People Data Labs shows up behind a surprising number of B2B products. Recruiting platforms, sales tools and fraud checks often run on its data without ever mentioning the name. Yet teams evaluating PDL for the first time usually have the same questions: where does the data come from, which API should I call, and what will a thousand lookups actually cost?
This guide answers those in order. It covers PDL's sources, each of its main APIs, how credits get charged (which differs by endpoint more than most people expect), what the data looks like in practice, and where PDL fits next to other providers.
What Is People Data Labs?
People Data Labs (PDL) is a data provider that sells person and company data through APIs and bulk licensed datasets. It was founded in 2015 by Sean Thorne and Henry Nevue and raised a $45M Series B led by Craft Ventures in 2021.
The size claims vary depending on which page you read. PDL's homepage describes a dataset of 1.5 billion unique person profiles, its person data stats page shows a larger total of roughly 2.47 billion person records, and its company data page cites more than 70 million company profiles. Treat all of these as a sense of scale. What matters for you is the match rate on your own list, which you can only learn by testing.
The basic model is simple. You send PDL whatever you know about a person or company, and it returns the matching profile with every field it has. An email becomes a work history, a title and a LinkedIn URL. A domain becomes headcount, industry, location and funding details.
PDL is built for engineers and data teams, not for sales reps clicking through a search UI. Its customers are often building products of their own (applicant tracking systems, sales intelligence tools, identity checks) and use PDL as the data layer underneath. You can see that orientation in the docs, the SDKs and the pricing.
Where PDL Gets Its Data
PDL's data sources documentation splits its inputs into two groups.
Proprietary sources. These are companies that share data with PDL and warrant that it's compliant with privacy regulations. PDL has historically marketed this data-sharing arrangement as its "Data Union." Contributors come from HR tech, real estate tech, identity and anti-fraud, martech and other verticals. Some provide a one-time file, others are refreshed with each data build.
Public sources. Open datasets, publicly available data and government public records.
Raw data from all of these goes through a build process before it reaches the API. Records that refer to the same person across sources get merged, so "Jon Smith at Acme" from one feed and "Jonathan Smith, acme.com" from another end up as one profile. Fields get standardized: job titles, company names, schools and locations are mapped to canonical values, which is why "VP of Sales" and "Vice President, Sales" come back in a consistent form. PDL also says it checks cardinality between fields and hand-reviews samples of each source before adding it.
The result is cleaner than any single source, but it is still a compiled database. A person who changed jobs last month may not show the new role yet. On update cadence, PDL's data updates page says data updates roll out monthly through the APIs, with major product changes on a quarterly schedule.
PDL's Core APIs
Picking the right endpoint affects both cost and result quality, so it's worth knowing what each one is for.
Person Enrichment API
This is the one most teams start with. You pass identifiers for one person and get back the single best-matching profile.
Inputs can be an email, phone number, LinkedIn or other social URL, name plus company, name plus location, or combinations. More identifiers mean a better chance of a match and a more confident one.
Every response includes a likelihood score from 1 to 10. Two parameters let you control what counts as a match:
min_likelihoodsets the minimum confidence for a 200 response. Raise it when a wrong match is expensive (say, you're auto-updating CRM records). Lower it when coverage matters more than precision.requiredlists fields the profile must contain to count as a match. If you only care about records with a work email, say so, and profiles without one return a 404 instead of consuming a credit.
A match returns HTTP 200 with the profile. No match returns 404. The Person Enrichment reference states that PDL charges per match. For larger jobs there is a bulk version that accepts many records per request.
Company Enrichment API
Same idea for organizations. Look up a company by domain, name, LinkedIn URL or ticker and get the profile back: industry, size range, founded year, headquarters and other locations, social profiles, funding data where available, and related entities. Domain is usually the most reliable key. Name-only lookups can collide when several companies share a name. Like person enrichment, it's charged per match.
Person Search and Company Search APIs
Enrichment assumes you already know who you're looking for. Search is for finding records that match criteria, such as every VP of Marketing at US software companies with 100 to 500 employees.
You write the query in either Elasticsearch syntax or SQL (SELECT * FROM person WHERE ...). Each request returns up to 100 records, with a scroll token for pagination.
