LinkedIn Company Scraper (No Login) ✅ Bulk

LinkedIn company data scraper - posts, jobs, offices & similar companies

Scrape the full public LinkedIn company page in one call: employees, every listed office location, recent posts with their media, products, similar companies, and live open-job counts. No login, no cookies, no browser.

$1 per 1,000 companies. No monthly minimum, no subscription.

One record per company bundles posts with engagement metrics, every office location a company lists worldwide, its products, and the live open-job count. Give it a company name, a LinkedIn URL, a company website or a LinkedIn company ID and it finds the right page for you.

Why this scraper?

• 🪙 $1 per 1,000 companies - among the lowest-priced full-featured LinkedIn company scrapers on Apify, where comparable actors charge $3 to $5. No monthly minimum.
• 📦 Every public field in one call - about, employees, address, offices, posts, products, similar companies, and live open-job count.
• 💼 Live open-job count - the exact number of open jobs LinkedIn lists for the company in the United States, included at no extra cost. Most LinkedIn company scrapers don't offer it at all.
• 🎯 Names, websites and IDs work, not just slugs - type "Ernst & Young", "Accenture plc" or a name in another script such as "日立製作所" (Hitachi), paste a website such as stripe.com, or a numeric LinkedIn company ID, and get the right company. Every result is checked against LinkedIn's own company ID.
• 📈 Posts with engagement and media - the recent posts shown on the page with text, date, reaction and comment counts, who posted them, and their video, images, article link or live event. Reposts are flagged, and a post that quotes another one includes the quoted post.
• 👥 Sample employees - up to four people shown on the public page, with name and profile link, ready for outreach.
• 🗺️ All office locations, not just HQ - every office LinkedIn lists (up to 100), worldwide, with street, city, region, postal code, and country, so you can map a company's footprint across cities and countries.
• 🧩 Products - every product a company lists on LinkedIn, with category and link.
• 🔗 Similar and affiliated companies with context - each comes with its industry and location, not just a name.
• ✅ Every company you send is accounted for - each distinct input gets a row. One that cannot be matched gets a row saying why, free of charge, so you never have to diff your list to find what is missing.
• 🌍 Regional presence - the languages LinkedIn has localised the company page into show which markets it is present in.
• ⚡ Fast and light - about 100 companies in about four minutes at the default memory, roughly three times faster with 1 GB. No cookies, no browser.

What data can you extract?

🏢 Company name	🏷️ Tagline	📝 About	🏭 Industry
🌐 Website	👥 Employee count (exact)	📊 Employee range	📅 Founded year
🏛️ Organization type	📍 HQ address (structured)	🗺️ All office locations	📣 Follower count
🎯 Specialties	💼 Open job count (US)	🖼️ Logo URL	🎨 Cover image URL
🔗 Similar companies	🏢 Affiliated pages	👥 Employees preview	🌐 Locale alternates
📰 Recent posts + reactions	🧩 Products	📮 Office postal codes	🆔 Organization ID
🎞️ Post media (video, images, articles, live)	✍️ Post author	🔁 Quoted posts	🔎 LinkedIn jobs link

How to scrape LinkedIn companies: step by step

1. Create a free Apify account. Takes 30 seconds, no card needed.
2. Open LinkedIn Company Scraper in the Apify Console.
3. Paste company URLs, slugs, names, websites or LinkedIn company IDs into the input box.
4. Click Start. A run of 50 companies takes about two minutes.
5. Export as JSON, CSV, or Excel - or pull via the Apify API.

How much does LinkedIn Company Scraper cost?

LinkedIn Company Scraper uses pay-per-result pricing. You pay $1.00 per 1,000 companies, plus a $0.001 per-run start fee. The Apify Free plan's $5 in monthly credits covers about 5,000 companies, and the $29 Starter plan about 29,000. Inputs that can't be matched to a company are never charged.

No minimum order and no subscription lock-in. At $1 per 1,000 this is among the lowest-priced full-featured LinkedIn company scrapers, where comparable actors charge $3 to $5 per 1,000.

Input

{
    "companies": [
        "google",
        "https://www.linkedin.com/company/stripe/",
        "Ernst & Young",
        "openai.com"
    ],
    "maxResults": 0,
    "maxConcurrency": 8
}

Field	Type	Default	Notes
companies	string[]	required	LinkedIn company URLs, bare slugs, company names in any language, company websites (stripe.com), or numeric LinkedIn company IDs. URL paths like /about/ are stripped automatically. A value made only of digits is read as a LinkedIn company ID, so for a company whose name is only digits (such as 1688) paste its LinkedIn URL.
maxResults	integer	0	Cap total companies. 0 = scrape everything you provided.
maxConcurrency	integer	8	How many companies to fetch in parallel (1-15). Lower if you see rate limits.

