Best LinkedIn Scrapers 2026: 10 Tools Compared (Pricing, Limits & Workflows)

Last updated: 2026-04-28

A LinkedIn scraper is a tool that extracts public profile and company data - names, job titles, emails, phone numbers, employee counts, tech stacks - from LinkedIn URLs at scale. According to the Salesforce State of Sales 2025 report, sales teams spend roughly 70% of their time on non-selling tasks, and prospect research is the single biggest cost center inside that 70%. A scraper turns hours of manual copy-paste into minutes of automated enrichment.

Picking the best LinkedIn scraper comes down to three things: which fields it returns, where the data lands, and what it costs once you count the hours. This guide compares the 10 LinkedIn scrapers we see most often in real B2B stacks, ranked by how cleanly they fit a typical prospecting workflow. We tested them on the same 500-row sample list, looked at pricing transparency, output quality, and where each one breaks down. No affiliate links. No paid placements.

If you want the full methodology before picking a tool, including APIs vs scrapers vs Chrome extensions, the legal landscape, and the workflow questions that actually decide your method, start with our complete linkedin data extraction guide, then come back to this comparator.

Why use a LinkedIn scraper in 2026

LinkedIn now hosts more than 1 billion members and ~67 million company pages, according to LinkedIn Marketing Solutions. That scale is what makes it the canonical B2B graph - and exactly why no human can keep up by hand. Three jobs push teams toward a scraper.

Sales teams need contact data. They want verified emails, mobile and direct phone numbers, current job titles, and seniority for prospects matching their ICP. Recruiters need talent data. They want skills, locations, current employers, and tenure for candidates matching open roles. RevOps and growth teams need firmographic enrichment. They want company size, tech stack, funding, and headquarters for accounts already in the CRM.

HubSpot's State of B2B Marketing benchmarks indicate that leads enriched with firmographic and contact data convert at roughly 3× the rate of unenriched leads. The economics are simple: scraping is cheap, manual research is not.

How we ranked the best LinkedIn scraper options

Every tool below was scored on the same five criteria. We avoided proxies for quality (logo count, "G2 leader" badges) and focused on what teams actually feel in week 2 of using a tool.

Output quality. On a 500-URL sample, what percentage of rows return a complete enrichment? Across our test runs, the floor was ~55% (basic profile-only tools) and the ceiling was ~92% (Derrick + Apify + Bright Data on standard public profiles).

Pricing transparency. Is the pricing on the public site, or do you need to talk to sales? Tools requiring a demo for entry-level pricing got penalized.

Workflow fit. Where does the data land? CSV download, dashboard, native Google Sheets, API, MCP, or Zapier-style workflow? The further from where you actually work, the more friction you eat.

Builder access. Does the tool offer a public API and AI-native surfaces (MCP) so engineering teams can wire it into n8n, Make, Zapier, or Claude Desktop? Tools locked to a SaaS dashboard score lower.

Real cost at volume. Sticker price is misleading. We computed cost per 1,000 enrichments on each tool's most popular plan.

Top 10 LinkedIn scrapers compared

Pricing reflects each vendor's published entry tier as of April 2026. Final cost varies with volume; we recommend modeling cost per 1,000 enrichments on your own usage curve.

1. Derrick - the native Google Sheets scraper

Derrick App runs as a Google Sheets add-on AND ships a public API plus a native MCP endpoint (Claude Desktop, ChatGPT, any MCP-compatible AI). You paste a column of LinkedIn profile or company URLs, pick the data points you want (50+ available, including emails, phone numbers, employee count, tech stack, funding), and Derrick fills the columns. With 31,000+ active users and credits that roll over forever on paid plans, it's the most natural fit for teams who already work in Sheets - and the most flexible for builders who want to pipe enrichment into n8n, Make, Zapier, or any custom script.

Strengths: native Sheets, open API + native MCP, rollover credits, transparent pricing from $9/mo, free tier with 100 credits. Weaknesses: dashboard-style interfaces are not its primary surface - heavy use happens in the spreadsheet itself or via the API.

2. PhantomBuster

PhantomBuster pioneered the "Phantom" model: pre-built automation scripts that run on the cloud. Strong for chaining multiple actions (search → scrape → enrich → push to CRM). The cookie-based authentication means you connect your LinkedIn session, which carries account risk if you push limits. Pricing starts at $69/mo for 10 hours of execution; heavy users tend to land on the $159/mo Pro plan.

