LinkedIn Profile Scraper: the 9 Best LinkedIn Scrapers of 2026 Compared (Pricing, Limits & Fields)

Last updated: 2026-10-19

A LinkedIn profile 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 9 LinkedIn scrapers we see most often in real B2B stacks, organized by architecture and by where the data lands. We 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 profile 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. What share of rows come back with a complete enrichment, and does the tool tell you when a row failed instead of quietly returning blanks?

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. The useful number is cost per 1,000 enrichments on the plan you would actually use.

Top 9 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 - web app and native Google Sheets scraper

Derrick runs in its web app with nothing to install, as a Google Sheets sidebar, through a public REST API and through a native MCP endpoint (Claude, ChatGPT, any MCP client). You paste a list 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. LinkedIn enrichments and imports run on your own LinkedIn session, connected once through the Derrick Chrome extension, so there is no cookie to copy and paste by hand. With 34,000+ users and credits that never expire while you stay on a paid plan, it fits teams who work from lists, and builders who want to pipe enrichment into n8n, Make, Zapier or a custom script.

Strengths: web app with nothing to install, Google Sheets sidebar, open API + native MCP (from the Plus plan at €47.50/mo), credits that never expire while you stay on a paid plan, a free plan with 100 credits per month with no card, then Standard at €20/mo. Trade-off: it is built around lists you enrich, in the web app, in the spreadsheet or via the API, rather than around scheduled crawls in a cloud dashboard.

2. PhantomBuster

PhantomBuster runs pre-built automation scripts ("Phantoms") in the cloud and can chain several actions (search → scrape → enrich → push to CRM). Authentication is cookie-based: you connect your LinkedIn session to its cloud, which carries account risk if you push volume. Pricing starts at about $69/mo for 10 hours of execution, with a higher tier at about $159/mo.

3. Apify

Apify hosts community-built and official "actors" (scrapers) that you run via API or on a schedule, with proxies handled on the platform side. It assumes a developer in the loop who pipes raw output into a pipeline. Entry pricing is about $39/mo, and usage-based costs rise with volume.

4. Evaboot

Evaboot focuses on Sales Navigator exports: you paste a Sales Nav search URL and get a CSV with emails. Its scope is Sales Navigator, so it is less relevant if you do not use it.

5. Captain Data

Captain Data targets mid-market ops teams that want a workflow builder rather than a one-shot scraper, with connectors for enrichment jobs that run on a schedule. Pricing starts around $399/mo.

6. Wiza

Wiza pulls emails out of Sales Navigator searches through a Chrome extension, in the same territory as Evaboot. The entry plan is about $83/mo for 100 credits, a per-lead cost worth modeling before you scale.

7. Lix

Lix is a Chrome extension used mostly by solo prospectors and consultants for light monthly volumes.

8. Bright Data

Bright Data sells scraping infrastructure: residential proxies, scraping APIs and datasets. The entry point is built for larger budgets: expect a sales conversation, with contracts starting around $500/mo.

9. Skrapp

Skrapp is primarily an email-finding tool that reads LinkedIn as a side feature. It covers the email; firmographics, tech stack or richer enrichment need another step.

What a LinkedIn profile 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: you see each row land and you decide the pace.

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. Count the hours your team spends on it each week: at any loaded hourly rate, they quickly outweigh 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 web app or 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 a LinkedIn profile 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: once a list no longer fits in a quick manual pass, automate it.

How do I avoid getting my LinkedIn account banned?

The biggest single risk factor is a tool that runs your LinkedIn session from its own cloud and pushes volume faster than a person would. Two habits keep you on the safe side: keep the pace close to how you would work by hand, and ramp up gradually instead of starting at your target volume. Derrick connects to LinkedIn through its Chrome extension, so you never paste a session cookie into a third-party form, and you decide when each enrichment runs. Any LinkedIn scraper still needs a LinkedIn or Sales Navigator account to source URLs and lists in the first place.

What's the realistic cost per enriched lead?

With Derrick, a LinkedIn profile enrichment costs 1 credit: €0.002 on the Standard plan (€20 for 10,000 credits) and €0.0016 on Scale (€320 for 200,000 credits). Adding a professional email with Email Finder costs 5 credits per email found. For any tool, the number to compute is cost per usable row: infrastructure options carry a fixed-cost floor, and billing per action or per hour of execution can cost far more than expected when your workload comes in bursts rather than steadily.

