People Data Labs alternative

Do you have an identifier?

This is the question that decides it.

If you hold a LinkedIn URL, handle or profile ID, we resolve it live and cheaply, and what you get back is current.

If you hold a name and an employer, or an email address, and you need to work out who that is — that is identity resolution across sources, and it is what PDL is for. We can approximate it with profile-search on firstName, lastName and currentCompany, but common names return several plausible matches and you need a confidence threshold or a human. We do not pretend that is the same thing.

Single source, stated plainly

Everything we return comes from one public source. That makes provenance simple — you can go and look at the page — and coverage narrow. Someone with no LinkedIn presence does not exist as far as we are concerned.

A multi-source database fills those gaps by merging records, which is genuinely more useful for coverage and introduces a different problem: merge errors are hard to detect and hard to correct, and a wrong merge is worse than a missing record.

Neither approach is strictly better. Ours is narrower and easier to verify; theirs is broader and requires trusting the merge.

Contact data

PDL offers contact data including email addresses. We do not, on any endpoint, at any price, and we are not planning to.

If email is part of your requirement, this comparison ends here — take PDL or a dedicated email provider. We would rather lose the evaluation than have you discover the gap after integrating.

Cost shapes that do not compare directly

Comparing a database subscription with a per-call API on price is harder than it looks, because you are comparing a fixed cost against a variable one.

A dataset is usually an annual commitment sized on volume. The cost per record falls the more you use, and it does not fall at all if you use less than you planned. That suits a workload you can forecast.

Per-call pricing is the opposite: nothing is committed, the cost tracks usage exactly, and there is no volume discount waiting at scale beyond the tier discounts. That suits a workload you cannot forecast, or one that is still finding its shape.

The honest way to compare is to model your actual pattern over a year, including the months where usage is low. A team that enriches in bursts around campaigns often finds per-call cheaper despite a higher headline rate, because they pay nothing in the quiet months. A team enriching continuously at volume usually finds the opposite.