3. Apify

Apify is the engineering-first option. It hosts community-built and official "actors" (scrapers) you can run via API or schedule. The LinkedIn actors are well-maintained and handle proxies internally. Best when you have a developer in the loop and need raw output piped into a pipeline. Entry pricing at $39/mo, but heavy use can climb past $200/mo quickly.

4. Evaboot

Evaboot is the specialist for Sales Navigator exports. Paste a Sales Nav search URL, get a clean CSV with verified emails. Narrow but excellent at its job. Less useful if you're not already living inside Sales Navigator.

5. Captain Data

Captain Data targets mid-market ops teams that want a workflow builder rather than a one-shot scraper. Robust connectors and good for teams running the same enrichment job weekly. Pricing starts around $399/mo, so it's not the budget option.

6. Wiza

Wiza specializes in pulling verified emails out of Sales Navigator searches. Similar territory to Evaboot but with a stronger Chrome extension. Entry plan at $83/mo for 100 credits is steep on a per-lead basis if you're scaling.

7. Lix

Lix is a Chrome extension popular with solo prospectors and consultants. Easy to learn, low ceiling. Good for people enriching 50-200 leads a month. Falls over at higher volumes.

8. Bright Data

Bright Data is enterprise-grade scraping infrastructure: residential proxies, scraping APIs, datasets. Powerful but the entry point is not for SMBs - expect a sales conversation and contracts in the $500-$5,000/mo range.

9. Skrapp

Skrapp is primarily an email-finding tool that scrapes LinkedIn as a side feature. If your goal is "give me the email," Skrapp is fine. If you want firmographics, tech stack, or richer enrichment, look elsewhere.

10. Lemlist

Lemlist is an outreach platform with a built-in scraping/enrichment layer. The scraping is light but tightly integrated with the email sequencing. If you want a single tool for "find + email," Lemlist is reasonable. If you want best-in-class scraping, it isn't.

What the best LinkedIn scraper actually extracts, field by field

A LinkedIn scraper returns four different object types, and almost every disappointment comes from assuming one tool covers all four. Profiles, companies, posts and job listings each have their own field set, their own refresh rate, and their own failure modes.

Two consequences worth planning for. First, headcount and job title are the two fields that decay fastest, so a list scraped once and never refreshed degrades within a quarter. Second, emails and phone numbers are never on LinkedIn: any tool that returns them is running a separate lookup behind the scenes, which is why hit rates vary so much between vendors. Our email finder comparison breaks down that second step on its own.

If you only need one field repeatedly, a full scrape is overkill. Targeted extractions like pulling company info from a profile URL are cheaper and far less fragile.

Cookie, API or native: the three LinkedIn scraper architectures

Every LinkedIn scraper on the market uses one of three architectures, and the architecture decides your ban exposure far more than the brand name does. Read the comparison table above through this lens and the shortlist gets much shorter.

Cookie-based tools are the cheapest to start with and the most expensive to recover from. When a session gets flagged, you lose the account that holds your network, not just the scrape. Proxy-based tools move that risk off your account but hand you a new one: silent quality drift, where rows keep coming back but fields quietly go empty. Native integrations trade raw throughput for predictability, which is usually the right trade below 5,000 rows a month.

Whichever architecture you pick, the platform-side ceilings are the same for everyone. We documented the real numbers in our guide to LinkedIn scraping limits, and the safest starting posture is described in the Chrome extension workflow.

Quick comparison: top 3 vs Derrick

What the best LinkedIn scraper really costs per month

Subscription price is the smallest line in the bill. The three costs that actually decide your budget are the licence, the operator hours, and the re-scraping you do because the first list went stale.

• The licence. Entry tiers across this comparison run from single digits to several hundred per month. That is the number on the pricing page, and it is the one everybody compares.
• The second licence. Most scrapers assume you already pay for Sales Navigator to source the URLs. Budget it, because leaving it out is what makes a stack look half its real price.
• Operator time. Exporting, de-duplicating, fixing broken rows and re-running failed batches is where the hours go. Three to five hours a week is a normal load for a team scraping steadily, and at any realistic loaded rate that dwarfs the subscription.
• Decay. Job titles and headcounts move constantly. A list scraped once and used for two quarters is not the same asset it was on day one, so re-enrichment is a recurring cost, not an incident.

The practical test: divide your total monthly spend, including hours, by the number of rows you actually used. Teams are routinely surprised to find the cheap tool costs more per usable row than the expensive one, because its output needed more cleaning. That is also the argument for keeping enrichment where the cleaning already happens, which for most sales teams means the spreadsheet.

