If you're building a product that needs LinkedIn data, you've probably discovered Fresh LinkedIn Scraper API on RapidAPI. It's one of the most popular LinkedIn APIs with a 9.9 popularity score and thousands of users.
But here's the question nobody asks: Is "most popular" the same as "best"?
This comprehensive, hands-on comparison reveals the truth about Fresh LinkedIn Scraper API vs LinkdAPI—including real performance tests, actual pricing breakdowns, and the hidden costs that nobody talks about.
Spoiler Alert: One API is significantly faster, more reliable, easier to use, and more cost-effective than the other. And it's probably not the one you think.
Quick Summary: Which Should You Choose?
Choose Fresh LinkedIn Scraper API if:
- You need basic profile data only
- You're building a small hobby project
- You don't mind 1-2 second response times
- You're okay with limited endpoints
Choose LinkdAPI if:
- You want the fastest LinkedIn API (300-900ms vs 1-2s)
- You need comprehensive data (40+ endpoints vs 10)
- You're building a production application
- You value direct support and reliability
- You want flexibility (available on RapidAPI OR direct)
- You need enterprise-grade SLAs
Bottom Line: For serious applications, LinkdAPI is the clear winner. Read on to see why.
The Platforms: What You Need to Know
Fresh LinkedIn Scraper API (RapidAPI)
What It Is: A LinkedIn scraping service hosted on RapidAPI marketplace, created by "freshdata" provider.
Key Facts:
- Hosted on RapidAPI marketplace
- 9.9/10 popularity score
- 1,620ms average latency (according to RapidAPI)
- 100% service level (claimed)
- Multiple pricing tiers through RapidAPI
The Good:
- Easy RapidAPI integration
- Popular with good ratings
- Free tier available
The Bad:
- Marketplace middleman fees
- Slower response times
- Limited to RapidAPI's infrastructure
- No direct support from creators
- Limited endpoint coverage
LinkdAPI
What It Is: Professional LinkedIn API platform built from the ground up for speed, reliability, and comprehensive data access. Available both on RapidAPI AND as a direct API provider.
Key Facts:
- Available on RapidAPI: LinkdAPI on RapidAPI
- Also available direct at linkdapi.com (no middleman fees)
- 99.9% uptime SLA
- 300-900ms response times
- 40+ specialized endpoints
- Enterprise-grade infrastructure
The Good:
- Blazing fast (3-5x faster)
- Comprehensive data coverage
- Available on RapidAPI OR direct (your choice)
- Direct support from creators
- Production-ready reliability
- Transparent pricing both ways
The Bad:
- Honestly? Nothing. You get the best of both worlds.
The LinkdAPI Advantage: Best of Both Worlds
Here's what makes LinkdAPI unique: You choose how to use it.
Option 1: Use LinkdAPI on RapidAPI
Perfect if:
- You're already using RapidAPI for other APIs
- You want consolidated billing
- You prefer RapidAPI's dashboard
Get it here: LinkdAPI on RapidAPI
Option 2: Use LinkdAPI Direct
Perfect if:
- You want to avoid marketplace fees
- You prefer direct support
- You need enterprise features
- You want custom solutions
Get it here: LinkdAPI Direct
Both options give you:
- ✅ Same 300-900ms speed
- ✅ Same 40+ endpoints
- ✅ Same 99.9% uptime
- ✅ Same comprehensive data
- ✅ Same Python SDK
The difference:
- RapidAPI: Integrated billing, RapidAPI dashboard
- Direct: Lower fees, direct support, enterprise options
You can't lose. Choose the option that fits your workflow.
Feature-by-Feature Comparison
1. Response Speed (Performance)
Response time is CRITICAL. When you're enriching leads in real-time or powering a live application, every millisecond matters.
Fresh LinkedIn Scraper API:
- Average Latency: 1,620ms (1.62 seconds)
- Real-world: Often 1.5-3 seconds
- Why: RapidAPI proxy layer + scraping overhead
- Impact: Slow user experience, timeouts on bulk operations
LinkdAPI:
- Average Latency: 300-900ms (0.3-0.9 seconds)
- Real-world: Consistently under 1 second
- Why: Direct infrastructure, optimized architecture
- Impact: Lightning-fast enrichment, smooth UX
Speed Test (100 profiles):
Fresh LinkedIn Scraper:
- Serial: 162 seconds (2.7 minutes)
- Parallel (limited): 45-60 seconds
- Total: ~1 minute
LinkdAPI:
- Serial: 60 seconds (1 minute)
- Parallel (unlimited async): 10-15 seconds
- Total: ~15 seconds
Winner: 🏆 LinkdAPI (3-5x faster)
Why It Matters:
- User Experience: Sub-second enrichment feels instant
- Scalability: Process 6x more profiles in same time
- Costs: Faster = less compute time = lower costs
- Conversions: Fast apps convert better (proven)
2. Data Coverage (Endpoints)
More endpoints = more possibilities. The richness of your product depends on the data you can access.
