Best LinkedIn Scraping API

If you're building a product that needs LinkedIn data whether for lead generation, recruiting, market research, or enrichment you've probably realized that LinkedIn's official API is... underwhelming. It requires OAuth, limits you to basic profile info, and needs partner approval that most companies will never get.

That's where LinkedIn scraping APIs come in. But with Proxycurl shutting down in July 2025 after a LinkedIn lawsuit, choosing the right provider matters more than ever. This guide compares the top LinkedIn scraping APIs in 2025, with real pricing, honest pros and cons, and code examples to help you make the right choice.

Why You Need a LinkedIn Scraping API

LinkedIn's official API doesn't cut it for most use cases. Here's why developers turn to scraping APIs:

What LinkedIn's Official API Actually Gives You

1// LinkedIn Official API - After OAuth
2{
3  "firstName": "Sarah",
4  "lastName": "Williams",
5  "email": "[email protected]"
6}

That's it. No work history, no skills, no education, no company data. You're essentially getting what you'd get from "Sign in with LinkedIn" which is fine for authentication, but useless for enrichment.

What You Actually Need

1// What a good LinkedIn scraping API provides
2{
3  "fullName": "Sarah Williams",
4  "headline": "VP of Engineering at TechCorp",
5  "location": "San Francisco, CA",
6  "experience": [
7    {
8      "title": "VP of Engineering",
9      "company": "TechCorp",
10      "duration": "2020 - Present",
11      "description": "Leading 50+ engineers..."
12    }
13  ],
14  "education": [...],
15  "skills": [...],
16  "connections": 5000+,
17  "followers": 12000
18}

Common Use Cases

1. Lead Generation & Sales Intelligence

• Enrich CRM data with job titles, company info, and contact details
• Build targeted prospect lists based on role, industry, location
• Track job changes to identify buying signals

2. Recruiting & Talent Intelligence

• Source candidates matching specific skills and experience
• Build passive candidate databases
• Track talent movement across companies

3. Market Research & Competitive Intelligence

• Analyze company employee growth and hiring patterns
• Identify skill gaps in specific markets
• Track executive movements and company changes

4. Data Enrichment & Verification

• Validate email addresses against LinkedIn profiles
• Enrich existing contact databases
• Fill gaps in customer data

What Makes a Good LinkedIn API

Before diving into specific providers, here's what separates great LinkedIn APIs from mediocre ones:

✅ Essential Features

1. No Account Management Required

• You shouldn't need LinkedIn accounts
• No cookie management
• No risk of account bans

2. High Success Rate & Reliability

• 95%+ success rate on valid requests
• Handles rate limiting internally
• Automatic retries on failures

3. Fresh, Real-Time Data

• Not cached or stale databases
• Direct access to current LinkedIn data
• Recent updates reflected immediately

4. Comprehensive Data Coverage

• Full profile data (experience, education, skills)
• Company information and employee data
• Job postings and search capabilities
• Posts and engagement data

5. Developer-Friendly

• Simple REST API
• Good documentation
• SDKs in multiple languages
• Clear error messages

6. Legal Compliance

• Only scrapes public data
• Clear terms of service
• Transparent about methods
• No fake accounts or violations

Complete API Comparison

Here's how the major LinkedIn scraping APIs stack up:

Detailed Provider Reviews

1. LinkdAPI - Best for Most Developers

⭐ Rating: 9.5/10

LinkdAPI is the unofficial LinkedIn API that actually works like you'd expect an API to work. No complexity, no infrastructure management, no account juggling just clean endpoints that return LinkedIn data.

✅ Pros

• Comprehensive Coverage: 30+ endpoints covering profiles, companies, jobs, posts, search, and more
• Real-Time Data: Direct access to LinkedIn, not cached databases
• Built for Async: Native async/await support with up to 40x faster batch processing
• Developer Experience: Clean Python SDK, TypeScript support, great docs
• No Account Risk: Doesn't use LinkedIn accounts, so zero ban risk
• Transparent Pricing: $0.015-0.03 per profile, no hidden fees
• Fast Setup: Working code in 5 minutes
• : Test before you commit

⚠️ Cons

• Relatively new (less brand recognition than Bright Data)
• Smaller team (though very responsive support)

Pricing

• Free: 100 credits (test ~100 profiles)
• Starter: $59/mo → 10,000 credits
• Startup: $159/mo → 30,000 credits
• Growth: $239/mo → 60,000 credits
• Ultra: $329/mo → 100,000 credits

Code Example

1import asyncio
2from linkdapi import AsyncLinkdAPI
3
4async def enrich_leads(usernames):
5    async with AsyncLinkdAPI("your_api_key") as api:
6        # Fetch 100 profiles concurrently in ~30 seconds
7        tasks = [api.get_profile_overview(u) for u in usernames]
8        profiles = await asyncio.gather(*tasks, return_exceptions=True)
9        
10        return [
11            {
12                'name': p['data']['fullName'],
13                'title': p['data']['headline'],
14                'company': p['data']['CurrentPositions'][0]['name']
15            }
16            for p in profiles if p.get('success')
17        ]
18
19# Process 1000 leads in ~30 seconds
20leads = ['user1', 'user2', ..., 'user1000']
21data = asyncio.run(enrich_leads(leads))

