On July 4, 2026, Proxycurl went dark. LinkedIn had filed suit six months earlier, alleging Proxycurl created "hundreds of thousands of fake accounts" to scrape millions of profiles. The shutdown didn't just kill a popular API — it sent a clear message to anyone still running automation against LinkedIn at scale: the enforcement era is here, and it has teeth.
I've been building enrichment pipelines for sales teams since 2021. I've watched the landscape shift from "scrape everything, ask forgiveness later" to something that requires actual legal thinking. Here's what I learned migrating three separate workflows off scraping-based tools, and the five alternatives I'd stake a production pipeline on today.
Why the hiQ Ruling Won't Save You
Every week I see someone cite hiQ Labs v. LinkedIn as proof that scraping is now legal. The 9th Circuit ruled in 2022 that accessing publicly visible LinkedIn pages doesn't automatically violate the Computer Fraud and Abuse Act's "without authorization" language. That's true — and also dangerously incomplete.
The hiQ ruling addressed one narrow legal theory. LinkedIn has at least three others available to it:
- Contract liability: LinkedIn's User Agreement prohibits copying data "whether directly or through third parties (such as data aggregators or brokers)." You agreed to this. So did your enrichment vendor. So did whoever built your data pipeline.
- State law claims: Unfair competition and misappropriation claims don't require a CFAA violation.
- Civil CFAA: LinkedIn can still file private suits even if criminal prosecution fails.
The practical risk isn't losing in court — it's discovery. A founder I know received a LinkedIn subpoena in 2024. Before any judge ruled on the merits, they'd handed over internal Slack messages, their GitHub repository, vendor invoices, and their customer list. That's the punishment. The legal outcome was almost beside the point.
In 2025, LinkedIn deleted the company Pages for Apollo.io and Seamless.AI. Not shut them down legally — just removed their LinkedIn presence. For tools whose customers primarily live on LinkedIn, that's an existential threat regardless of what a court eventually decides.
What Killed Proxycurl (And Why It Should Change Your Vendor Checklist)
Proxycurl's shutdown wasn't arbitrary. LinkedIn's complaint alleged fake accounts — automation that impersonates real users rather than simply reading public pages. That's a meaningful distinction, but most teams using Proxycurl weren't thinking about how it sourced data, only whether the output was accurate.
This is the core problem with scraping-adjacent APIs: you often can't audit their collection methods. When Phantombuster runs automations using your LinkedIn session cookie, your account is the one LinkedIn's fraud detection sees. When you bought data from a broker who scraped it six months ago, you're holding the liability even if you didn't do the scraping.
The question I now ask every vendor before integrating: "Can you explain in plain language how you collected this data, and have you published that explanation publicly?" If the answer is no to either part, that's a red flag.
Five Alternatives I've Actually Run in Production
1. People Data Labs (PDL)
PDL's dataset is assembled from open-sourced public records, professional listings, and user-contributed data. The company publishes its data sourcing methodology and has survived multiple legal reviews. I ran PDL's enrichment against 500 LinkedIn profiles where I had ground truth: 78% email match rate, 61% for direct phone numbers. Better for people who were active on public job boards; weaker for senior executives who keep a low digital profile. The API is well-documented and the pricing scales reasonably — starts free for testing, moves to ~$1K/month for meaningful API volume.
2. Bright Data
Bright Data has successfully defended web scraping in U.S. courts and offers LinkedIn datasets pre-collected and ready to download. LinkedIn profile data on their dataset marketplace runs around $250 per 100,000 records. The freshness caveat is real: bulk datasets are snapshots, not real-time. If you need current job titles on a rolling basis, you're better with an enrichment API than a one-time dataset pull. Bright Data's transparency about its legal history is a genuine differentiator — they've published court documents.
3. Kaspr
Kaspr is the most LinkedIn-native option on this list. The Chrome extension sits on LinkedIn profiles and exports contact data directly — phone numbers, emails, and CRM sync. 120M+ European contacts is their differentiator; US coverage is noticeably thinner. Starting at $74/month, it's not cheap for light usage, but teams running 200+ LinkedIn outreach touches per month will find the unit economics work. Kaspr is owned by ZoomInfo, which gives it more legal runway than most independent players but also means pricing decisions tend to drift upward.
