How to Scrape Crunchbase Company Profiles in Python (Funding, Investors, No API Key)

Crunchbase is the primary database for startup funding data. Its free API was discontinued in 2019, and current API plans are enterprise-priced. Scraping is how most teams get Crunchbase data at scale. Here is the full walkthrough.

What you get per company

• Company name, description, and website URL
• Headquarters location (city, country)
• Founded year
• Total funding raised in USD
• Last or largest funding round type and date
• Total investor count
• Crunchbase profile URL

Common use cases

Investment screening. Pull funding data for a list of companies to quickly filter by funding stage, total raised, and geography before a deeper dive.

Sales prospecting. Find recently funded startups in a vertical — Series A and B companies with fresh capital are actively buying software.

Market mapping. Count companies by industry, geography, or funding stage to map a competitive landscape.

CRM enrichment. Append funding stage and total raised to existing lead lists.

Using the Apify actor

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "query": "climate tech",
    "maxResults": 50,
}

run = client.actor('themineworks/crunchbase-companies').call(run_input=run_input)

for company in client.dataset(run['defaultDatasetId']).iterate_items():
    if company.get('_type') == 'summary':
        continue
    print(f"{company['name']} ({company.get('founded_year', 'N/A')})")
    print(f"  Location: {company.get('location', 'N/A')}")
    print(f"  Total raised: ${company.get('total_funding_usd', 0):,.0f}")
    print(f"  Last round: {company.get('last_round_type', 'N/A')} ({company.get('last_round_date', 'N/A')})")
    print(f"  Investors: {company.get('investor_count', 0)}")

Filtering recently funded companies

from datetime import datetime, timedelta

run_input = {
    "query": "B2B SaaS",
    "maxResults": 200,
}

run = client.actor('themineworks/crunchbase-companies').call(run_input=run_input)
results = [r for r in client.dataset(run['defaultDatasetId']).iterate_items()
           if not r.get('_type')]

cutoff = datetime.now() - timedelta(days=365)
recent = []
for co in results:
    date_str = co.get('last_round_date')
    if date_str:
        try:
            dt = datetime.fromisoformat(date_str.split('T')[0])
            if dt > cutoff:
                recent.append(co)
        except ValueError:
            pass

print(f"Funded in last 12 months: {len(recent)}")
for co in sorted(recent, key=lambda x: x.get('total_funding_usd', 0), reverse=True)[:20]:
    print(f"  {co['name']}: ${co.get('total_funding_usd', 0)/1e6:.1f}M | {co.get('last_round_type', '?')}")

Building a targeted prospect list

Find Series A or B companies in a specific sector with raised over a threshold:

import csv

run_input = {
    "query": "vertical SaaS fintech",
    "maxResults": 300,
}

run = client.actor('themineworks/crunchbase-companies').call(run_input=run_input)
results = [r for r in client.dataset(run['defaultDatasetId']).iterate_items()
           if not r.get('_type')]

SERIES = {'Series A', 'Series B'}
MIN_RAISED = 5_000_000

prospects = [
    co for co in results
    if co.get('last_round_type') in SERIES
    and co.get('total_funding_usd', 0) >= MIN_RAISED
    and co.get('website')
]

with open('prospects.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['name', 'website', 'location', 'total_funding_usd', 'last_round_type'])
    writer.writeheader()
    writer.writerows([{k: co.get(k, '') for k in writer.fieldnames} for co in prospects])

print(f"Exported {len(prospects)} prospects to prospects.csv")

Geography breakdown

from collections import Counter

geo = Counter(co.get('location', 'Unknown') for co in results if not co.get('_type'))
print("Top company locations:")
for loc, count in geo.most_common(15):
    print(f"  {loc}: {count}")

Pricing

$0.004 per company returned. No charge on failed anti-bot blocks or empty searches.