Output

One row per company. This is a real row for Stripe, with long lists shortened.

{
    "input": "stripe",
    "companyName": "Stripe",
    "companySlug": "stripe",
    "companyUrl": "https://www.linkedin.com/company/stripe",
    "companyUrn": "2135371",
    "description": "Stripe builds programmable financial services.",
    "slogan": "Help increase the GDP of the internet.",
    "website": "https://stripe.com",
    "industry": "Technology, Information and Internet",
    "organizationType": "Privately Held",
    "companySizeRange": "5,001-10,000 employees",
    "employeeCountRange": {
        "min": 5001,
        "max": 10000
    },
    "exactEmployeeCount": 16694,
    "foundedYear": 2010,
    "headquarters": "South San Francisco, California",
    "address": {
        "street": "354 Oyster Point Blvd",
        "city": "South San Francisco",
        "state": "California",
        "postalCode": "94080",
        "country": "US",
        "countryName": "United States"
    },
    "officeLocations": [
        {
            "isPrimary": true,
            "street": "354 Oyster Point Blvd",
            "city": "South San Francisco",
            "region": "California",
            "postalCode": "94080",
            "country": "US",
            "fullAddress": "354 Oyster Point Blvd, South San Francisco, California 94080, US",
            "directionsUrl": "https://www.bing.com/maps?where=354+Oyster+Point+Blvd+South+San+Francisco+94080+California+US&trk=org-locations_url",
            "countryName": "United States"
        },
        {
            "isPrimary": false,
            "street": "1 Wilton Park, Wilton Terrace",
            "city": "Dublin",
            "region": "County Dublin",
            "postalCode": "D02 FX04",
            "country": "IE",
            "fullAddress": "1 Wilton Park, Wilton Terrace, Dublin, County Dublin D02 FX04, IE",
            "directionsUrl": "https://www.bing.com/maps?where=1+Wilton+Park%2C+Wilton+Terrace+Dublin+D02+FX04+County+Dublin+IE&trk=org-locations_url",
            "countryName": "Ireland"
        }
    ],
    "followerCount": 1762786,
    "openJobCount": 806,
    "specialties": [],
    "fundingRounds": null,
    "lastFundingRound": null,
    "fundingInvestors": [],
    "crunchbaseUrl": null,
    "logoUrl": "https://media.licdn.com/dms/image/v2/D560BAQE2ZfJyfn-VCg/company-logo_200_200/B56ZlyKwpUKIAI-/0/1758557047806/stripe_logo?e=2147483647&v=beta&t=En4Hm6NDGbDTYCoOQ0Ko5Ne2gylx0WRb0yL2GlOeXBQ",
    "coverImageUrl": "https://media.licdn.com/dms/image/v2/D563DAQFt98MykXcktg/image-scale_191_1128/B56Z352EMMKIAY-/0/1778013193531/stripe_cover?e=2147483647&v=beta&t=UwiZWbPrYkD77DpQmZIjVuuqKMCfyZ-e-YcHE336MlY",
    "similarCompanies": [
        {
            "name": "Atlassian",
            "url": "https://www.linkedin.com/company/atlassian",
            "industry": "Software Development",
            "location": "Sydney, NSW"
        },
        {
            "name": "Airbnb",
            "url": "https://www.linkedin.com/company/airbnb",
            "industry": "Software Development",
            "location": "San Francisco, CA"
        }
    ],
    "affiliatedPages": [
        {
            "name": "Stripe Partner Ecosystem",
            "url": "https://www.linkedin.com/showcase/stripe-partner-ecosystem",
            "industry": "Internet Publishing",
            "location": "South San Francisco, California"
        }
    ],
    "products": [
        {
            "name": "Stripe",
            "category": "Payment Processing Software",
            "url": "https://www.linkedin.com/products/stripe",
            "imageUrl": "https://media.licdn.com/dms/image/sync/v2/C4D27AQG8G0fkDJ7C3g/articleshare-shrink_480/articleshare-shrink_480/0/1712155030493?e=2147483647&v=beta&t=mLUHZYvxwBTeJOUiuN2tKwFjS4Tye9537DIpX9iTXn4"
        }
    ],
    "recentPosts": [
        {
            "text": "We’re partnering with FedEx Dataworks. Together, we’ll leverage supply-chain data to help small businesses access financing from Stripe Capital to accelerate their growth.\n\nhttps://lnkd.in/g8fQwgWM",
            "datePublished": "2026-10-06T16:11:39.750Z",
            "url": "https://www.linkedin.com/posts/stripe_were-partnering-with-fedex-dataworks-together-activity-7513269756498636800-qruC",
            "activityUrn": "7513269756498636800",
            "reactions": 226,
            "comments": 33,
            "isRepost": false,
            "author": {
                "name": "Stripe",
                "url": "https://www.linkedin.com/company/stripe",
                "type": "company"
            },
            "media": {
                "type": "images",
                "imageUrls": [
                    "https://media.licdn.com/dms/image/v2/D5622AQF6al7GfmDptA/feedshare-shrink_800/B56aER_CILJwAc-/0/1791303099184?e=2147483647&v=beta&t=5dBhnomoe1UMoT57g3pf1LvW0de6uXQZ8JU2R_JUu8A"
                ],
                "imageCount": 1,
                "videoUrl": null,
                "url": null,
                "title": null
            },
            "quotedPost": null
        },
        {
            "text": "You can now hold, send, spend, and build with OUSD on Stripe.  \n\nRead the blog: https://lnkd.in/g9FBAn_g.",
            "datePublished": "2026-09-30T15:37:50.671Z",
            "url": "https://www.linkedin.com/posts/stripe_you-can-now-hold-send-spend-and-build-activity-7511086918730915840-q6Od",
            "activityUrn": "7511086918730915840",
            "reactions": 124,
            "comments": 10,
            "isRepost": false,
            "author": {
                "name": "Stripe",
                "url": "https://www.linkedin.com/company/stripe",
                "type": "company"
            },
            "media": null,
            "quotedPost": {
                "activityUrn": "7511077528879632384",
                "url": "https://www.linkedin.com/feed/update/urn:li:activity:7511077528879632384",
                "author": {
                    "name": "Open Standard",
                    "url": "https://www.linkedin.com/company/openstandard",
                    "type": "company"
                },
                "text": "$OUSD is live.\n\nBusinesses can build with OUSD via four integration paths Coinbase Mastercard Stripe and Visa.\n\nEach integration path offers APIs and tools that enable businesses and developers to build with OUSD across a range of critical services including settlement, payment orchestration, trading, FX, wallets, cards, and more.\n\nAll paths support mint and burn functionality at a 1:1 USD conversion rate, at no cost. \n\nRead the announcement: https://lnkd.in/g9FmQ_TY."
            }
        }
    ],
    "employeesPreview": [
        {
            "name": "Daniel Almenara",
            "slug": "danielalmenara",
            "profileUrl": "https://www.linkedin.com/in/danielalmenara"
        }
    ],
    "localeAlternates": [
        "en-IE",
        "en-US",
        "en-IN",
        "en-AU",
        "ja-JP",
        "en",
        "ja",
        "en-SG",
        "fr-FR",
        "en-GB",
        "nl",
        "fr",
        "pt-BR",
        "es-MX"
    ],
    "scrapedAt": "2026-10-12T15:05:12.967Z",
    "pageType": "company",
    "jobSearchUrl": "https://www.linkedin.com/jobs/stripe-jobs-worldwide?f_C=2135371"
}

