Edges vs Xverum
A bulk dataset feed, or live records on demand.
Who this comparison is for
You're sourcing people and company data at volume and comparing a licensed dataset delivery against a per-call API. The two look interchangeable on a feature grid and are completely different in practice.
At a glance
The axes that matter when you’re picking between us.
| Topic | Edges | Xverum |
|---|---|---|
| Delivery model | Per-call REST action, response in ~1.2s. | Bulk dataset feeds delivered to storage on a refresh cadence. |
| Freshness | Read at request time — the record is as current as the source. | As fresh as the last crawl cycle for that record. |
| Coverage shape | Deep per record: full experiences, skills, SN identifiers, network position. | Broad across records; field depth varies by crawl. |
| Sales Navigator | First-class — SN IDs on every record, SN search as an action, managed seats. | Not a dataset surface. |
| Write actions | Messages, invites, follows from your connected identities. | Out of scope — data delivery only. |
| Cost shape | Per-action credits — you pay for records you actually look at. | Dataset licensing — you pay for the corpus. |
| Best fit | Product features that read or act on a specific person or company. | Warehouse-scale analysis, enrichment backfills, model training. |
Pick the right one
Choose Edges when
Your product reads a specific person or company at the moment a user asks, and the answer has to be current. You also need Sales Navigator IDs, post activity, and the ability to act — message, invite, follow — not just read.
Consider Xverum when
You need hundreds of millions of records sitting in your own warehouse for offline analysis, model training, or market sizing, and a record that is weeks old is acceptable for that job.
Migration
Map your current Xverum calls or automations to Edges actions using the Library and the documentation. Ask support for the OpenAPI spec and it drops straight into Postman, Hoppscotch, or any code generator. Share your integration outline and we can suggest parity endpoints and credit estimates.
FAQ
Isn't a full dataset better value than per-call pricing?
It depends entirely on your read pattern. If you touch a small fraction of the corpus, per-call is dramatically cheaper and always current. If you genuinely analyse the whole corpus offline, a licensed dataset is the right shape and Edges is the wrong tool.
Can Edges backfill our warehouse?
For a defined list, yes — run the action in async mode over your input set. For hundreds of millions of records with no target list, a dataset provider is the better instrument.
How stale is a dataset in practice?
Ask for the refresh cadence per field, not per dataset. Job titles and headcount move faster than crawl cycles, which is why a record that was accurate at crawl time is often wrong when your user reads it.
Can we use both?
Common pattern: license a dataset for the broad backfill and analytics layer, call Edges live at the point of use so the record your user sees is current.
Build on Edges
Credits-based pricing, SOC 2 Type II, and a growing catalog of LinkedIn actions — see Pricing and Enterprise for scale.
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Edges is not related to LinkedIn and is not an official LinkedIn product.