Subscription figures are provider list prices recorded in treg.to’s own catalog grid; per-call prices are what treg.to charges today, with $0.000 added.
set up treg — https://treg.to/llms.txt
Using treg, fetch these 40 LinkedIn profile URLs from the cheapest verified provider, show me the price per profile first, then give me name, headline, current company and years in the current role as a table, and list the URLs that came back empty.
Most rows take the public profile URL or its slug. A name and a company is a different job, and a different page.
A full profile is a large object. Naming the columns keeps the table readable and keeps the agent from spending its context on someone's volunteering history.
The rates here differ by more than an order of magnitude per profile. treg.to prints the rate before the call, so 40 profiles has a number on it before anything runs.
Every provider misses some profiles. A list of the URLs that came back empty is worth more than a table that quietly has 34 rows instead of 40.
treg.to holds the provider keys. Neither you nor the agent sees them.
The provider's own rate, $0.000 markup, from a prepaid balance.
Charged per call. $1.00 free per new team, no card to start.
Already pay Hunter? Register it and those calls are never metered.
Another provider is a different word in the prompt, not a new integration.
No SDK, no OAuth dance per vendor, no seats.
treg.to does not choose for you. It hands ChatGPT this comparison, with the price shown before any call, and ChatGPT picks. Or you tell it how: "cheapest", "most reliable", "the one that takes what I have", or a provider by name.
Bright Data at $0.0015
ScrapeCreators at $0.00188
TikHub at $0.001
Those units are not interchangeable: one call can return many results, so compare on the unit you will actually be billed in.
| Provider | Success | Median | Sample |
|---|---|---|---|
| 100% | 213ms | 6 calls | |
| 100% | 13.2s | 300 calls | |
| 100% | 18.9s | 9 calls | |
| 100% | 26.1s | 84 calls | |
| 100% | 2.5s | 1999 calls |
Measured on treg.to traffic: real calls, real inputs, and sample sizes differ by provider. Live reliability, not a controlled benchmark.
| Provider | Price | Accepts | Success rate | Verified |
|---|---|---|---|---|
| $0.001 per found | url | 100% (300 calls) | 2026-07-28 | |
| $0.0015 per result | dataset_id, format, input | 100% (84 calls) | 2026-07-28 | |
| $0.00188 per call | url | 100% (1999 calls) | 2026-07-28 | |
| $0.0295 per found | username | 100% (9 calls) | 2026-07-28 | |
| $0.04 per call | identifier, getDetailedEducation, getDetailedWorkExperience | (3 calls) | unverified | |
| free, your own account | see endpoints | 100% (6 calls) | unverified |
treg call brightdata.linkedin.user.profile --query dataset_id=gd_l1viktl72bvl7bjuj0 --query format=json --data '[{"url":"https://www.linkedin.com/in/satyanadella"}]'Swap the id for any provider above. All 6 endpoints behind this job, with their parameters and captured responses, are on the LinkedIn shelf.
LinkedIn data is one of the most seeded topics on Reddit: of the ~170 posts read in August 2026, one account posted fourteen actor listings in a subreddit it appears to run, one promo shipped with its template placeholder still in the body, and one seeded thread carried a zero-width space. These five are practitioners, and the last one is the reason the page will not claim zero risk.
“My SDR got his account limited after using PhantomBuster for like 3 days.” r/b2bmarketing, 16 points
What this page can do about it: That risk is the one thing these rows genuinely remove. A call here runs on the provider's infrastructure, not on your seat, so there is no cookie of yours and no activity on your account to flag.
“I review hundreds of Linkedin profiles a week for a living and it is not viable to be banned from the platform.” r/apify
What this page can do about it: Which is the argument for keeping the fetch off your login entirely. Read the profiles through a provider, keep your own account for the human work, and pay per profile rather than per seat.
“Tools like Clay's 'Enrich Profile' feature or APIs like Brightdata feel quite pricey. Any suggestions or alternative approaches?” r/revops, 5 points
What this page can do about it: Bright Data is one of the rows here, at its own per-record rate with nothing added, and it is not the cheapest one. The point is the shape: a profile is a call, there is no plan to be on, and the rate is printed before the agent spends it.
“Looking for a good alternative to Proxycurl (they shut down) for B2B sales automation” r/n8n, 6 points
What this page can do about it: This page is five of the alternatives with their prices next to each other. It is worth saying that the forum has not moved on to one successor, so the checklist that thread asks for, profile plus company plus job changes, is three jobs here and three rows.
“the funny thing is i wasnt using anything to automate linkedin, just clicking around and opening new tabs” X, 199 likes
What this page can do about it: Which is why this page says the rows remove the automation risk on your own login and stops there. Nothing here makes LinkedIn safer for the account you browse with; it just stops needing it.
LinkedIn's own OAuth row is free, runs on your team's connected account and is never metered, and it returns that account's own profile: name, email, member id. It cannot fetch a prospect. The other five fetch anyone's public profile and none of them uses an account of yours, so no cookie of yours, no extension in your browser and no view on your activity log.
TikHub bills per successful call, ScrapeCreators per call whether or not the profile comes back, Bright Data per record delivered, JustOneAPI per success priced in yuan and converted, and Fiber per credit for a live fetch. The cheapest claim on this page is made inside one unit and never across them. ScrapeCreators, Bright Data, TikHub and JustOneAPI have been called live and carry a verified date in the catalog; the Fiber row and the LinkedIn OAuth row are documented from the provider's own docs and have not been verified through treg.to.
Each provider reads LinkedIn its own way, so a profile one returns in full is one another returns thin, and the fields most often missing are exactly the ones people want: the dated experience list. The cheap experiment is the honest answer: run the same twenty URLs through three rows for a few cents and count who filled the columns you need. treg.to shows the rows side by side and the agent picks; it does not route between them and it does not fail over.
A LinkedIn API, as people mean the phrase, returns a person's public profile as structured data: headline, location, the roles they have held with dates, and where they studied. LinkedIn's official API does not do this. It is an OAuth API for the account that authorised it, so it can tell you your own name and email and it cannot tell you a stranger's job history; that product does not exist for you. Everything else on this page is a third-party provider that reads public profiles with its own infrastructure and sells the result per profile. Proxycurl, which was the default answer here for years, shut down, which is why the question keeps being asked.
No. LinkedIn's OAuth API returns the profile of the member who authorised your app, so it answers "who am I" and never "who is this person". The row on this page is that call, marked free and run on your own connection. Everything else here is a third party.
None of the five third-party rows uses an account of yours: no cookie, no session, no extension, so there is nothing of yours for LinkedIn to restrict. That is not the same as zero risk in general, because the restriction stories in the forum include people who were only browsing. The risk these rows remove is the automation risk on your own login; the provider carries its own.
Nothing single replaced it. The five third-party rows here are what the catalog carries for a person profile by URL, priced per profile with no plan, and the honest note is that they are not a drop-in: the field coverage and the freshness are each provider's own. Company pages, job changes and people search are separate jobs on separate rows.
The one whose fields survive your list at a rate that fits the volume. treg.to puts the rate, the billing unit and the measured success rate beside each other and the agent chooses, or you tell it which to use. treg.to compares; it does not pick for you.