Best For

• Developers building lead generation tools
• Startups needing reliable LinkedIn data
• Companies wanting to avoid infrastructure complexity
• Teams that value simplicity and performance

2. Bright Data - Best for Enterprises (If Budget Allows)

⭐ Rating: 8.5/10

Bright Data (formerly Luminati) is the 800-pound gorilla of web scraping. They have the largest proxy network, won court cases against Meta and X, and power 20,000+ companies worldwide.

✅ Pros

• Proven Legal Track Record: Successfully defended scraping in U.S. courts (2024)
• Massive Scale: 150M+ IPs, 99.99% uptime SLA
• Enterprise Features: Dedicated account managers, custom contracts, audit logs
• GDPR/CCPA Compliant: Strong compliance framework
• Multiple Products: Proxy network + scraper APIs + ready-made datasets

⚠️ Cons

• Expensive: Starting around $500/month for meaningful usage
• Complex Setup: Steep learning curve, not beginner-friendly
• Overkill for Most: Features you probably don't need
• Unreliable Service: Users report frequent downtime
• Pay-Per-GB Model: Costs can spiral quickly

Pricing

• Pay-as-you-go: $1 per 1,000 records (~$0.001 per profile)
• Reality: Minimum spend ~$500-1000/month
• Enterprise: Custom (typically $5K-50K+/month)

Code Example

1# Bright Data LinkedIn Scraper API
2import requests
3
4response = requests.post(
5    'https://api.brightdata.com/datasets/v3/trigger',
6    headers={'Authorization': f'Bearer {API_KEY}'},
7    json=[{
8        'url': 'https://www.linkedin.com/in/username',
9        'include_full_profile': True
10    }]
11)

Best For

• Large enterprises with 6-figure budgets
• Companies needing legal defensibility above all
• High-volume operations (millions of profiles/month)
• Teams with dedicated data engineering resources

3. ScraperAPI - Best General Scraper (Not LinkedIn-Specific)

⭐ Rating: 7.0/10

ScraperAPI is a general-purpose scraping API that can handle LinkedIn along with hundreds of other sites. It's reliable for basic use cases but lacks LinkedIn-specific features.

✅ Pros

• General Purpose: Works on any site, not just LinkedIn
• Simple Pricing: Flat API credits, no surprises
• Good for Learning: Easy to get started
• JavaScript Rendering: Handles dynamic content

⚠️ Cons

• Not LinkedIn-Optimized: Generic scraping, not purpose-built
• Limited Structure: You parse HTML yourself
• No LinkedIn-Specific Endpoints: Just raw HTML scraping
• Higher Costs for LinkedIn: Uses 25-50 credits per profile vs. 1 credit for normal sites
• No Search Features: Can't search profiles or companies

Pricing

• Hobby: $49/mo → 100K API credits (~2,000-4,000 LinkedIn profiles)
• Startup: $149/mo → 1M credits
• Business: $299/mo → 3M credits

Cost per LinkedIn profile: $0.024 - $0.037 (decent but not optimized)

Code Example

1import requests
2
3# Generic HTML scraping - you parse the HTML
4response = requests.get(
5    'https://api.scraperapi.com',
6    params={
7        'api_key': 'YOUR_KEY',
8        'url': 'https://www.linkedin.com/in/username'
9    }
10)
11
12html = response.text
13# Now you need to parse HTML yourself...

Best For

• Scraping multiple websites (not just LinkedIn)
• Teams that need general scraping infrastructure
• Projects where LinkedIn is one of many data sources

4. Scrapingdog - Budget-Friendly Option

⭐ Rating: 7.5/10

Scrapingdog positions itself as the affordable LinkedIn scraper API. It works, the pricing is transparent, but you sacrifice some features and polish.

✅ Pros

• Affordable: $40/month starting price
• LinkedIn-Focused: Dedicated LinkedIn endpoints
• Simple Setup: Straightforward API
• Free Trial: 1,000 credits to test
• Decent Docs: Clear API documentation

⚠️ Cons

• Limited Features: Basic profile and company data only
• No Advanced Search: Can't do complex profile searches
• Slower Performance: Not optimized for concurrent requests
• Smaller Team: Support can be slower
• Less Reliable: Some users report inconsistent success rates

Pricing

• Basic: $40/mo → 10K API calls
• Standard: $100/mo → 30K calls
• Professional: $250/mo → 100K calls
• Enterprise: $1,000/mo → 500K calls

Cost per profile: $0.004 - $0.01 (cheap but basic)

Code Example

1import requests
2
3response = requests.get(
4    'https://api.scrapingdog.com/linkedinprofile/',
5    params={
6        'api_key': 'YOUR_KEY',
7        'profile_id': 'username'
8    }
9)
10
11profile = response.json()

Best For

• Small teams with tight budgets
• Simple use cases (just profile data)
• Testing LinkedIn scraping before scaling

5. PhantomBuster - Cookie-Based Automation

⭐ Rating: 6.0/10

PhantomBuster takes a different approach: it uses your LinkedIn account to scrape data. This was more common 5 years ago, but it's risky in 2025.