The billing detail to note: search is charged per record returned. A query that returns 100 profiles costs 100 credits. The API also returns a 200 for any valid query even if nothing matches, so check the total field rather than the status code. On repeat searches you can exclude PDL IDs you've already pulled (the docs cap this at about 1,000 IDs per query) so you don't pay twice for the same person.
Person Identify API
Identify is for messy inputs. Instead of one best match, it returns up to 20 candidate profiles ranked by match score. It's useful for entity resolution across dirty datasets, or when you want a human (or your own logic) to pick the right person.
Its billing is different from the other endpoints. The Identify reference says each call uses one credit regardless of how many profiles come back, and it charges on both 200 and 404 responses. So a call that finds nothing still costs a credit here, unlike enrichment.
IP Enrichment API
Maps an IP address to the associated company and location metadata. PDL describes matching against a pool of nearly 2 billion IPs. Typical uses are identifying which companies visit your website and adding a business-identity signal to fraud checks. It's charged per match.
Supporting APIs
A few utility endpoints help with input quality and search UX:
Cleaner APIs normalize company names, schools and locations before you enrich, which can raise match rates on messy CRM exports. PDL's docs list a free allowance of 10,000 calls a month, rate-limited.
Autocomplete API suggests values for fields like company, industry, country and title class, handy if you're building a search interface on top of PDL.
Job Title Enrichment and Job Posting Search extend the dataset beyond person and company profiles. PDL says its job posting data comes only from company career pages.
How PDL Pricing Works
PDL uses credits, and the cost of a lookup depends on the endpoint. Here is a summary of the billing rules above:
API | What consumes a credit |
|---|---|
Person Enrichment | One credit per match (404s are free) |
Company Enrichment | One credit per match |
IP Enrichment | One credit per match |
Person / Company Search | One credit per record returned |
Person Identify | One credit per call, match or not |
Cleaner APIs | Free up to a monthly limit |
As of September 2026, PDL's person data pricing page lists three tiers:
Testing (free): up to 100 credits a month. Contact data is not included, so emails, phones and addresses come back obfuscated. You can confirm a record exists and inspect non-contact fields, which is enough to measure match rate but not to run outreach.
Self-Serve: starts at $98 a month (or $940 a year) with 350 person credits a month, contact data included, premium fields as an add-on, and CSV enrichment.
Enterprise: custom volume and pricing, plus bulk data licensing, a customer success manager and SSO.
Company data is priced separately; its company pricing page lists a self-serve tier starting at $100 a month. Pricing pages change, so check both before you budget.
Estimating what you'll spend
A worked example makes the endpoint differences concrete. Say you want to enrich 2,000 CRM contacts that have a work email and then find five more decision-makers at each of 200 target accounts.
Enriching the 2,000 contacts: if 1,300 match, you pay 1,300 credits. The 700 misses cost nothing.
Searching the 200 accounts at five people each: that's up to 1,000 credits, one per returned record, whether or not you end up using those people.
The numbers are illustrative, but the shape isn't. Enrichment spend scales with your match rate. Search spend scales with how loosely you write the query. Tight filters (and a size that matches what you actually need) are the main cost control on search.
Match rate is the variable you can't read off a pricing page. It depends on your inputs (a LinkedIn URL matches far more reliably than a first name and a company) and on who you're enriching. Run a representative sample through the free tier before you pick a plan.
What Data PDL Returns
PDL publishes its full field list in the docs. What you'll see in practice falls into rough tiers.
Usually present on person profiles: full name, current employer and title, a standardized seniority and job function, location at city or country level, and a LinkedIn URL when one exists in PDL's sources.
Often present: work and personal emails, employment history with dates, education, and skills inferred from job history.
More variable: phone numbers, and mobile numbers in particular. This is true of most providers, since compliant phone data is hard to source.
On company profiles: name, domain, industry, size range and headquarters are well covered for established companies. Funding data is solid for venture-backed firms and thin for bootstrapped ones. Treat revenue figures as estimates, as you would from any provider.
Coverage is generally strongest for professionals in North America and Europe and weaker in markets and industries with less digital footprint. Emails returned by PDL are not a deliverability guarantee. Run them through an email verifier before you send.
Common Use Cases
Enriching CRM and lead records
You have contacts with an email address and want titles, companies and seniority for routing and segmentation. Person enrichment with a sensible min_likelihood covers this well, since job title, employer and company size are among PDL's best-covered fields.
