Best Crunchbase Scrapers in 2026: Startup Funding, Company Data, Investor Info

Crunchbase is the go-to database for startup and venture capital data. With profiles on millions of companies, funding rounds, investors, and acquisitions, it's essential for anyone in VC research, lead generation, or competitive analysis.

But Crunchbase's API is expensive ($49/month minimum, enterprise pricing for bulk access), and manual browsing doesn't scale. That's where scraping comes in.

What Data Does Crunchbase Have?

Crunchbase tracks the full lifecycle of companies:

• Company profiles — name, description, founded date, HQ location, employee count, website
• Funding rounds — round type (seed, Series A-F, etc.), amount raised, date, lead investors
• Investors — firm profiles, portfolio companies, fund sizes, investment focus
• Founders and team — key people, their roles, previous companies
• Acquisitions — acquirer, target, price, date
• IPO data — valuation, stock symbol, exchange
• Categories and industries — sector classification, technology tags

This structured data is incredibly valuable for market intelligence, but accessing it at scale requires the right tools.

Why Scrape Crunchbase?

Crunchbase's official API pricing puts bulk access out of reach for most researchers and small teams:

• Basic API: Limited to 200 requests/minute, restricted fields
• Pro API: $49/month with usage caps
• Enterprise: Custom pricing (typically $10K+/year)

Scraping lets you access the same public data at a fraction of the cost. Common use cases:

1. VC research — Map funding landscapes, track investment trends by sector
2. Lead generation — Find recently funded startups that need services
3. Competitive analysis — Monitor competitor funding, team changes, and acquisitions
4. Market sizing — Count companies by category, geography, and stage
5. Deal sourcing — Identify investment opportunities matching specific criteria

Best Crunchbase Scrapers in 2026

1. Crunchbase Scraper (Apify)

The Crunchbase Scraper on Apify provides cloud-based Crunchbase data extraction without managing infrastructure.

Key features:

• Search mode — Search companies by keyword, category, or location
• Structured output — Clean JSON with company details, funding data, and more
• Cloud execution — Runs on Apify's infrastructure with automatic scaling
• No API key needed — Scrapes public Crunchbase pages directly

The actor ID is 69rKa1LJibbN8fIVh. One important note: for full company details beyond search results, residential proxies deliver the best results. Apify's residential proxy tier handles this seamlessly.

Example output:

{
  "name": "Example Startup",
  "description": "AI-powered analytics platform",
  "foundedDate": "2024",
  "headquarters": "San Francisco, CA",
  "employeeCount": "51-100",
  "totalFunding": "$12.5M",
  "lastFundingRound": {
    "type": "Series A",
    "amount": "$10M",
    "date": "2025-09-15",
    "leadInvestor": "Sequoia Capital"
  },
  "categories": ["Artificial Intelligence", "SaaS", "Analytics"]
}

2. Build Your Own with Python + ScrapeOps

For custom extraction logic, you can build a Crunchbase scraper in Python. The challenge is that Crunchbase uses aggressive anti-bot measures — you'll need a solid proxy solution.

ScrapeOps provides a proxy aggregator that routes your requests through the best-performing proxies automatically, plus monitoring to track success rates.

import requests
from bs4 import BeautifulSoup

SCRAPEOPS_KEY = "your_scrapeops_key"

def scrape_crunchbase_company(slug):
    url = f"https://www.crunchbase.com/organization/{slug}"
    proxy_url = (
        f"https://proxy.scrapeops.io/v1/"
        f"?api_key={SCRAPEOPS_KEY}"
        f"&url={url}"
        f"&residential=true"
    )

    response = requests.get(proxy_url)
    soup = BeautifulSoup(response.text, "html.parser")

    # Extract structured data from the page
    data = {
        "name": extract_field(soup, "identifier"),
        "description": extract_field(soup, "short_description"),
        "funding": extract_field(soup, "funding_total"),
        "employees": extract_field(soup, "num_employees_enum"),
    }
    return data

def extract_field(soup, field_name):
    el = soup.select_one(f"[data-field=\{field_name}]") 
    return el.text.strip() if el else None

Get started with ScrapeOps — they offer a generous free tier for testing.

3. Crunchbase API (Official)

If budget allows, the official Crunchbase API is the most reliable option:

• Pros: Structured data, stable endpoints, no anti-bot concerns
• Cons: Expensive ($49-10K+/year), rate limited, field restrictions on lower tiers

Best for companies that need guaranteed uptime and compliance.

4. Browser Automation (Playwright/Puppeteer)

For small-scale extraction, headless browser tools can navigate Crunchbase pages:

• Works for one-off research (under 50 companies)
• Handles JavaScript-rendered content
• Slow and resource-intensive at scale
• Higher detection risk without proper proxy rotation

Choosing the Right Approach

Factor	Apify Actor	Python + ScrapeOps	Official API	Browser
Setup time	Minutes	Hours	Minutes	Hours
Scale	Thousands	Thousands	Rate limited	Dozens
Cost	Pay per use	Proxy costs	$49-10K+/yr	Infrastructure
Maintenance	Managed	You maintain	Stable	You maintain
Anti-bot handling	Built-in	Via ScrapeOps	N/A	Manual
Best for	Production	Custom needs	Enterprise	Research

Tips for Scraping Crunchbase Effectively

1. Use residential proxies — Crunchbase blocks datacenter IPs aggressively. ScrapeOps routes through residential IPs automatically
2. Start with search pages — Collect company slugs first, then scrape individual profiles
3. Respect rate limits — Even with good proxies, space your requests (2-5 seconds between hits)
4. Cache everything — Company profiles don't change daily, so cache results for at least 24 hours
5. Handle pagination — Search results are paginated; make sure your scraper follows all pages
6. Monitor success rates — Track your scraping success rate and adjust proxy settings if it drops

Legal Considerations

Crunchbase's data is publicly accessible, but their ToS restricts automated access. When scraping:

• Only collect publicly visible data
• Don't overload their servers
• Use the data for legitimate business purposes
• Consider the official API if you need guaranteed compliance

Conclusion

Crunchbase data is essential for startup research, lead generation, and market analysis. The Crunchbase Scraper on Apify offers the fastest path to structured data, while building your own with ScrapeOps gives you full control over extraction logic.

Whichever approach you choose, start with a clear use case, respect rate limits, and cache aggressively. The startup data landscape moves fast — having reliable scraping infrastructure means you'll always have fresh intelligence.

Happy scraping! 🚀