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Posted on Apr 12

Best Proxycurl Alternative in 2026: Apify LinkedIn Scrapers vs Scrapingdog vs LinkdAPI

#linkedin #scraping #data #api

Proxycurl was the go-to LinkedIn data API for recruiters, sales teams, and developers. At its peak, it served ~200K paying customers and pulled in $10M ARR. Then LinkedIn sued them in January 2025, and by July 2025, Proxycurl was dead.

If you relied on Proxycurl, you need a replacement. I tested the top alternatives so you don't have to.

What Happened to Proxycurl?

LinkedIn filed a lawsuit in January 2025 alleging unauthorized scraping and reselling of member data. Proxycurl fought it briefly, but by July 2025 they shut down completely. API endpoints went dark, documentation was pulled, and their website now shows a generic service discontinued page.

This left a massive gap. Proxycurl was clean, well-documented, and cheap. Finding something comparable is not trivial.

The Contenders

I evaluated four alternatives based on what matters most: reliability, pricing, data quality, and ease of migration.

Feature	Scrapingdog	LinkdAPI	BrightData	Apify LinkedIn Scrapers
Profile Data	Yes	Yes	Yes	Yes
Company Data	Yes	Yes	Yes	Yes
Job Listings	Limited	No	Yes	Yes
Pricing Model	Monthly plans	Monthly plans	Enterprise	Pay-per-event
Starting Price	$40/mo (5K credits)	$49/mo (2K lookups)	Custom (~$500+/mo)	~$0.005/result
Free Tier	1K credits	100 lookups	Trial only	Apify free tier
Rate Limits	10 req/s	5 req/s	Unlimited	Concurrent runs
API Format	REST JSON	REST JSON	REST JSON	REST JSON

Scrapingdog ($40-$200/mo)

Scrapingdog is a general-purpose scraping API with a LinkedIn endpoint. It works, but LinkedIn is not their focus — they cover 20+ sites. Their LinkedIn data sometimes has gaps in education history and skills sections.

Pros: Established company, good uptime, multi-site coverage.
Cons: LinkedIn data quality is inconsistent, monthly commitment, credits expire.

LinkdAPI ($49-$249/mo)

LinkdAPI is LinkedIn-focused, which means better data structure. Their profile endpoint returns clean JSON similar to Proxycurl. The downside is no job listing support, and the $49/mo minimum is steep if you are doing low-volume lookups.

Pros: Clean data format, LinkedIn-focused, Proxycurl-like response structure.
Cons: No job data, minimum $49/mo even for occasional use, 2K lookup cap on starter plan.

BrightData (Enterprise, $500+/mo)

BrightData is the enterprise option. If you are scraping millions of profiles for a sales intelligence platform, this is your pick. For everyone else, it is overkill. Pricing is opaque, onboarding involves a sales call, and the minimum commitment is high.

Pros: Scale, reliability, compliance team.
Cons: Expensive, slow onboarding, not suitable for startups or solo developers.

Apify LinkedIn Scrapers (Pay-Per-Event)

This is what I migrated to. Apify runs actors (cloud scrapers) that you call via API. Two actors cover the Proxycurl equivalent:

LinkedIn Jobs Scraper — public job listings with filters for title, location, and company
LinkedIn Profile and Company Scraper — profiles and company data for lead generation

The pricing model is pay-per-event (PPE): you pay per result returned, not per month. At $0.005-$0.01 per result, 10K profiles costs $50-$100 with no monthly commitment and no expiring credits.

Pros: No monthly fees, pay only for what you use, scales from 1 to 1M results, free tier available.
Cons: Slightly higher latency than direct APIs (runs are async), Apify platform learning curve.

Migration Guide: Proxycurl to Apify

Here is a working Python example that replaces a typical Proxycurl job lookup with the Apify LinkedIn Jobs Scraper:

import requests
import time

APIFY_TOKEN = "your_apify_token_here"  # get at apify.com/account#/integrations
ACTOR_ID = "cryptosignals/linkedin-jobs-scraper"

def scrape_linkedin_jobs(query, location="United States", max_results=25):
    # Start the actor run
    run_url = f"https://api.apify.com/v2/acts/{ACTOR_ID}/runs"
    payload = {
        "searchQueries": [query],
        "location": location,
        "maxResults": max_results
    }
    headers = {"Authorization": f"Bearer {APIFY_TOKEN}"}
    response = requests.post(run_url, json=payload, headers=headers)
    run_id = response.json()["data"]["id"]

    # Poll until finished
    status_url = f"https://api.apify.com/v2/actor-runs/{run_id}"
    while True:
        status = requests.get(status_url, headers=headers).json()
        if status["data"]["status"] in ("SUCCEEDED", "FAILED"):
            break
        time.sleep(5)

    # Fetch results
    dataset_id = status["data"]["defaultDatasetId"]
    results = requests.get(
        f"https://api.apify.com/v2/datasets/{dataset_id}/items",
        headers=headers
    ).json()
    return results

jobs = scrape_linkedin_jobs("python developer", location="Remote")
for job in jobs[:5]:
    print(f"{job.get('title')} at {job.get('company')}")

This pattern works the same way for profiles. Swap the actor ID to cryptosignals/linkedin-profile-scraper and adjust the input parameters. Check each actor page on Apify for the full input schema.

Which One Should You Pick?

Choose Scrapingdog if you need multi-site scraping and LinkedIn is just one of many sources.

Choose LinkdAPI if you only need profile data, want Proxycurl-like JSON responses, and don't mind monthly billing.

Choose BrightData if you are an enterprise doing millions of lookups and need a compliance team.

Choose Apify LinkedIn Scrapers if you want pay-per-use pricing, need jobs and profiles in one place, or you prefer controlling costs per request rather than per month.

For most developers migrating from Proxycurl, Apify makes the most sense financially, especially if your usage varies month to month. You are not locked into a $49/mo plan during months when you only need 200 lookups.

Final Thoughts

Proxycurl's shutdown was a wake-up call about depending on a single vendor for LinkedIn data. Whatever you choose, build your pipeline with swap-ability in mind. Abstract your data source behind an interface so the next time a provider goes down, migration is a config change, not a rewrite.

The LinkedIn data market is more fragmented now than in 2024, but that also means more options and more competitive pricing. For most use cases, pay-per-event pricing will save you money over monthly plans.

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