People Data Labs›People Data Labs› offers Person Data

On its Person Data page, People Data Labs stakes its advantage on matching economics: customers pay per successful match, define match strictness and required fields, and use a bulk endpoint with industry-leading latency for scale, under compliance practices certified as ISO and SOC 2 Type 2. Depth is the second lever, with comparisons describing more than one billion person profiles and tens of millions of company records, monthly production releases with record-level timestamps, and CRM-agnostic REST access.

The September 2026 v35.2 release notes evidence an active quality flywheel: a large reduction in duplicate LinkedIn profiles through removal of tens of millions of sparse records, validation of GitHub URLs down to verified links, and inferred-salary coverage growth of more than twenty percent in one month from compensation signals in the job posting dataset. Reviewers note the counterweights as well: no self-serve prospecting interface, monthly snapshot cadence rather than live request-time enrichment, and credit-based costs that climb with volume.

People Data Labs first-party release telemetry shows an expanding, self-cleaning dataset: in the v35.1 to v35.2 month it refreshed tens of millions of resume experiences and locations, detected millions of global job changes, lifted inferred-salary coverage by more than twenty percent to hundreds of millions of records, grew GitHub-LinkedIn linkages and expanded the Job Posting Dataset by double digits to tens of millions of records across more than one hundred thousand companies, while deliberately shrinking the Full Dataset through precision-first deduplication.

The surrounding market consolidates around distinct models: 2026 buyer guides benchmark enrichment providers on price per successful match, with People Data Labs cited near twenty to thirty cents per record, while several 2026 reviews frame the next contest as real-time enrichment for AI sales agents. The outlook is dual-edged: scale, a monthly freshness engine, and the September 2026 Job Posting Sandbox API fit the developer-driven segment, while live-crawl challengers and per-seat suites court the workflow-driven buyer.

Person Data is priced on a credit-based per-record model with volume tiers: a Free plan at no cost with up to one hundred records per month, obfuscated contact data, and no commitment for testing; a Pro self-serve plan starting near one hundred dollars per month for a few hundred credits with contact data, premium fields, CSV enrichment, and email support; and custom-priced Enterprise contracts with annual terms, formal data testing, flexible payment, field bundles, Data License Feeds, a designated success team, and single sign-on.

The strategy monetizes volume growth with discounts that trigger as usage grows and pushes annual commitments with a stated twenty percent saving and upfront data access, while premium field bundles such as the Inferred Salary bundle added in v35.2 create expansion revenue on top of base credits. Effective Pro pricing is near thirty cents per successful enrichment, against per-seat enterprise suites in the tens of thousands per year, while Enterprise remains usage-based and quoted.

Person Data packages workforce profiles spanning work history, education, skills, certifications, inferred salary ranges, and social profiles, built on People Data Labs person datasets and exposed through integration APIs. The Person Enrichment API accepts inputs such as name, location, email, phone, education, work, or social profiles, matches them against the Person Dataset with proprietary matching logic and returns the profile of the best matching record; callers set match strictness and required fields, and a bulk endpoint with industry-leading latency targets high-volume production use.

Delivery is API-first: beyond Enrichment, Search, Identify, and IP Enrichment APIs with supporting Autocomplete, Cleaner, and Job Title Enrichment APIs, customers license data feeds delivered through AWS, GCP, or Snowflake, access data natively in Snowflake and Databricks through Delta Share, and connect PDL into no-code workflows through Zapier and Make integrations. The offering serves engineering, data science, and product teams building enrichment pipelines, recruiting platforms, investment tooling, and machine learning models rather than sales users seeking a prospecting dashboard.