Can I scrape LinkedIn without writing any code?

Yes. Derrick (in the web app or in Google Sheets), PhantomBuster (cloud workflows) and Lix (Chrome extension) all run without 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 working in one place pays off: in the Derrick web app or in a Google Sheets tab, the deduplication, the enrichment and the verification all happen on the same list, 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.

LinkedIn profile scraper vs company scraper: which one your pipeline needs

A LinkedIn profile scraper and a company scraper answer two different questions, and picking the wrong one is the most common reason a scraping project stalls after week two. A profile scraper works at the person level: it reads a profile URL and returns the human, their current title, their tenure, their location, their skills and the identifiers you need to reach them. A company scraper works at the account level: it reads a company page and returns headcount, industry, locations, growth signals and the shape of the org.

The test is simple. If your next action is addressed to a person (a message, a call, a sequence), you need profile data. If your next action is a decision about a list (which accounts to open, which segment to prioritise, which territory to staff), you need company data. Teams that buy a profile scraper to build a target account list end up with 4,000 rows of individuals and no way to roll them up, and teams that buy a company scraper to run outreach end up with a clean account list and nobody to contact.

The third row is the one that decides your bill. Running a profile scraper across every employee of every account on your list is how a 100-row experiment turns into a 9,000-row invoice. Qualify at the company level first, keep the accounts that match, and only then go down to the people. Inside Derrick that is two separate steps you control row by row: Enrich Companies at 1 credit per company to qualify the account, then Enrich Leads at 1 credit per profile on the survivors only. Both are available on the free plan, which carries 100 credits per month, so a two-step qualification on a small test list costs you nothing but the credits you actually spend.

Contact details sit one level further out, and they are deliberately not part of the profile payload: finding a verified professional email is Email Finder at 5 credits per email found, and a direct phone number is Phone Finder at 200 credits per number found. Both are paid features, both bill only when they return something, and both exist as separate steps precisely so that you never pay for a contact lookup on a row you would have disqualified anyway.

Run a LinkedIn profile scraper without maintaining one: spreadsheet, API or MCP

Most of what breaks in a LinkedIn profile scraper is not the extraction, it is the maintenance. Selectors move, a login flow changes, a proxy pool degrades, and the script that worked in March returns empty strings in September without raising an error. That is the real cost of the open-source route: the code is free, the week you spend every quarter repairing it is not. The alternative is to consume profile data as a service, and there are four ways to do that depending on where you actually work.

Whichever door you use, the payload is the same, which matters more than it sounds: a profile enriched from the sidebar and a profile enriched from the API return the same fields with the same names, so a workflow that starts as a manual sheet can become a scheduled API job without anyone rewriting the mapping. A single profile lookup returns a structured record along these lines:

{
  "linkedin_url": "https://www.linkedin.com/in/example-profile/",
  "first_name": "Dana",
  "last_name": "Whitfield",
  "headline": "VP RevOps at Northwind Logistics",
  "current_title": "VP Revenue Operations",
  "company_name": "Northwind Logistics",
  "company_linkedin": "https://www.linkedin.com/company/northwind-logistics/",
  "location": "Chicago, Illinois, United States",
  "seniority": "VP",
  "tenure_months": 27
}

Two things are worth noticing in that record. The first is that it contains no email and no phone: those come from separate, paid lookups, and keeping them out of the profile payload is what stops a bulk profile run from silently becoming an expensive contact run. The second is tenure_months, which is the kind of field that decides an outreach angle far more reliably than a headline does. Someone 3 months into a VP RevOps seat is rebuilding a stack; someone 27 months in is defending one. Same job title, opposite message.

If you are picking between the open-source route and a managed one, the honest decision rule is the volume of profiles per month multiplied by how much a wrong field costs you. Below a few hundred profiles a month with low stakes, a script you maintain is perfectly reasonable. Above that, or anywhere the output feeds a CRM that other people trust, the maintenance is the product. Our Python scraping guide walks through the DIY path honestly, including what you will be signing up to maintain, and the scraping API guide covers the managed path end to end.

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, teams keep a reasonable pace and treat the contractual risk as part of doing business.

None of this is legal advice. Talk to counsel if you're scraping at scale.