Coresignal vs People Data Labs: B2B Data Providers Compared (2026)

Coresignal charges 1 credit for a job posting and 10 to 20 credits for a single company record, per its pricing page on 27 July 2026. People Data Labs publishes no readable price at all. That gap in disclosure is a decent preview of how differently the two vendors behave, and both of them land on the same shortlist: the one a team writes once its own crawlers have started costing more in engineering time, proxy contracts and on-call hours than a licence would.

Coresignal and People Data Labs both sell firmographic and person records, both offer an API alongside a bulk feed, and both refresh continuously from the public web rather than selling a static snapshot. The vendor lists that rank B2B data providers review them separately and rarely put them side by side, which is a shame, because the choice turns on delivery model rather than on quality. How your team consumes the data decides which one fits.

All observations below are dated to 27 July 2026 and sourced to the page they came from. Where a vendor does not publish something, this comparison says so rather than filling the gap.

What each one actually sells

Coresignal, founded in 2016 and headquartered in New York, sells structured company, employee, job-posting and social-post data collected from multiple public web sources. Access comes through REST APIs for company, employee and jobs data, a natural-language Agentic Search API, bulk flat-file exports, an MCP server for agent integration, and a Snowflake Marketplace listing with Databricks, AWS, GCP and Azure integrations. The record counts the company publishes vary across its own materials: company records in the 70 million to 106 million range with 500-plus fields, employee profiles from 700 million to 895 million with 300-plus fields, and job postings from 413 million to 468 million with 85-plus fields. Its site footer on 27 July 2026 claimed 4.5 billion company, employee and jobs records in total. That spread is worth noting before you build a plan around any single figure.

People Data Labs, founded in 2015 and headquartered in San Francisco, sells Person Enrichment, Company Enrichment, Person Identify, IP Enrichment and Job Posting Search APIs, plus bulk data licence feeds and warehouse integrations. Its documentation on 27 July 2026 described the product as data on over one billion people globally, and its dataset index listed eight named datasets with exact profile counts: an All Dataset at 2,466,550,602 profiles, a Resume Dataset at 814,855,745, a Phone Dataset at 779,988,054, a Consumer Social Dataset at 703,116,100, an Email Dataset at 612,504,502, a Mobile Phone Dataset at 478,994,460, a Street Address Dataset at 216,925,007 and a Developer Dataset at 4,085,892. Publishing counts to the individual record is unusual and mildly reassuring, though a precise number is still a total, not a coverage rate.

The structural difference shows up in that list. Coresignal organises its catalogue by entity type: companies, employees, jobs. People Data Labs organises a large part of its catalogue by which contact attribute is present. If your job is to find every company matching a filter, the first shape is more natural. If your job is to attach a phone number or an email to a person you already have, the second is.

Head to head

Everything in that table came from the vendor's own site or docs on 27 July 2026, except the Coresignal record ranges, which come from a compilation of the company's published figures rather than one authoritative page.

Refresh cadence, and the delivery gap behind it

Coresignal is unusually direct here. Its FAQ, read on 27 July 2026, separates how often data is refreshed from how often it is delivered. On refresh: most datasets are fully refreshed daily, weekly or monthly depending on available scraping resources, and the database behind the APIs is refreshed in real time. On delivery: most data ships monthly, with daily and quarterly deliveries available for specific datasets.

That distinction matters more than it sounds. A dataset can be refreshed daily and still reach you a month old if your contract is a monthly drop. For headcount trends in a warehouse, fine. For alerting on job postings or a funding signal, a monthly drop is a month of latency that no query optimisation recovers.

People Data Labs does not publish a refresh interval on the documentation pages we could read on 27 July 2026. Its data licence documentation describes delta files containing modified records, which implies incremental releases rather than full monthly snapshots, but the frequency is not stated publicly and should be asked for in writing. That is a gap in what a buyer can verify before a sales call, and it is the sort of thing our guide to evaluating web data providers exists to force into the open.

Whichever you pick, do not accept a stated interval as a test result. Take records you know changed in the last 30 days, in the fields you care about, and measure how long the change took to appear. Refresh intervals are averages across a catalogue. Staleness is per field, and the stalest fields are usually the ones you bought the data for.

Delivery is the underrated engineering decision

An enrichment API and a bulk drop produce different systems, not two routes to the same one, and this is where an engineering team should spend most of its evaluation time. An API means per-call latency, rate limits, retry logic, a credit budget that maps to request volume, and a cache you will end up building whether you planned to or not. A bulk file means an ingestion job, a schema you have to version, storage that grows monotonically, and a diffing step to work out what changed. The failure modes have nothing in common. An API degrades by rate-limiting you at the worst moment. A bulk feed degrades silently, delivering fewer rows than last month while every query still returns results.