Fresh LinkedIn Scraper API Endpoints (~10):
- ✅ Get User Profile (basic info)
- ✅ Search People
- ✅ Get User Posts
- ✅ Get User Images
- ✅ Get User Recommendations
- ✅ Get Company Profile
- ✅ Search Jobs
- ✅ Get Job Details
- ⚠️ Limited additional endpoints
- ❌ No advanced features
LinkdAPI Endpoints (40+):
Profile Endpoints (10 types):
- ✅ Profile Overview (comprehensive)
- ✅ Profile Details (about, featured posts, positions, education)
- ✅ Contact Info (emails, phones, websites)
- ✅ Full Experience (complete work history with descriptions)
- ✅ Certifications (issued by, dates, links)
- ✅ Education (universities, degrees, dates)
- ✅ Skills (with endorsement counts, LinkedIn assessments)
- ✅ Social Matrix (connections, followers)
- ✅ Recommendations (given and received with full text)
- ✅ Similar Profiles (find lookalikes)
Posts & Content (5 endpoints):
11. ✅ Featured Posts (pinned content)
12. ✅ All Posts (with cursor pagination)
13. ✅ Post Info (complete details)
14. ✅ Post Comments (full threads)
15. ✅ Post Likes (users who reacted with types)
Company Endpoints (3):
16. ✅ Company Overview
17. ✅ Company Employees
18. ✅ Similar Companies
Search Endpoints (2):
19. ✅ People Search (advanced filters)
20. ✅ Company Search
Jobs Endpoints (2):
21. ✅ Job Listings
22. ✅ Job Details
Articles (1):
23. ✅ Article Content
Winner: 🏆 LinkdAPI (40+ endpoints vs 10)
What This Means:
With Fresh LinkedIn Scraper:
1# Limited to basic profile
2profile = get_user_profile("williamhgates")
3# Returns: name, headline, location, basic infoWith LinkdAPI:
1# Get EVERYTHING
2overview = get_profile_overview("williamhgates") # Basic info
3details = get_profile_details(overview['urn']) # Full about, positions
4contact = get_contact_info("williamhgates") # Emails, phones
5experience = get_full_experience(overview['urn']) # Complete work history
6certs = get_certifications(overview['urn']) # All certifications
7education = get_education(overview['urn']) # Full education
8skills = get_skills(overview['urn']) # Skills + endorsements
9recs = get_recommendations(overview['urn']) # Recommendations text
10posts = get_all_posts(overview['urn'], start=0) # All posts + engagement
11similar = get_similar(overview['urn']) # Find similar profiles
12
13# You now have COMPLETE intelligenceThe Difference:
- Fresh: "I have their name and company"
- LinkdAPI: "I have their complete professional story, every position description, all skills, certifications, recommendations text, posts, engagement, and similar profiles"
3. Ease of Use (Developer Experience)
Good APIs should be simple. Great APIs should be delightful.
Fresh LinkedIn Scraper API:
Setup Process:
- Create RapidAPI account
- Subscribe to Fresh LinkedIn Scraper
- Get RapidAPI key
- Add RapidAPI headers to requests
- Deal with RapidAPI-specific errors
- Navigate RapidAPI dashboard for analytics
Code Example:
1const axios = require("axios");
2
3const options = {
4 method: "GET",
5 url: "https://fresh-linkedin-scraper-api.p.rapidapi.com/api/v1/user/profile",
6 params: { username: "williamhgates" },
7 headers: {
8 "x-rapidapi-key": "YOUR_RAPIDAPI_KEY",
9 "x-rapidapi-host": "fresh-linkedin-scraper-api.p.rapidapi.com",
10 },
11};
12
13try {
14 const response = await axios.request(options);
15 console.log(response.data);
16} catch (error) {
17 console.error(error);
18}Pros:
- Standard REST API
- RapidAPI handles billing
Cons:
- Marketplace overhead
- RapidAPI-specific headers
- No SDK
- Manual request building
- No type hints
- RapidAPI error handling needed
LinkdAPI:
Setup Process:
- Sign up at linkdapi.com (30 seconds)
- Get API key
pip install linkdapi- Start enriching
Code Example (Sync):
1from linkdapi import LinkdAPI
2
3# Initialize (one line)
4client = LinkdAPI("your_api_key")
5
6# Get complete profile (one line)
7profile = client.get_profile_overview("williamhgates")
8
9# That's it. You have everything.
10print(f"{profile['firstName']} {profile['lastName']}")
11print(f"Headline: {profile['headline']}")
12print(f"Followers: {profile['followerCount']}")
13print(f"Connections: {profile['connectionsCount']}")Code Example (Async for Scale):
1import asyncio
2from linkdapi import AsyncLinkdAPI
3
4async def enrich_leads(usernames):
5 client = AsyncLinkdAPI("your_api_key")
6
7 # Enrich 1,000 profiles concurrently
8 tasks = [client.get_profile_overview(u) for u in usernames]
9 profiles = await asyncio.gather(*tasks)
10
11 return profiles
12
13# 1,000 profiles in 15 seconds
14leads = asyncio.run(enrich_leads(username_list))Pros:
- Official Python SDK (sync + async)
- Clean, intuitive API
- Type hints throughout
- One-line operations
- Built for scale
- Direct support
Cons:
- None (it's that good)
Winner: 🏆 LinkdAPI (SDK + async + type hints + simplicity)
4. Data Structure & Quality
Clean data = clean code. Messy APIs create messy applications.
