Every provider in this market leads with a count: millions of postings, hundreds of thousands of companies, dozens of sources. The count is the least useful thing about the product, because raw postings are cheap and the hard parts are elsewhere.
What you are actually buying
- Collection. Someone runs the crawlers, handles the blocks, and keeps up when a source changes its markup. This is continuous work, not a one-off.
- Deduplication. One role cross-posted to five boards must become one record. Without this your counts are inflated by a factor you cannot estimate.
- Normalisation. Free-text locations into city, region, country. Salary strings into a number, a currency and a period. Titles into something groupable.
- Expiry. Knowing when a listing came down is harder than knowing when it went up, and more valuable.
- A stable schema. Fields that mean the same thing next quarter.
The fields that separate providers
| Field | Why it is hard | Why it matters |
|---|---|---|
| Normalised salary | Free text in dozens of formats and currencies | Without it you cannot compute anything |
| Deduplication group | Same role, different wording, different boards | Every count depends on it |
| First seen / last seen | Requires storing history, not snapshots | The only way to compute posting age |
| Posted by agency | Rarely stated; must be inferred | Changes what the posting means |
| Normalised location | Ambiguity, remote, multi-site roles | Every geographic cut depends on it |
Questions worth asking before you buy
- What share of postings have a normalised salary, not just a salary field?
- How do you decide two postings are the same role, and what is your error rate?
- Do you keep first-seen timestamps, or only the current snapshot?
- When a listing disappears from the source, how quickly do I learn?
- Where does the data come from — the employer's own systems, or another vendor's feed?
That last one matters commercially and legally. A provider re-selling another vendor's feed inherits that vendor's risk. Proxycurl, a well-known provider of adjacent data, shut down in July 2025 following litigation from LinkedIn. Where the data comes from is not a detail.
What "real time" usually means
Rarely what it sounds like. Ask for the distribution, not the claim: what share of postings are discovered within an hour, within a day, within a week. A provider who cannot answer has not measured it.