Top LinkedIn Dataset Providers: Company & Profile Data

updated on Sep 22, 2025

LinkedIn datasets can be categorized into profile data and company data:

LinkedIn company data: This category encompasses essential business intelligence, including basic company information, detailed employee profiles, active job postings, emerging hiring trends, and engagement metrics.
LinkedIn profile data: Focusing on individuals, this involves public profile information, employment history, educational backgrounds, professional certifications, connection networks, and user profile activity.

This article compares Bright Data, Coresignal, and People Data Labs (PDL), focusing on their offerings, pricing models, and strengths. From one-time datasets to subscription APIs, we outline how each works and what to expect in cost, scale, and flexibility:

Features

Common fields across all providers:

The following fields are present in all three providers and were removed from the tables above for clarity:

Company fields: LinkedIn URL, Name, Website URL, Industry, Description, Employee count, Founded year, Logo URL, Headquarters, Company type, Funding info, Headline, Country code.

Pricing

The best LinkedIn dataset providers

Bright Data

Bright Data provides web data services, including proxies, web scraping APIs, and datasets across various categories, such as e-commerce, social media, real estate, and market research. For LinkedIn, they offer datasets on people profiles, company profiles, job listings, and posts.

With Bright Data’s “request a custom dataset” feature, users can order datasets tailored to their specific requirements. Pricing is based on compute costs (data extraction) and record costs (per record).

Pricing:

Free samples are available.
One-time purchase (Marketplace): Starting at $250 for 100K records.
Custom datasets: Typically start from $250 for one-time deliveries.
Subscription plans: With continuous updates, pricing starts around $1,200+.

Coresignal

Coresignal offers both current and historical data (up to 5 years back). Their products include multi-source company datasets, raw data (both real-time and historical), API integrations, and the Historical Headcount API, which provides employee count trends over time.

Their API-linked databases are refreshed regularly (near real-time updates for many datasets). Dataset pricing depends on factors like contract length, source coverage, and location.

Pricing:

Monthly plans: Start at $49/month, including 250 API credits.
Free trial: 14 days, with 200 API credits for testing.
Custom quotes: Required for larger or more complex datasets.

People Data Labs (PDL)

People Data Labs (PDL) is a B2B data provider specializing in datasets on individuals and companies. Their offerings include APIs like the Company Search API and Company Enrichment API, enabling seamless access and integration of data into applications.

The datasets include attributes such as year founded, industry, website domain, and LinkedIn URL. PDL also provides a free company dataset and limited-access APIs (100 credits) for users to explore their services at no cost.

Pricing:

Free trial: Includes a free company dataset and 100 API credits.
Pro Plan: Starting at $98/month, with ~350 enrichment credits.
Per-record (credit-based) pricing:
- Person Enrichment / Search: ~$0.28 per record → drops to ~$0.20 at scale.
- Company Enrichment / Search: ~$0.10 per record → down to ~$0.05 at scale.
- Other APIs (e.g., IP Enrichment, Person Identify) have different per-credit rates.
Enterprise plans: Custom pricing for high-volume usage.

What is a LinkedIn dataset?

LinkedIn dataset is a collection of structured data obtained from the LinkedIn platform. LinkedIn data refers to the user-generated information available on the LinkedIn platform, such as user profiles, company pages, industry trends, and events.

What data is included in the LinkedIn dataset?

A LinkedIn dataset includes different data points related to users, companies, and job postings. Here are major data points included in a LinkedIn dataset:

LinkedIn profile dataset: Work experience title, position, current company, education, connections, avatar, skills, and endorsements.
LinkedIn company dataset: Company page, industry size, # of followers, website, location, and employee counts.
LinkedIn job posting dataset: Job title, date posted, number of applicants, requirements, company, and location.

Types of LinkedIn Datasets

There are different types of LinkedIn datasets that can be categorized based on their sources and the methods used to obtain them. The choice of the LinkedIn dataset depends on the specific needs, use case, or budget of individuals and businesses. The main types of LinkedIn datasets include:

Public LinkedIn datasets: They can be accessed through LinkedIn’s public APIs and by web scrapers. LinkedIn APIs provide a more reliable way to access LinkedIn data. However, APIs have rate limits and restrictions that regulate the volume and frequency of data requests.
Web scraping allows users to collect specific data points tailored to their specific requirements. However, web scraping may violate LinkedIn’s terms of service and data privacy regulations (e.g., GDPR).
Proprietary LinkedIn datasets: Data is available through its premium products and services, such as Sales Navigator and LinkedIn Talent Insights. Proprietary datasets provide businesses and recruiters with exclusive access to certain data points that may not be available through public datasets.
Third-party LinkedIn datasets: Collections of data obtained from third-party sources and supplemented with LinkedIn information.

