BlockingBrowser Fingerprinting

About BetaList

Learn what BetaList offers and what valuable data can be extracted from it.

The Premier Startup Discovery Platform

BetaList is a widely recognized discovery platform dedicated to early-stage internet startups. Founded by Marc Köhlbrugge, it serves as a launchpad for founders to connect with early adopters, gather feedback, and build initial traction before entering mainstream markets like Product Hunt or the App Store.

Data-Rich Startup Profiles

The platform provides a vast directory of listings across sectors such as SaaS, Artificial Intelligence, Fintech, and E-commerce. Each listing contains rich metadata, including startup taglines, detailed product descriptions, high-resolution screenshots, founder profiles, and social media links. This data provides a snapshot of the newest innovations in the tech ecosystem.

Strategic Value for Data Scraping

For researchers and businesses, scraping BetaList is essential for identifying emerging trends and sourcing high-quality B2B leads. Investors use the platform to spot high-potential startups in their infancy, while service providers (agencies, developers, and marketers) use it to reach out to founders who are actively seeking growth and support tools.

Why Scrape BetaList?

Discover the business value and use cases for extracting data from BetaList.

B2B Lead Generation

Reach out to founders of new companies who need marketing, development, or legal services.

Venture Capital Sourcing

Discover early-stage startups before they gain mainstream popularity for investment opportunities.

Market Trend Analysis

Identify which tech niches (like Generative AI) are seeing the most growth based on submission volume.

Competitive Intelligence

Monitor your industry for new competitors launching similar products or services.

Content Aggregation

Build tech newsletters or startup directories by curating the latest tools from BetaList.

Scraping Challenges

Technical challenges you may encounter when scraping BetaList.

Cloudflare Protection

BetaList uses Cloudflare to block automated traffic, requiring advanced header management or specialized solvers.

Dynamic Page Rendering

Content is loaded via JavaScript, meaning simple HTML parsers often fail to see the startup cards.

Infinite Scroll/Pagination

The platform uses 'Load More' buttons or pagination params that require browser interaction to scrape deep archives.

Lazy-Loaded Media

Images and logos only load when visible in the viewport, requiring a scrolling strategy during extraction.

Scrape BetaList with AI

No coding required. Extract data in minutes with AI-powered automation.

How It Works

Describe What You Need

Tell the AI what data you want to extract from BetaList. Just type it in plain language — no coding or selectors needed.

AI Extracts the Data

Our artificial intelligence navigates BetaList, handles dynamic content, and extracts exactly what you asked for.

Get Your Data

Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.

Why Use AI for Scraping

No-Code Visual Interface: Build a BetaList scraper in minutes by simply clicking on the elements you want to extract.

Automatic Anti-Bot Handling: Automatio manages browser fingerprints and proxies to bypass Cloudflare and IP blocks.

Scheduled Extraction: Set your scraper to run daily at 9:00 AM to automatically capture the latest startup launches.

Seamless Exports: Send your leads directly to Google Sheets, CSV, or a Webhook for immediate sales outreach.

Start Scraping Free

No credit card requiredFree tier availableNo setup needed

No-Code Web Scrapers for BetaList

Point-and-click alternatives to AI-powered scraping

Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape BetaList. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.

Typical Workflow with No-Code Tools

Install browser extension or sign up for the platform

Navigate to the target website and open the tool

Point-and-click to select data elements you want to extract

Configure CSS selectors for each data field

Set up pagination rules to scrape multiple pages

Handle CAPTCHAs (often requires manual solving)

Configure scheduling for automated runs

Export data to CSV, JSON, or connect via API

Common Challenges

Learning curve

Understanding selectors and extraction logic takes time

Selectors break

Website changes can break your entire workflow

Dynamic content issues

JavaScript-heavy sites often require complex workarounds

CAPTCHA limitations

Most tools require manual intervention for CAPTCHAs

IP blocking

Aggressive scraping can get your IP banned

Code Examples

import requests
from bs4 import BeautifulSoup

# Note: BetaList uses Cloudflare; requests alone may get a 403 Forbidden.
# You typically need a bypass or to use a session with realistic headers.
url = 'https://betalist.com/topics/saas'
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8'
}

try:
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # Target the startup card containers
    for card in soup.select('.startupCard'):
        name = card.select_one('.startupCard__name').get_text(strip=True)
        tagline = card.select_one('.startupCard__tagline').get_text(strip=True)
        print(f'Scraped: {name} - {tagline}')
except Exception as e:
    print(f'Request failed: {e}')

When to Use

Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.