Building data into a product
An ATS auto-filling candidate profiles, a sales tool showing company context, a marketplace verifying sellers. Here PDL is infrastructure inside your product, called by your backend and often licensed in bulk.
Market and workforce research
Search queries across the whole dataset let you count people by title, industry or location, or follow how headcount in a function shifts between companies. Individual records have gaps, but the aggregate picture is useful.
Identity checks and fraud prevention
Does the person signing up really work at the company they claim? Does the IP belong to a business? PDL's person and IP data support both checks.
Where PDL Is Strong, and Where It Isn't
PDL does a few things very well. The dataset is large, the schema is consistent and well documented, and the matching controls (min_likelihood, required) give you real say over the precision versus coverage trade-off. Charging per match on enrichment is friendly to teams working with cold or messy lists. The documented sourcing and privacy-request handling help if compliance reviews are part of your buying process.
The limits are the ones you'd expect from a compiled database:
Freshness. Records reflect the latest build. For someone who changed roles recently, a live source may be ahead.
Contact data coverage. No single provider finds emails and mobile numbers for everyone. PDL is strong on profiles and good on emails, with phones varying more.
Engineering effort. Direct use means working with an API and writing queries. There's no prospecting UI for reps.
Billing differences by endpoint. Search and Identify don't follow the pay-per-match rule, which catches teams who assume they do.
If you're choosing between PDL and all-in-one sales platforms such as Apollo or ZoomInfo, the question is mostly about workflow. Those tools bundle a database with a prospecting interface and, in Apollo's case, sequencing. PDL gives you raw data and leaves the workflow to you. For a side-by-side of developer-focused options, see our technical guide to data enrichment APIs.
Using PDL With Other Providers
For contact data in particular, the best results usually come from asking more than one provider. That's the idea behind waterfall enrichment: query providers in a set order and stop at the first valid result, so a record PDL can't match still has a chance with the next source.
People Data Labs is available as a native integration in Databar, so you can run it without writing API code or holding a separate PDL subscription. In practice that means:
Adding PDL person or company enrichment as a column in a Databar table and running it on your list.
Putting PDL in a waterfall with other providers for fields like work email, so misses fall through to the next source.
Using your own provider API keys where you already have a contract, since Databar supports bring-your-own keys.
Calling the same enrichments from Databar's REST API, Python SDK or MCP server if you'd rather stay in code.
Databar has 160+ data providers, charges for results rather than empty lookups, and paid plans start at $99 a month. If you want to see how PDL performs on your own data next to other sources, start a 14-day trial.
Getting Started With PDL
Test match rate on real data. Pull a few hundred records that look like your actual use case, not your cleanest ones, and run them through the free tier. The obfuscated contact fields still tell you whether a record exists.
Start with enrichment before search. It's cheaper to reason about and shows you data quality on people you already know.
Write down the fields you need. Then use
requiredso you only pay for profiles that contain them.Scope search queries tightly. Every returned record costs a credit, so filter hard and paginate only as far as you need.
Plan a second source for contact data. If emails or mobiles are the goal, assume some records will need another provider.
FAQ
Is People Data Labs free?
There's a free Testing plan with up to 100 credits a month. Contact fields (email, phone, address) are obfuscated on it, so it's for evaluation rather than outreach. Paid self-serve person data starts at $98 a month according to PDL's pricing page.
Does PDL charge when no match is found?
For Person, Company and IP Enrichment, no: a 404 doesn't use a credit. Search charges per record returned, and Identify charges one credit per call whether or not it finds anyone.
Where does People Data Labs get its data?
From proprietary sources (companies in HR tech, real estate tech, identity and anti-fraud, martech and other verticals that share data with PDL and warrant it's compliant) plus public sources like open datasets and government records.
How often is PDL data updated?
PDL's docs say data updates roll out monthly through its APIs, with major product changes on a quarterly cycle. Customers licensing flat files can get monthly or quarterly deliveries.
How accurate is People Data Labs?
Core profile fields (name, employer, title) are generally reliable, and the likelihood score tells you how confident each match is. Contact data accuracy varies by region and population, so verify emails before sending and test on your own records before committing.
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