How to access LinkedIn datasets?

There are several methods to access LinkedIn datasets. Regardless of the method you select, it is essential to adhere to data privacy regulations and LinkedIn’s terms of service.

LinkedIn API: LinkedIn provides several APIs that allow developers to access and obtain data from the platform. Some of LinkedIn’s APIs include:
- LinkedIn Company API: Provides access to publicly accessible LinkedIn members’ profile data, such as name, headline, profile picture, and location.
- LinkedIn Profile API: Allows users to obtain company data, including company description, industry, and employee count.
Web Scraping: Web scraping techniques can be used to access and extract publicly accessible LinkedIn data. This method of obtaining LinkedIn data can be suitable for large-scale web scraping projects.
Third-Party Data Providers: LinkedIn data providers are companies or platforms that provide access to LinkedIn datasets through third-party datasets, APIs, or web scraping tools. Some companies specialize in providing tailored LinkedIn datasets for a specific use case or industry.
Data Enrichment Services: Integrate LinkedIn data with other data sources to provide a more comprehensive individual customer or prospect profile. These services can help sales and marketing teams better target potential customers.
Since data enrichment services typically focus on enriching individual data points, they may be limited to providing industry-level information. If you require broader insights that combine LinkedIn data with other sources, third-party data providers offer more extensive and targeted insights.

Applications of LinkedIn Datasets

Recruitment and talent sourcing

LinkedIn datasets can help recruiters identify talent and streamline their talent acquisition process. LinkedIn datasets provide a significant amount of data on professionals, including their skills, work experiences, and education.

This information can be used to conduct a targeted candidate search, tailor talent acquisition strategies, and identify areas for improvement in employer branding efforts.

Market research and competitor analysis

LinkedIn datasets might be useful for market research and competitive analysis. Organizations can use the professional data accessible on LinkedIn to:

Reveal industry trends like emerging technologies, popular job titles, and in-demand skills
Benchmark a company’s performance against its competitors
Identify potential partners or acquisition targets based on factors such as company size and industry.

Lead generation

LinkedIn has more than 930 million members with over 63 million registered companies (Figure 3). LinkedIn datasets enable sales professionals to target the right prospects and create tailored outreach strategies.

They can analyze a prospect’s professional background, including their connections and interests, enabling them to create personalized outreach messages. Sales professionals can also reveal upcoming events and conferences by utilizing LinkedIn data, allowing them to expand their network and identify new leads.

Figure 3: Showing the number of LinkedIn members from 200 countries and regions worldwide

Source: LinkedIn¹

Best practices for using LinkedIn data ethically and responsibly

Ensuring web scraping ethics when collecting and utilizing data is important to avoid legal and ethical concerns. For example, HiQ Labs, a data analytics company, scraped publicly available LinkedIn profile data for a professional skill analysis.

However, LinkedIn sued HiQ Labs in 2019, alleging that HiQ Labs violated the Computer Fraud and Abuse Act (CFAA) by accessing LinkedIn’s data without authorization. The Ninth Circuit determined that HiQ Labs’ actions did not violate the CFAA because the data was publicly accessible.

Follow LinkedIn’s terms of service: LinkedIn outlines data usage limitations, unapproved use cases, and access limitations in LinkedIn profiles in its terms of service. Adhere to LinkedIn’s terms of service to ensure that you are complying with their guidelines and policies.
Compliance with data protection laws: It is essential to adhere to regional and industry-specific regulations, such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States.
Secure storage: You can employ access restrictions to limit who can access your stored data or use secure storage infrastructure, such as firewalls and intrusion detection systems, to protect data from unauthorized access.
Anonymize data: There are numerous techniques for anonymizing data, including data masking, pseudonymization, and generalization. Data anonymization removes or alters personally identifiable information (PII) to assist organizations in protecting the privacy and adhering to data protection regulations.

Scraping LinkedIn data vs. utilizing LinkedIn datasets: which is better?

LinkedIn strictly forbids scraping in its terms of service, and doing so may result in account suspension or legal consequences. Using LinkedIn datasets is a safer choice if you prioritize legal compliance and require well-structured data.

Scraping data directly from LinkedIn may provide more up-to-date information. However, web scraping may require technical expertise to set up and maintain web scrapers, and it can be time-consuming and resource-intensive compared to pre-built datasets.

On the other hand, pre-built datasets save time and resources, and using a dataset from a reputable data provider may eliminate legal and ethical concerns. However, pre-built datasets may not be tailored to specific requirements. It is crucial to select a data provider that allows users to modify the data to fit their unique needs.