An input that cannot be matched to a company gets a row like this instead. These rows are free.

{
    "input": "this-company-does-not-exist-xyz123",
    "companySlug": "this-company-does-not-exist-xyz123",
    "error": "not_found",
    "errorMessage": "Could not be matched to a single live LinkedIn company page. Check the spelling, or paste the company URL from LinkedIn.",
    "scrapedAt": "2026-09-26T10:17:26.819Z"
}

Use cases

• 🧲 B2B lead enrichment - append firmographics (size, industry, address, website) to your CRM at scale.
• 🔍 Account-based prospecting - pull the sample employees per company, with names, as a starting point for outreach.
• 🧩 Product mapping - products shows what a company sells on LinkedIn, grouped by category.
• 🗺️ Footprint mapping - use officeLocations to spot companies with multiple offices and target accounts by city or country.
• 📊 Market mapping - combine similarCompanies and localeAlternates to find adjacent vendors and regional presence.
• 📈 Hiring signal - openJobCount is the live number of US openings; spike-watch a competitor cohort.
• 📰 Brand monitoring - recent posts with engagement counts, authors and media types feed into content benchmarking.
• 🌐 Website to LinkedIn - turn a list of company websites into LinkedIn company pages and firmographics.
• 📚 CRM hygiene - re-scrape periodically to keep employee counts and addresses fresh.
• 🥇 Competitive tracking - headcount, hiring volume and posting activity for a whole peer set on one line item each.