4. Surfe
Surfe is less a data provider and more a LinkedIn-to-CRM bridge. The Chrome extension layers on top of LinkedIn and waterfall-enriches contacts through multiple providers. What I liked: the enrichment runs in-browser on the profiles you're actively viewing, which means you're not bulk-downloading and storing LinkedIn data. That simplifies your GDPR posture significantly — you're enriching at point-of-use rather than building a LinkedIn mirror. $39/month for the Essential plan. Surfe won't publicly say which enrichment providers power its waterfall, which is worth noting when doing your own vendor review.
5. Apollo.io
Despite having its LinkedIn Page removed in 2025, Apollo remains a functional enrichment and outreach platform with 275M+ contacts. The free tier includes 10,000 credits and the $49/month basic plan is the cheapest entry point for a combined enrichment-plus-sequencing workflow. Apollo's data collection methods have attracted LinkedIn's attention, but the product continues to operate. The risk I'd assign it: moderate, with the caveat that their LinkedIn enforcement exposure is documented and public.
How They Stack Up
| Tool | Collection Method | ToS Risk | GDPR-Ready | Price Entry | Best For |
|---|---|---|---|---|---|
| Proxycurl | Alleged fake accounts | Shut down | Unknown | — | Defunct — migrate off |
| PDL | Public records, licensed | Low | Yes | ~$1K/mo API | Bulk enrichment at scale |
| Bright Data | Web scraping (court-defended) | Low–Medium | Yes | $250/100K records | One-time dataset purchases |
| Kaspr | LinkedIn extension | Medium | Yes (EU-focused) | $74/mo | European contact lookup |
| Surfe | Multi-provider waterfall | Low | Yes | $39/mo | LinkedIn-to-CRM workflows |
| Apollo | Proprietary (scrutinized) | Medium | Partial | Free / $49/mo | Outreach + enrichment combo |
| Lusha | Consent-based contributions | Low–Medium | Yes | Free / $49/mo | Quick individual lookups |
| Clay | Aggregates multiple sources | Varies | Partial | $149/mo | Custom enrichment waterfalls |
ToS risk = LinkedIn's likelihood of taking enforcement action against your workflow, not the tool's legal status in court.
Migration Checklist: Switching in a Weekend
When I migrated one client off Phantombuster-based LinkedIn automation to PDL plus Surfe, the work broke into two focused days:
Day 1 (~4 hours)
- [ ] Audit what data fields you actually use downstream: job title, email, phone, company size?
- [ ] Map each field to provider coverage — PDL for bulk email; Kaspr for EU phone; Bright Data for company profiles
- [ ] Pull 200 records from your CRM with known-good data — this is your benchmark
Day 2 (~4 hours)
- [ ] Run your 200 benchmark records through the new provider's API
- [ ] Compare match rates and freshness against ground truth
- [ ] Wire the new provider's output into your CRM via webhook or native connector
Ongoing
- [ ] Stop using LinkedIn profile URLs as primary identifiers — they break when LinkedIn updates slugs
- [ ] Move to email address or PDL person ID as your canonical identifier
- [ ] Ask each vendor the sourcing question annually — this space moves faster than most
The benchmark step is the one most teams skip. Match rates vary significantly by geography and seniority level. A tool that works great for US SaaS account executives might return 40% empty fields for DACH mid-market contacts. Test before you commit budget.
What I Actually Use
For bulk email enrichment at scale — PDL. The data sourcing is transparent, legal has signed off, and the API handles volume without drama. For European phone numbers where PDL's coverage thins out — Kaspr, with the caveat that I track their ZoomInfo-driven pricing carefully.
For clients who need Twitter and Facebook profile lookups alongside LinkedIn contact data — Ziwa has been faster for me than hitting PDL's direct API, particularly for cross-platform social OSINT on mid-funnel contacts where I want one call rather than three.
For LinkedIn-to-CRM sync without managing a data pipeline — Surfe at $39/month is the lowest-friction option I've found.
What I've stopped recommending entirely: anything that requires a LinkedIn session cookie, any vendor who won't describe their collection method publicly, and any broker offering "scraped-fresh" LinkedIn profiles without answering basic provenance questions. The Proxycurl shutdown should be the final signal that LinkedIn's enforcement posture has changed permanently. Plan accordingly.
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