Coresignal supports both ends. Bulk exports arrive as JSON, JSONL, CSV or Parquet, per its FAQ on 27 July 2026, and Parquet is the format that signals a vendor expects a warehouse rather than a spreadsheet. The Snowflake Marketplace listing removes the ingestion job entirely for teams already on Snowflake. Its APIs return JSON or CSV. The MCP server is a newer addition aimed at agent workflows.

People Data Labs leans the other way. Its documentation frames the product as two consumption methods: enriching profiles you already have via the API, or licensing a dataset subset. The enrichment framing is the giveaway. The natural PDL workflow is that you have a person or a company and you want more about them. The data licence route delivers to S3, GCP, Azure and more, with delta files for modified records, which is warehouse-friendly. The MIT-licensed SDKs across Python, JavaScript and TypeScript, Go and Ruby cut real integration time if you are wiring enrichment into an application rather than a pipeline.

A practical rule: if the consumer of this data is a query, take the bulk route. If the consumer is a user action or a webhook, take the API. Teams that get this wrong end up paying API rates for what is effectively a batch job, which is the same mistake covered in our guide to scraping API credit pricing.

Coverage, and why the record counts do not settle it

Both vendors publish big numbers. Neither publishes a per-region breakdown we could read on 27 July 2026, which means any claim about who is stronger in EMEA or APAC is unverified until you test it yourself.

What can be said from primary sources: People Data Labs publishes exact per-dataset counts, which at least tells you the shape of its catalogue. The All Dataset at 2.47 billion profiles is described in its docs as containing unmerged records, so it is not 2.47 billion distinct people. The named attribute datasets are more informative for planning: 612 million profiles in the Email Dataset and 479 million in the Mobile Phone Dataset are the numbers that matter if contactability is the point of the purchase.

Coresignal's counts are ranges, and they move between the company's own materials. The company also discloses no funding and has limited independent coverage, so scale claims rest on the vendor's word. That is not disqualifying, and plenty of profitable data businesses never raise, but it means a coverage audit does more work here than it would with a vendor whose numbers are externally corroborated.

The answer for both vendors is the same, and it is dull work: run the audit yourself. Draw 300 to 500 records from your own population, in the regions and segments you actually query, hand them over as match keys, and score two things. Match rate is the share of your keys that returned any record. Field completeness is the share of matched records where each required field is populated. A vendor-supplied sample is worthless here, because it was selected to look good. The data providers category lists the rest of the field if these two do not cover your slice.

Pricing shape

Coresignal publishes list pricing, which is rarer than it should be in this category. On 27 July 2026 its pricing page listed seven Database API tiers: Mini at $49 per month for 2,500 credits, Starter at $199 for 12,000, Pro at $499 for 35,000, Growth at $1,000 for 150,000, Premium at $1,500 for 1,000,000, Scale at $3,000 for 4,000,000 and Elite at $5,000 for 10,000,000 credits, with a 10 percent discount on annual plans. Credit costs on the same page: 10 to 20 credits for an employee record, 10 to 20 for a company record, 1 credit for a job posting and 10 for contact enrichment. Datasets are quoted separately, starting from $1,000, with the final number depending on contract duration, processing level and record count.

Two things follow. The credit-per-record cost falls sharply as tiers rise: Mini buys roughly 125 to 250 company records a month, Elite buys 500,000 to a million. And job postings at 1 credit are an order of magnitude cheaper than person or company records, which makes Coresignal notably economical if job data is your primary interest. Verify against a current quote, since published pricing in this market changes without announcement.

People Data Labs' public pricing page did not render readable content when we fetched it on 27 July 2026, so this comparison cannot quote its tiers. What can be said is that the model is credit-based for API enrichment and quoted for data licensing, and that its documentation directs licensing enquiries to a data consultant rather than a self-serve checkout. Treat the pricing comparison as open until you have both quotes in front of you.

What each one is bad at

Coresignal's weakness is verifiability. Its published record counts appear as ranges that differ between pages, its free-tier terms differ between its pricing page and its FAQ, and its terms of service were not reachable at the address we tried on 27 July 2026. Independent coverage is thin and the company discloses no funding, so there is little external corroboration for any scale claim. None of that means the data is bad. It means you carry more of the verification burden yourself, and should budget a proper audit rather than a demo.

Coresignal is also delivery-first by its own description: most data ships monthly. If you need a record within hours of it changing, you are relying on the real-time API database rather than the dataset product, and should price the workload as API calls.