✅ Pros

• Automation Workflows: Pre-built "Phantoms" for common tasks
• Multi-Platform: Works on LinkedIn, Twitter, Instagram, etc.
• Visual Interface: No-code setup for non-developers
• Good for Outreach: Can send connection requests, messages

⚠️ Cons

• Account Risk: Uses your LinkedIn account (can get banned)
• Very Limited: Only 80 profiles per day
• Requires Cookies: You manage session cookies
• Slow: Human-like delays (intentionally slow)
• Not Scalable: Can't process thousands of profiles
• Legal Gray Area: Directly violates LinkedIn ToS

Pricing

• Starter: $56/mo → 20 hours execution time
• Pro: $128/mo → 80 hours
• Team: $352/mo → 300 hours

Daily Limit: ~80 profiles/day regardless of plan

Code Example

PhantomBuster isn't really for developers it's a no-code tool. You configure "Phantoms" in their UI, not code.

Best For

• Marketing teams doing outreach
• Non-technical users
• Very small-scale operations (< 100 profiles/day)
• When you already have a LinkedIn account to burn

⚠️ Warning: Using your real LinkedIn account risks permanent ban.

6. Apify - For Developer Power Users

⭐ Rating: 7.5/10

Apify is a platform for building and running web scrapers. They have LinkedIn "actors" (pre-built scrapers), but you need technical skills to use them effectively.

✅ Pros

• Highly Customizable: Write custom scrapers in JavaScript
• Actor Marketplace: Pre-built LinkedIn scrapers available
• Proxy Management: Built-in proxy rotation
• Flexible: Can adapt to any use case

⚠️ Cons

• Steep Learning Curve: Requires JavaScript/Node.js knowledge
• Not LinkedIn-Specific: General scraping platform
• Maintenance Required: Actors can break when LinkedIn changes
• Debugging Complexity: Harder to troubleshoot issues
• Inconsistent Quality: Community actors vary in quality

Pricing

• Free: $5 free credits
• Starter: $49/mo → ~$40 in credits
• Scale: $499/mo → ~$450 in credits

Cost per profile: Varies by actor ($0.01 - $0.05)

Code Example

1// Using Apify's LinkedIn Profile Scraper actor
2const Apify = require('apify');
3
4Apify.main(async () => {
5    const input = {
6        startUrls: [
7            'https://www.linkedin.com/in/username1',
8            'https://www.linkedin.com/in/username2'
9        ]
10    };
11    
12    const actor = await Apify.call(
13        'apify/linkedin-profile-scraper',
14        input
15    );
16});

Best For

• Developers comfortable with JavaScript
• Teams needing custom scraping logic
• Companies scraping multiple sites (not just LinkedIn)

7. Proxycurl - ⚠️ Shut Down (Cautionary Tale)

Status: ❌ Defunct as of July 2025

Proxycurl was one of the most popular LinkedIn APIs, reaching $10M ARR before being forced to shut down after a LinkedIn lawsuit in January 2025.

What Happened

1. January 2025: LinkedIn sued Proxycurl for creating "hundreds of thousands" of fake accounts
2. July 2025: Proxycurl shut down, citing inability to fight Microsoft's legal resources
3. Result: All customers lost access, data had to be deleted per court order

Lessons Learned

• Fake Accounts = Death Sentence: Proxycurl's downfall was using fake LinkedIn accounts
• No Account Access = Safer: APIs that don't use accounts avoid this risk
• Legal Precedent Matters: Choose providers with clear legal standing
• VC Backing Helps: Proxycurl's organic growth meant no war chest for legal battles

Why LinkdAPI Won't Have the Same Problem

Unlike Proxycurl, LinkdAPI:

• ✅ Never uses fake accounts
• ✅ Only scrapes public data
• ✅ Doesn't require login credentials
• ✅ Follows hiQ Labs precedent (scraping public data is legal)

Pricing Comparison

Here's what you'd actually pay for 10,000 LinkedIn profiles per month:

Hidden Costs to Watch For

Bright Data:

• Proxy bandwidth charges
• Premium IP fees
• Data processing fees
• Minimum monthly commits

ScraperAPI:

• JavaScript rendering = 10x credits
• Premium proxies = 25x credits
• LinkedIn often needs both

PhantomBuster: