Coresignal vs People Data Labs: two dataset providers, and when on-demand enrichment fits better

If you are choosing between Coresignal and People Data Labs, the first thing to know is that they are the same kind of thing. Both match your input against a stored corpus they refresh on their own schedule, and both are sized for coverage. The real fork is not which of the two you pick. It is whether you need a dataset at all, or whether you need to enrich a specific record you can already name.

They are both dataset providers, sized differently

Neither service fetches a page live when you call it. You send a match key, they answer from a corpus they maintain, and the answer is as fresh as their last refresh of that record.

People Data Labs performs a one-to-one match against the “nearly three billion individual profiles” hosted in its dataset, which makes it person-first. Coresignal advertises “4.5B+ company, employee, and jobs records in real time” and leans toward the company and firmographic side, delivered through APIs, bulk downloads, and licensed datasets.

Pick People Data Labs when the job is person enrichment at scale and you can accept the coverage of a stored corpus. Pick Coresignal when the job is company and workforce data and you want bulk delivery, not just per-record calls. That is the honest short version of the head to head.

Where the dataset model bills differently than you expect

One difference matters before you write any code. People Data Labs charges a credit only when a request returns a profile; a request that matches nothing does not consume a credit. A stored-dataset match either finds a row or it does not, and a miss is cheap.

An on-demand enrichment API prices the opposite way, and it is worth saying plainly rather than burying it. Triguna charges one credit for a 404, because a lookup that turns out to have no record is still a real retrieval that ran. If most of your keys are going to miss, the no-charge match model is genuinely cheaper for that shape of work, and you should know that going in. The full cost picture is in what an enrichment run costs.

When neither dataset is the right tool

A corpus is the right tool when you want to search, list, or match at volume against records someone else has already gathered. It is the wrong tool when you already have the identifier and what you actually need is the current state of that one record, with a record of where it came from.

That second job is on-demand enrichment. You key it by identifier and it retrieves that entity now. Triguna is exactly this and nothing more: two endpoints, GET /v1/people/profile and GET /v1/companies/details, plus a balance check. There is no search path, no employee-listing path, and no bulk endpoint, so enriching N entities is N calls. If your job is discovery, a dataset provider is your tool and this is not.

curl "https://api.triguna.ai/v1/companies/details?company_id=acme-corp" \
  -H "ApiKey: $TRIGUNA_KEY" -i

HTTP/1.1 200 OK
X-Data-Source: live
X-Fetched-At: 2026-09-13T09:14:22Z
X-Data-Age-Seconds: 0
X-Credits-Charged: 1

The company_id takes the URL slug, acme-corp, not the display name. Send “Acme Corporation” and you get a 404 that still costs a credit, which is the first of the identifier traps worth fixing before your first call.

The axis that separates on-demand from a dataset

Freshness and provenance are the same question for a stored corpus and different questions for an on-demand fetch.

A dataset row is as current as the provider’s last refresh of it, and that date lives in the body as a last_updated field. An on-demand response is fetched at call time, and its provenance lives in the headers instead: X-Data-Source says whether the row was fetched live, served from a store under 24 hours old, or returned as a stale_fallback during an outage, and X-Fetched-At and X-Data-Age-Seconds say exactly how old it is. Provenance in the headers, never a "source" field in the body, is the same shape on a fresh fetch and a cached one. The caching and provenance reference has the detail.

A short decision list

1. You need to search or list entities you cannot name yet. A dataset provider is your tool. People Data Labs for person-first work, Coresignal for company and workforce data.
2. Most of your keys will miss. A no-charge match model favors you, because a Triguna miss costs a credit and a dataset miss does not.
3. You already have the identifier and want the current record. Enrich on demand, and read the source and age out of the response headers.
4. You want a surface you can hold in your head and hand to an agent. Two documented endpoints is a contract you can read in one sitting, which is the point of the about page.

Start on the five free credits before you wire up billing; the pattern for calling it once at account creation without slowing signup is in enrich on signup.

Next

• Person API: what GET /v1/people/profile returns and the identifier it expects.
• Company API: the company fields, and why company_id takes the slug rather than the name.
• Proxycurl alternatives compared: the wider field sorted by how each replacement actually works.
• Enrich on signup: calling the API once at account creation without blocking a new user.
• Pricing: one credit per answered lookup, and the status codes that cost nothing. Sign up for five free credits at app.triguna.ai/signup.

comparisoncoresignalpeople-data-labsenrichment