People Data Labs' weakness is disclosure of cadence and cost. Neither the refresh interval nor a readable price list was available to us on 27 July 2026, which pushes more of the evaluation behind a sales conversation. Its catalogue is also person-centric. If you want deep firmographics with hundreds of fields per company rather than person records with a company attached, that is not the shape the product is built around, and the free company dataset's nine fields illustrate the emphasis.

There is also a history worth weighing in any data-handling review. In 2019 a misconfigured Elasticsearch server belonging to a customer exposed roughly 622 million records sourced from People Data Labs, an incident widely reported at the time. PDL's own systems were not reported as breached and the exposure sat on a third party's infrastructure, but the episode is a reminder that person data acquires obligations the moment it lands in your environment. Our post on data broker rules and people-data vendors covers the compliance surface that comes with it.

Licensing, which decides whether any of the above matters

Neither vendor answers the downstream question on a public page in enough detail to sign against. People Data Labs' data licence documentation, read on 27 July 2026, says customers may use licensed data as long as the use complies with its Acceptable Data Use Policy, without restating the restrictions on that page. Coresignal's terms of service returned a 404 at the address we tried the same day.

So ask, in writing, before you argue about price:

• Redistribution. Can you expose records, or fields derived from them, to your own customers? A sales-intelligence product built on a licence that forbids redistribution is a product you cannot ship.
• Derived data. If you train a model or publish an aggregate, what survives termination? This is the clause AI teams discover too late.
• Deletion and takedown. What must you remove, how quickly, and who builds the pipeline that does it?
• Subject access. Who answers a data subject, and what must you prove about provenance?

One genuine bright spot on the PDL side: the free company dataset of over 22 million companies is released under CC BY 4.0, which its documentation states may be used for any purpose. That is an unusually clean licence for this market and a useful firmographic baseline even if you never become a customer. It is not the paid dataset, being restricted to companies with at least one current associated employee in PDL's data, but the terms are unambiguous, which is more than most of the category manages.

Where these sit against the wider market

Neither is your only option. Datarade is a brokerage layer rather than a producer, useful for scoping which providers exist before you narrow to two. Bright Data Datasets sells prebuilt and custom datasets across overlapping business categories and is the natural third quote if you want a proxy-network operator's dataset arm in the mix; the same vendor's infrastructure side shows up in our Bright Data vs Oxylabs comparison.

Buying is also not the only answer. If your schema is genuinely unusual, running the collection yourself on a platform like Apify or your own stack can beat a licence that almost fits. That is a different evaluation, covered in how to choose a web scraping tool and, at volume, in large-scale crawl architecture. Be honest about what you take on: collecting person and company data at scale means fighting the anti-bot systems described in why scrapers get blocked, and the maintenance never ends. Our 2026 web scraping stack post sketches what that build looks like.

If you do buy, keep validating after the contract starts. Field-level completeness drifts between releases, and the checks in validating AI-extracted data transfer cleanly to licensed feeds. Treat any vendor coverage figure the way our guide to reading scraping benchmarks treats a vendor benchmark: as a claim to reproduce, not a result to accept.

Pick Coresignal when, pick People Data Labs when

Pick Coresignal when the output of this purchase is analysis. You want deep company, employee and job-posting records with hundreds of fields, landed in a warehouse as Parquet or pulled from a Snowflake listing, and you can live with a delivery cadence measured in weeks for most of it. Job data at 1 credit per posting is the strongest specific reason to start here, and published list pricing lets you model cost before talking to anyone. Accept that you will do more of the verification yourself.

Pick People Data Labs when the output is an action inside a product. A user submits an email and you enrich it. A signup fires and you attach firmographics before the record reaches your CRM. The API-first framing, the four MIT-licensed SDKs and the per-attribute dataset structure all point the same way, and the delta-file licence route is there when you later want the same data in the warehouse. Accept that you will need a sales conversation to learn the cadence and the price.

Who each one is wrong for

Coresignal is wrong for a team that needs a record enriched in the request path, in milliseconds, with a support contract guaranteeing it. It is also wrong for a compliance-conservative buyer who needs published terms and externally corroborated scale figures before signing, because on 27 July 2026 neither was readable in public.

People Data Labs is wrong for a team whose unit of analysis is the company rather than the person, and which needs several hundred firmographic fields per record for research or investment work. It is also wrong for a buyer who needs to model total cost from a public price list before opening a sales conversation.

Both are wrong for the team whose real problem is a schema nobody sells. If the fields you need are in neither catalogue, no coverage negotiation fixes that, and you are better off building the collection and owning the maintenance. Licensing beats collecting only when what is licensed is what you actually needed.