Apify vs Bright Data
Both are platforms rather than APIs, which is what separates them from everything else in this category. The choice between them is mostly a choice about how much infrastructure you want to own and who you want to be accountable when it breaks.
We build a competing product, so we are a third party to this comparison. It sticks to structural differences that are observable, and avoids pricing and data-quality claims we would have to invent.
| Apify | Bright Data | |
|---|---|---|
| Shape | Marketplace of community actors | Proxy and collection infrastructure |
| Who maintains the scraper | Individual actor authors | Bright Data, or you |
| Self-serve | Yes, with a free tier | Partly; enterprise is contracted |
| Bulk datasets | Via actor runs | Yes, first-class |
| Beyond LinkedIn | Very broad | Very broad |
| Execution model | Asynchronous runs | Proxy requests or dataset delivery |
Marketplace versus infrastructure
Apify is a platform where independent developers publish and sell scrapers, called actors. For LinkedIn there are several, of varying quality and maintenance. You pick one, learn its input schema, and depend on its author continuing to care.
Bright Data sells the layer underneath: proxy networks, unblocking, scraping browsers, and prepared datasets. You are more likely to be writing or configuring the collection logic yourself, or buying a dataset outright, rather than picking someone's actor.
Neither is "the API" that a lot of teams are actually looking for. Both are chosen by people who need breadth across many sites, or scale beyond what a per-call API sensibly serves.
How you get started differs sharply
Apify is genuinely self-serve, with a free tier and a large community. You can be running an actor in ten minutes without talking to anyone.
Bright Data's enterprise offering typically involves an account manager, a compliance review and a contract. That is slow if you are one engineer with a deadline, and it is exactly what a regulated company needs — a DPA, an SLA and a named contact are not bureaucracy when procurement requires them.
A useful heuristic: if you would struggle to get a purchase order approved, Apify. If you would struggle to get an unvetted vendor approved, Bright Data.
Maintenance risk, in both directions
On Apify, the risk is actor abandonment. Layouts change, an actor stops working, and if its author has moved on you discover this when your pipeline fails. The mitigation is to check the actor's update history before you depend on it, and to have a second one identified.
On Bright Data, if you are writing your own collection logic, the maintenance is simply yours. Their infrastructure keeps working; whether your parser still matches the page is your problem. Buying a prepared dataset moves that burden back to them, at dataset prices.
Both models push a real ongoing cost onto you that a first-party API absorbs. Whether that trade is worth it depends on how many sources you need and how much engineering time you have.
When neither is the right shape
If you need LinkedIn data specifically, synchronously, inside a product feature where a user is waiting — neither platform is a natural fit. Apify's run-and-poll model is built for batch, and Bright Data's is built for collection at scale.
That is the gap a single-source REST API fills, ours included: one endpoint, a few hundred milliseconds, JSON back, priced per call. It is a much narrower product than either of these, and for anything beyond LinkedIn it is no help at all.
Frequently asked questions
Which is cheaper for LinkedIn scraping?
It depends on volume and shape, and Bright Data enterprise pricing is negotiated rather than published, so any figure quoted here would be invented. Apify is generally more accessible at low volume; negotiated infrastructure pricing tends to win at very high volume.
Do I need proxies with either?
Bright Data is largely a proxy and collection product, so proxies are the point. On Apify, actors typically handle it internally, sometimes using Apify's own proxy offering as a paid add-on.
Can I use either for a live product feature?
Awkwardly. Apify actors are asynchronous runs, which suits batch far better than a user waiting on a page load. A synchronous per-call API is a more natural fit for interactive features.
What happens if an Apify actor stops being maintained?
You switch to another actor and adapt to its input and output schema. Checking an actor's recent update history before depending on it is the standard mitigation.