Advantages

●Fastest execution (no browser overhead)
●Lowest resource consumption
●Easy to parallelize with asyncio
●Great for APIs and static pages

Limitations

●Cannot execute JavaScript
●Fails on SPAs and dynamic content
●May struggle with complex anti-bot systems

from playwright.sync_api import sync_playwright

def run():
    with sync_playwright() as p:
        # Launch a real browser to handle JavaScript and anti-bot
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        page.goto('https://betalist.com/', wait_until='networkidle')

        # Scroll down to trigger lazy loading
        page.evaluate('window.scrollTo(0, document.body.scrollHeight)')
        page.wait_for_timeout(2000)

        # Extract startup data
        startups = page.query_selector_all('.startupCard')
        for item in startups:
            name = item.query_selector('.startupCard__name').inner_text()
            tagline = item.query_selector('.startupCard__tagline').inner_text()
            print({'startup': name.strip(), 'tagline': tagline.strip()})

        browser.close()

run()

When to Use

Use when content loads dynamically via JavaScript, or when you need to interact with the page (clicks, scrolls, form fills). Handles modern anti-bot detection better.

Advantages

●Executes JavaScript like a real browser
●Handles SPAs and dynamic content
●Better anti-bot evasion with stealth plugins
●Can take screenshots and PDFs

Limitations

●Slower than HTTP requests
●Higher memory/CPU usage
●More complex to set up

import scrapy

class BetalistSpider(scrapy.Spider):
    name = 'betalist_spider'
    start_urls = ['https://betalist.com/topics/ai']

    def parse(self, response):
        # Scrapy is fast but might need a middleware for Cloudflare
        for startup in response.css('.startupCard'):
            yield {
                'name': startup.css('.startupCard__name::text').get().strip(),
                'tagline': startup.css('.startupCard__tagline::text').get().strip(),
                'link': response.urljoin(startup.css('a::attr(href)').get())
            }

        # Handle simple numbered pagination
        next_page = response.css('a.pagination__next::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

When to Use

Ideal for large-scale crawling projects that need to scrape thousands of pages. Built-in support for rate limiting, retries, and data pipelines.

Advantages

●Built for scale (millions of pages)
●Automatic request throttling
●Built-in data export pipelines
●Middleware system for proxies/headers

Limitations

●Steeper learning curve
●Overkill for small projects
●No native JavaScript rendering

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Mimic a real user browser to avoid immediate detection
  await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/110.0.0.0 Safari/537.36');
  await page.goto('https://betalist.com/');

  // Wait for content to render via JS
  await page.waitForSelector('.startupCard');

  const results = await page.evaluate(() => {
    const cards = Array.from(document.querySelectorAll('.startupCard'));
    return cards.map(c => ({
      title: c.querySelector('.startupCard__name').innerText.trim(),
      description: c.querySelector('.startupCard__tagline').innerText.trim()
    }));
  });

  console.log(results);
  await browser.close();
})();

When to Use

Choose this if you're in a Node.js/JavaScript ecosystem or need tight integration with frontend tools. Similar capabilities to Playwright.

Advantages

●Native JavaScript/TypeScript support
●Chrome DevTools Protocol access
●Large ecosystem and community
●Good for JS-heavy projects

Limitations

●Chrome-only (vs Playwright's multi-browser)
●Similar overhead to Playwright
●Less mature stealth options

How to Scrape BetaList with Code

Python + Requests

import requests
from bs4 import BeautifulSoup

# Note: BetaList uses Cloudflare; requests alone may get a 403 Forbidden.
# You typically need a bypass or to use a session with realistic headers.
url = 'https://betalist.com/topics/saas'
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8'
}

try:
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # Target the startup card containers
    for card in soup.select('.startupCard'):
        name = card.select_one('.startupCard__name').get_text(strip=True)
        tagline = card.select_one('.startupCard__tagline').get_text(strip=True)
        print(f'Scraped: {name} - {tagline}')
except Exception as e:
    print(f'Request failed: {e}')

Python + Playwright

from playwright.sync_api import sync_playwright

def run():
    with sync_playwright() as p:
        # Launch a real browser to handle JavaScript and anti-bot
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        page.goto('https://betalist.com/', wait_until='networkidle')

        # Scroll down to trigger lazy loading
        page.evaluate('window.scrollTo(0, document.body.scrollHeight)')
        page.wait_for_timeout(2000)

        # Extract startup data
        startups = page.query_selector_all('.startupCard')
        for item in startups:
            name = item.query_selector('.startupCard__name').inner_text()
            tagline = item.query_selector('.startupCard__tagline').inner_text()
            print({'startup': name.strip(), 'tagline': tagline.strip()})

        browser.close()

run()

