Coresignal alternative

Stored records versus live fetch

A dataset provider collects records continuously and serves you what was collected. That is enormously powerful for anything population-scale: market sizing, labour-market analysis, training a model, longitudinal studies of how careers move.

The cost is freshness. Any stored record has an age, and for employment data that matters — the single most valuable fact about a contact is usually whether they still work where you think they do.

We fetch at request time. Every response reflects the profile as it is now, and there is no staleness question to reason about. We have no history at all: we cannot tell you what a profile said last year, because we did not store it.

Which shape fits your question

Use a dataset if your question is about a population: how many companies in this segment, how has headcount moved across an industry, what does the career graph look like. Answering that with per-record lookups would be absurd — millions of calls to compute one number.

Use lookups if your question is about a specific entity right now: is this contact still at this company, what does this account look like today, who is on this buying committee. Answering that from a dataset means accepting whatever age the record has.

Plenty of teams sensibly use both — the dataset for analysis, lookups for the records that are about to be acted on.

Commercial shape

Dataset access is typically an annual contract with a commercial process attached, which is appropriate for the value and volume involved. It is a poor fit for evaluating something this week.

Ours is self-serve with a free trial balance and no card. That is better for getting started and worse if you need a DPA, a negotiated SLA and a named account manager — we do not offer those on self-serve, and we would rather say so than imply otherwise.

Freshness is a question about your use case

"How fresh is the data" is the wrong question on its own. The useful version is: how fast does the field you care about change, and what does being wrong cost you?

Company headcount moves slowly. A record a few months old is fine for segmentation and will not embarrass anyone.

Job titles and employers move constantly, and being wrong is visible — addressing someone by a role they left is the fastest way to have an email deleted.

So the pragmatic split most teams land on: a dataset for the attributes that are stable and needed at population scale, and live lookups for the handful of records about to be acted on this week. That costs far less than enriching everything live, and avoids the specific failure that makes outreach look careless.