Apify LinkedIn scraper alternative

Marketplace versus single API

On Apify you choose an actor from several that scrape LinkedIn, each maintained by a different developer, each with its own input schema, output shape, pricing and update cadence. Some are excellent and actively maintained. Some have not been touched in a year.

That evaluation is real work, and it recurs — the actor you picked can be abandoned, and you find out when your pipeline breaks. Here there is one API, one schema, one price list, and one team responsible when it stops working.

The trade is flexibility. If your requirement is unusual, Apify probably has something for it and we probably do not.

Runs versus requests

Apify actors are runs: you start a job, it executes for some duration, you collect the dataset. That model suits batch work well and interactive work poorly — a user waiting on a page load is not a good fit for starting a run and polling for a result.

Ours is a synchronous request that returns JSON in a few hundred milliseconds, which suits interactive enrichment and awkwardly suits very large batch jobs. If your workload is "enrich two million rows overnight", a run-based platform is a more natural shape and you should probably use one.

Cost predictability

Actor pricing varies by author and often bills on compute time as well as results, so cost per record depends on how efficiently that particular actor was written and how long the run takes. Estimating a bill in advance is genuinely hard.

Here the arithmetic is fixed: the endpoint has a published price, that price is on the response, and a failed call is free. Whether that is worth the price difference depends on how much your team's time is worth relative to the per-record cost.

Evaluating an actor before you depend on it

If you go the marketplace route, the evaluation is the work, and it is worth doing properly rather than picking the top result.

Check the update history. An actor last touched fourteen months ago is a liability regardless of how good its description reads.

Check what happens on failure. Does it return an error, or an empty dataset? An actor that silently returns nothing when a page changes will corrupt your pipeline quietly, which is far worse than one that crashes.

Read the pricing model carefully. Results-plus-compute means a slow actor costs more for identical output. That difference is invisible until the bill.

Have a second one identified. Not as a formality — actually run it once, so that switching is an afternoon rather than a research project on the day your pipeline breaks.