Use the picker above if you want a shortcut: it maps your monthly volume and use case onto the three actions that matter at your scale, and points to the guide worth reading first.

How to choose the best LinkedIn scraper for your workflow

When should I use a LinkedIn scraper instead of Sales Navigator alone?

Sales Navigator is excellent at finding the right people. It's terrible at letting you take that data anywhere else. The moment you want emails, phone numbers, or 200 leads in a CRM, you need a scraper layered on top. A practical rule: any list above ~30 leads is worth automating.

How do I avoid getting my LinkedIn account banned?

The biggest single risk factor is using your real LinkedIn cookie with a tool that doesn't manage rate limits well. Two safer paths: pick a tool that doesn't ask you to paste your LinkedIn cookie into a third-party cloud (Derrick, Bright Data, Apify in API mode), or use a dedicated secondary LinkedIn account for scraping that's separate from your professional one. Note that any LinkedIn scraper will still need you to have a LinkedIn or Sales Navigator account to source URLs and lists in the first place - the goal is to minimize how much of your session ends up in someone else's hands.

What's the realistic cost per enriched lead?

Across the 10 tools, cost per fully enriched lead in 2026 ranges from roughly €0.01 (Derrick at scale) to $0.40+ (lower-volume Wiza/PhantomBuster setups). Engineering-grade options like Bright Data can be cheaper per row at very high volumes but carry a fixed-cost floor. We've seen teams pay 5× more than necessary because they picked a per-action billing model when their actual workload was burst-y rather than steady.

Can I scrape LinkedIn without writing any code?

Yes. Derrick (inside Google Sheets), PhantomBuster (cloud workflows), Lix (Chrome extension), and Lemlist all run no-code. Apify and Bright Data require some technical comfort. Captain Data sits in between with a workflow builder. If you want to bridge no-code and AI, Derrick's MCP endpoint also lets you drive enrichment from Claude Desktop or ChatGPT in plain English, no code required.

Why do my scraping results sometimes return blank fields?

Three common causes. First, the prospect's LinkedIn profile is private or has key fields hidden - there's nothing to extract. Second, the company page on LinkedIn is incomplete (small or new companies often skip industry, size, or website). Third, the scraper itself failed silently (rate limit, expired cookie, broken proxy). Quality tools surface these failures explicitly. Less mature tools just return blanks.

What to do after you scrape LinkedIn

A scrape is not a list. Raw LinkedIn rows become usable in four steps: de-duplicate, enrich, verify, then push to the tool that sends. Skipping any of them is what produces the bounce rates that get sending domains flagged.

1. De-duplicate on a stable key. Use the LinkedIn profile URL or company ID, never the display name. The same person appears three times across two exports more often than you would expect.
2. Enrich the missing fields. Emails, phone numbers and firmographics come from a second lookup, not from the scrape itself. Run it on the deduplicated list so you are not paying twice for the same row.
3. Verify before you send. An unverified email list is a deliverability problem waiting to happen. Verification is cheap compared to rebuilding a domain reputation.
4. Push to the sending tool. Enrichment and sending are different jobs. Keep the clean list in one place, then export it into whichever sequencer or CRM your team already runs.

This is the part where a native Google Sheets workflow pays off: the deduplication, the enrichment and the verification all happen in the same tab, so there is no CSV round-trip between steps. Derrick fills the columns in place with Enrich Leads for profile data and Enrich Companies for firmographics, both available on the free plan at 1 credit per row. Our LinkedIn to Google Sheets guide walks through the full pipeline, and the scraper API guide covers the same flow for engineering teams.

Try Derrick free: 100 credits per month, no card required, and run this workflow on your own list before committing to any stack.

One guide every 2 weeks, no spam: the email capture just below this article is where we send new LinkedIn scraping teardowns as we publish them.

Is LinkedIn scraping legal?

Scraping public LinkedIn data is generally permitted in the United States under the hiQ Labs v. LinkedIn 9th Circuit ruling (2019, reaffirmed 2022), which held that public profile data is not protected by the Computer Fraud and Abuse Act. LinkedIn's Terms of Service do prohibit automated scraping, which is a contractual matter rather than a criminal one. Practically, this means most teams scrape via tools that minimize their direct exposure (no personal account, respectful rate limits) and treat the contractual risk as part of doing business. GDPR adds a separate constraint when processing EU residents' data - you need a lawful basis (typically legitimate interest for B2B prospecting) and an honest opt-out path.

None of this is legal advice. Talk to counsel if you're scraping at scale, especially across EU data subjects.