Limitations

• Renamed companies are handled for you. When LinkedIn moves a company to a new URL (dell to delltechnologies, transferwise to wiseaccount), the right page is found. A slug with no LinkedIn page at all returns a free not_found row unless LinkedIn's name for a company is that exact text (openai-inc finds OpenAI; disney does not find "The Walt Disney Company"), so a typo never bills a similarly named business. Nothing is billed for an input that can't be identified.
• Posts cap at ~10. LinkedIn's anonymous feed only exposes around ten recent posts per company; there's no way to page deeper without a logged-in session. Reposts show only a relative time on the page, so their datePublished is null.
• Employees preview is up to four people. LinkedIn shows between one and four sample employees to logged-out visitors, and which ones changes from visit to visit; we return all it shows.
• Funding fields are empty. LinkedIn removed funding and Crunchbase data from public company pages. fundingRounds, lastFundingRound, fundingInvestors and crunchbaseUrl are kept in the output so existing integrations keep working, but they now return null. No scraper can read this from a public page any more.
• Open job count is for the United States. It is the exact number LinkedIn's job search shows for the company in the US. It is 0 when LinkedIn shows no US openings and null only when the count could not be read. jobSearchUrl opens LinkedIn's own worldwide job search for the company.
• Offices stop at 100. LinkedIn's public page lists at most 100 offices per company.
• Founded year is not always published. Big-tech megacaps often omit it. Fields come back as null when LinkedIn doesn't show them.
• Names are matched with LinkedIn's own company search. A name returns the company LinkedIn ranks first for it, checked against that company's LinkedIn ID. For a name with a legal ending such as Inc, plc, SE or AG, the company named exactly that is preferred over small pages named after the full text. For a rarely searched name, or one in a script LinkedIn search covers poorly, the top match can be a different business with a similar name. A name the search does not recognise at all returns a free row with error set to not_found, never a guess. Compare companyName with your input, or paste the LinkedIn URL when you need an exact company.
• Websites need a match on the company page. A website input returns the company whose LinkedIn page lists that site as its home page. Big brands that list a careers site or a short link instead (Google, Netflix, Amazon) come back as not_found; type the name or paste the LinkedIn URL for those.
• A slug is fetched exactly as written. Lowercase single words are treated as LinkedIn slugs. A few brands do not own their bare slug: twilio opens an unrelated page named "TWILIO". Type the name (Twilio) or paste the company URL instead.
• Public data only. Everything comes from the public company page. Tabs that need a login, such as /people/, are out of scope by design.

FAQ

How much does LinkedIn Company Scraper cost?

LinkedIn Company Scraper uses pay-per-result pricing. You pay $1.00 for 1,000 results, plus a $0.001 per-run start fee. The Apify Free plan gives you $5 in usage credits a month, and the $29/month Starter plan covers thousands of results.

No subscription lock-in. Pause whenever.

Is it legal to scrape LinkedIn?

Scraping public data is generally allowed in the US and most of the EU, as long as you don't collect personal data covered by GDPR or CCPA without a lawful basis. This actor only touches publicly accessible pages, but how you use the output is on you.

Apify's full breakdown: Is web scraping legal?.

Can I integrate LinkedIn Company Scraper with other tools?

Push results into Make, Zapier, Slack, Airbyte, GitHub, Google Sheets, Google Drive, and more. Apify treats every actor as a webhook source, so anything that consumes webhooks or pulls from an API works.

Full list: Apify integrations.

Can I use LinkedIn Company Scraper with the Apify API?

Yes. Every run is available via the Apify REST API:

curl -X POST "https://api.apify.com/v2/acts/sourabhbgp~linkedin-company-scraper/runs?token=APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"companies": ["google", "stripe"]}'

Docs: Apify API reference.

How do I scrape a large list quickly?

Put the whole list in one run instead of starting one run per company. Every run pays the $0.001 start fee and spends a few seconds starting up, so one run of 500 companies is cheaper and much faster than 500 runs of one. For lists of more than about 50 companies, set the run memory to 1 GB in the run options: the same list finishes about three times faster, and the start fee does not change. A list of more than about 1,500 companies at the default memory needs more than Apify's default one-hour run timeout, so raise the timeout in the run options or use 1 GB; otherwise the run stops near the hour and tells you how many were left.

If you start runs from code, wait for each run to finish before reading its dataset. A run with a single company usually takes 5 to 25 seconds including start-up, so allow at least 60 seconds before giving up on it.

Can I use LinkedIn Company Scraper through an MCP Server?

Yes. Apify ships an MCP server that exposes every actor as a tool, so Claude Desktop, Cursor, and any other MCP-capable client can call LinkedIn Company Scraper. Setup: Apify MCP docs.

Your feedback

Bug, missing field, or odd behavior? Drop a note in the Issues tab. Reports go to a human and fixes usually ship the same week.