People Data Labs vs NinjaPear: Updated Notes on LinkedIn Data, Proxycurl, and What Still Matters
US employers still struggle to find the right workers fast enough, and the old playbook of posting jobs and waiting is not enough. If you are evaluating People Data Labs for recruiting, enrichment, or LinkedIn-adjacent workflows, the short version is this: PDL is still useful when you want a large pre-collected database, but the tradeoff is freshness, and that tradeoff matters more than most buyers realize.
The hardest part is not finding a database. It is figuring out whether the data is fresh enough to act on.
LinkedIn contains a ridiculous amount of useful public professional data: jobs, company information, work histories, and the context around who works where. The practical question is not whether that data is valuable. It is how you get it into your system in a way that is structured, current, and economically sane.
What is LinkedIn data scraping?
The fastest way to extract large amounts of public LinkedIn data is through scraping. A scraper can collect information from profile pages, company pages, and job pages, then turn that raw mess into structured records you can actually use.
Anyone with decent programming skills can build a DIY scraper for LinkedIn. The problem is that the work stops being fun very quickly. You are dealing with anti-bot systems, changing page structures, identity resolution, retries, infrastructure cost, and all the boring operational nonsense that shows up after the first demo works.
Unless you are a data team yourself, doing LinkedIn data scraping on your own is usually more expensive than it looks. You are not just paying for code. You are paying for maintenance, failure handling, and the opportunity cost of your engineers working on this instead of your product.
If you do not want to build the tooling in-house, you need a data partner. And this is where the differences matter: some vendors return live or near-live results, some mostly sell pre-collected datasets, and some give you just enough data to get into trouble but not enough to build a reliable workflow.
Two of the more relevant names here are the legacy Proxycurl product and People Data Labs. Today, there is also NinjaPear, which is where the founder behind Proxycurl is now building.
Update: Proxycurl API has been sunset. I am retaining the original comparison context here because it explains the tradeoffs well, but the founder behind Proxycurl is now working on NinjaPear, which takes a different approach and does not scrape LinkedIn. NinjaPear focuses on public-web B2B intelligence, enrichment, company monitoring, customer intelligence, and person/company profile data from public sources.
Proxycurl
Proxycurl was a LinkedIn scraping API designed for developers. It offered a LinkedIn Person Lookup Endpoint, Person Profile Endpoint, Company Profile Endpoint, and Job Profile Endpoint using LinkedIn URLs. The LinkDB PostgreSQL database also provided pre-crawled LinkedIn person profiles in the US and Singapore and LinkedIn company profiles globally.
Pros: The API crawls were dispatched on-demand and made in real time for the latest data. Proxycurl also crawled at scale, scraping about a million pages per day. In addition, it could crawl popular websites like LinkedIn by bypassing CAPTCHAs and bot detection to parse raw data and deliver structured data.
Cons: There was limited functionality for running discovery searches and extracting profiles using search criteria.
That original distinction still matters because it highlights the core fork in the road: do you want fresh retrieval from the source, or do you want instant access to a giant pre-built database?
People Data Labs
People Data Labs offers a pre-collected data solution with datasets on people and companies. PDL features include an Enrichment API and Search API to generate matching profiles or filter pools of profiles to fit specific search criteria. Their Data Licensing product also gives clients annual access to datasets, while advanced functions are typically usage-based.
Pros: PDL offers large people datasets, so it is a comprehensive option if your priority is breadth. Its plug-and-play model is also straightforward. You can get up and running quickly if your workflow depends more on scale and filtering than on real-time freshness.
Cons: PDL can get expensive fast, especially when you are paying per match and still need to validate or refresh records downstream. The larger issue is that pre-collected databases are, by definition, a race against staleness. If your use case is sales timing, hiring, or trigger-based outreach, stale data quietly kills ROI.
The original version of this article cited pricing of $0.25 per match. I am leaving that reference here as historical context from the imported draft, but if pricing is a buying criterion for you, check PDL directly before making a decision.
Where NinjaPear fits
This is the part that needed updating.
Proxycurl is no more. The founder behind it is now building NinjaPear, and the product direction is different in a meaningful way. NinjaPear is not trying to be a straight LinkedIn scraper replacement. It is a public-web B2B intelligence platform that includes person and company enrichment, customer intelligence, competitor data, company monitoring, and work email finding.
If you are comparing People Data Labs to NinjaPear, the difference is less about “who has more rows in a database?” and more about what kind of workflow you are running.
Use People Data Labs if you want:
- a large pre-collected people dataset
- search and filtering across a broad static corpus
- licensed data access for internal modeling or bulk enrichment jobs
Use NinjaPear if you want:
- public-web B2B intelligence beyond a resume-style profile
- company monitoring across blogs, websites, and X via the Monitor API
- customer and competitor intelligence that standard people databases do not expose
- person and company enrichment from public sources without relying on LinkedIn scraping
That distinction is not academic. It changes how your GTM stack behaves.
PDL is closer to a structured dataset vendor. NinjaPear is closer to an operating system for live B2B intelligence.
Comparison table
If you are comparing 3 options, you should force yourself to score them on the dimensions that actually affect execution.
| Tool | Data freshness | Pricing clarity | Ease of use | API flexibility | Best fit | Avg. score |
|---|---|---|---|---|---|---|
| Proxycurl (legacy) | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐⭐ | Live LinkedIn retrieval | 4.50/5 |
| People Data Labs | ⭐⭐⭐☆☆ | ⭐⭐⭐☆☆ | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | Large pre-collected dataset | 3.50/5 |
| NinjaPear | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | ⭐⭐⭐⭐☆ | Public-web B2B intelligence | 4.00/5 |
This is obviously a directional table, not a lab-certified benchmark. But it reflects the real buying tradeoff better than feature-grid theater.
The verdict
The original verdict said Proxycurl. That made sense in the context of live LinkedIn data retrieval.
The updated verdict is more specific:
- If you want a large pre-collected people database, People Data Labs is still a serious option.
- If you wanted what Proxycurl used to do, note that Proxycurl has been sunset.
- If your actual problem is broader than LinkedIn scraping, and you care about fresh public-web company and people intelligence, NinjaPear is now the more relevant place to look.
That is the cleanest way to say it without pretending these products are identical.
The bigger lesson is the same one I learned the expensive way years ago: data volume is easy to market, but data timeliness is what actually closes the gap between “interesting record” and “useful signal.” Those are not the same thing.
If you are evaluating People Data Labs today, map your use case first. Bulk enrichment and licensed datasets are one problem. Live competitive intelligence, person resolution, and trigger-based sales timing are another. Buy for the problem you actually have.
If that second bucket sounds more like your world, start with NinjaPear’s free trial and test it against a real workflow, not a spreadsheet fantasy.