Python + Scrapy

import scrapy

class BetalistSpider(scrapy.Spider):
    name = 'betalist_spider'
    start_urls = ['https://betalist.com/topics/ai']

    def parse(self, response):
        # Scrapy is fast but might need a middleware for Cloudflare
        for startup in response.css('.startupCard'):
            yield {
                'name': startup.css('.startupCard__name::text').get().strip(),
                'tagline': startup.css('.startupCard__tagline::text').get().strip(),
                'link': response.urljoin(startup.css('a::attr(href)').get())
            }

        # Handle simple numbered pagination
        next_page = response.css('a.pagination__next::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

Node.js + Puppeteer

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Mimic a real user browser to avoid immediate detection
  await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) Chrome/110.0.0.0 Safari/537.36');
  await page.goto('https://betalist.com/');

  // Wait for content to render via JS
  await page.waitForSelector('.startupCard');

  const results = await page.evaluate(() => {
    const cards = Array.from(document.querySelectorAll('.startupCard'));
    return cards.map(c => ({
      title: c.querySelector('.startupCard__name').innerText.trim(),
      description: c.querySelector('.startupCard__tagline').innerText.trim()
    }));
  });

  console.log(results);
  await browser.close();
})();

What You Can Do With BetaList Data

Explore practical applications and insights from BetaList data.

Lead Enrichment for Sales Teams

B2B agencies use BetaList data to build a pipeline of newly launched startups that need marketing or growth services.

How to implement:

1Scrape startup names and founder profile links from the 'Today' section.
2Visit founder profiles to extract Twitter/X handles.
3Use a third-party API (like Clay or Apollo) to find the founder's email.
4Launch a personalized email sequence referencing their recent BetaList launch.

Use Automatio to extract data from BetaList and build these applications without writing code.

More than just prompts

Supercharge your workflow with AI Automation

Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.

AI Agents

Web Automation

Smart Workflows

Get Started Free

Pro Tips for Scraping BetaList

Expert advice for successfully extracting data from BetaList.

Use Residential Proxies

To avoid 403 errors from Cloudflare, use a proxy provider that offers high-reputation residential IPs.

Rotate User Agents

Switch between modern browser strings (Chrome, Firefox, Safari) to avoid patterns that flag your script as a bot.

Implement Slow Scrolling

BetaList uses lazy loading; scrolling the page slowly (mimicking a human) ensures all data loads into the DOM.

Target Topic Pages

Instead of the home page, scrape URL patterns like /topics/fintech or /topics/ai for more targeted lead generation.

Use Headless Browsers

Standard HTTP clients often fail to render the startup list; use Playwright or Puppeteer for reliable extraction.

Testimonials

What Our Users Say

Join thousands of satisfied users who have transformed their workflow

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.

Mohammed Ibrahim

CEO, qannas.pro

I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!

Ben Bressington

CTO, AiChatSolutions

Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Mohammed Ibrahim

CEO, qannas.pro

Ben Bressington

CTO, AiChatSolutions

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Related Web Scraping

Frequently Asked Questions About BetaList

Find answers to common questions about BetaList

How to Scrape BetaList | BetaList Web Scraper Guide

About BetaList

The Premier Startup Discovery Platform

Data-Rich Startup Profiles

Strategic Value for Data Scraping

Why Scrape BetaList?

B2B Lead Generation

Venture Capital Sourcing

Market Trend Analysis

Competitive Intelligence

Content Aggregation

Scraping Challenges

Cloudflare Protection

Dynamic Page Rendering

Infinite Scroll/Pagination

Lazy-Loaded Media

Scrape BetaList with AI

How It Works

Why Use AI for Scraping

How to scrape with AI:

Why use AI for scraping:

No-Code Web Scrapers for BetaList

Typical Workflow with No-Code Tools

Common Challenges

No-Code Web Scrapers for BetaList

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

How to Scrape BetaList with Code

Python + Requests

Python + Playwright

Python + Scrapy

Node.js + Puppeteer

What You Can Do With BetaList Data

Lead Enrichment for Sales Teams

VC Investment Signal Monitoring

SaaS Competitor Intelligence

Emerging Tech Trend Reports

What You Can Do With BetaList Data

Supercharge your workflow with AI Automation

Pro Tips for Scraping BetaList

Use Residential Proxies

Rotate User Agents

Implement Slow Scrolling

Target Topic Pages

Use Headless Browsers

What Our Users Say

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Frequently Asked Questions About BetaList

Is it legal to scrape BetaList?

Does BetaList have an official API?

How do I avoid getting blocked by BetaList?

What format can I export the data into?

Can I scrape the names of founders?

How often should I scrape BetaList for new leads?

Does BetaList use Cloudflare?