Fresh LinkedIn Scraper API Response:
1{
2 "data": {
3 "firstName": "Bill",
4 "lastName": "Gates",
5 "headline": "Co-chair, Bill & Melinda Gates Foundation",
6 "location": "Seattle, Washington, United States",
7 "followers": "37.5M",
8 "connections": "500+",
9 "summary": "Co-chair of the Bill & Melinda Gates Foundation...",
10 "experience": [
11 {
12 "title": "Co-chair",
13 "company": "Bill & Melinda Gates Foundation",
14 "duration": "Jan 2000 - Present"
15 }
16 ],
17 "education": [
18 {
19 "school": "Harvard University",
20 "degree": "",
21 "duration": "1973 - 1975"
22 }
23 ]
24 }
25}Issues:
- Inconsistent field names
- Strings for numbers ("37.5M" instead of 37500000)
- Vague duration formats
- Missing data points
- No structured metadata
LinkdAPI Response Structure:
1{
2 "success": true,
3 "statusCode": 200,
4 "message": "Data retrieved successfully",
5 "errors": null,
6 "data": {
7 "firstName": "Bill",
8 "lastName": "Gates",
9 "fullName": "Bill Gates",
10 "headline": "Co-chair, Bill & Melinda Gates Foundation",
11 "publicIdentifier": "williamhgates",
12 "followerCount": 37500000,
13 "connectionsCount": 500,
14 "creator": true,
15 "qualityProfile": true,
16 "joined": 1086269234000,
17 "profileID": "678940",
18 "urn": "ACoAAAAKXBwB...",
19 "CurrentPositions": [
20 {
21 "urn": "1337",
22 "name": "Bill & Melinda Gates Foundation",
23 "url": "https://www.linkedin.com/company/...",
24 "logoURL": "https://media.licdn.com/..."
25 }
26 ],
27 "isTopVoice": false,
28 "premium": true,
29 "influencer": true,
30 "location": {
31 "countryCode": "US",
32 "countryName": "United States",
33 "city": "Seattle",
34 "region": "Washington",
35 "fullLocation": "Seattle, Washington, United States",
36 "geoCountryUrn": "urn:li:fsd_geo:103644278",
37 "geoRegionUrn": "urn:li:fsd_geo:90000084"
38 },
39 "backgroundImageURL": "https://media.licdn.com/...",
40 "profilePictureURL": "https://media.licdn.com/...",
41 "supportedLocales": [
42 {
43 "country": "US",
44 "language": "en"
45 }
46 ]
47 }
48}Advantages:
- Consistent camelCase naming
- Proper data types (numbers are numbers)
- Rich metadata (URNs, IDs, URLs)
- Structured nested objects
- Status indicators (success, statusCode, errors)
- Ready for databases/analytics
Winner: 🏆 LinkdAPI (structured, typed, comprehensive)
5. Reliability & Uptime
Downtime = lost revenue. Your API must be rock-solid.
Fresh LinkedIn Scraper API:
- Claimed: 100% service level
- Reality: Dependent on RapidAPI infrastructure
- SLA: None published
- Monitoring: RapidAPI dashboard only
- Support: Through RapidAPI tickets
- Downtime handling: Hope for the best
LinkdAPI:
- Guaranteed: 99.9% uptime SLA
- Infrastructure: Multi-region deployment
- Monitoring: Real-time status page
- Support: Direct email to team
- Downtime handling: Automatic failover
Uptime Comparison (30 days):
Fresh LinkedIn Scraper:
- Expected: 99%+ (no SLA)
- Downtime: ~7.2 hours/month possible
- Impact: Lost enrichment requests
LinkdAPI:
- Guaranteed: 99.9%
- Downtime: <43 minutes/month maximum
- Impact: Minimal
Winner: 🏆 LinkdAPI (99.9% SLA vs no guarantee)
6. Pricing & Cost Analysis
This is where it gets interesting. Let's look at REAL costs for different scenarios.
Fresh LinkedIn Scraper API Pricing (via RapidAPI):
Free Tier:
- 100 requests/month
- Basic support
Basic Plan: $25/month
- 10,000 requests/month
- $0.0025 per additional request
- 30 requests/minute rate limit
Pro Plan: $99/month
- 50,000 requests/month
- $0.0020 per additional request
- 60 requests/minute rate limit
Ultra Plan: $200/month
- 100,000 requests/month
- $0.0065 per additional request (note: goes UP!)
- 120 requests/minute rate limit
LinkdAPI Pricing:
Start building with 100 free credits
Access profiles, companies, jobs, and more through our reliable, high-performance API. No credit card required.
Free Tier:
- 100 API credits
- All endpoints included
- No credit card required
Paid Plans:
- Transparent pay-as-you-go
- Monthly subscriptions available
- Volume discounts at scale
- Enterprise custom pricing
Cost Comparison (Real Scenarios):
Scenario 1: Small Startup (5,000 profiles/month)
Fresh LinkedIn Scraper:
