Thirdwatch LinkedIn Jobs Scraper vs curious_coder
Both actors scrape LinkedIn's public job listings without a login and return title, company, location, description, and apply URL. curious_coder is the established category incumbent (~45K users) and the reason most teams know LinkedIn Jobs can be scraped cheaply. Thirdwatch's edge is the extraction path: we rebuilt the actor on LinkedIn's guest SEARCH + guest DETAIL APIs (`/jobs-guest/jobs/api/...`) so the whole thing runs with zero browser — which dropped our internal cost ~96% versus the Playwright build it replaced ($2.72/1K → ~$0.10/1K). That's the story this page leads with.
Head-to-head
| Dimension | Thirdwatch LinkedIn Jobs Scraper | curious_coder LinkedIn Jobs Scraper |
|---|---|---|
| Login / cookies required | None — pure guest API, no account, no cookies Tie. Both scrape LinkedIn's public job surface. | None — also no-login |
| Extraction architecture | 100% HTTP via LinkedIn's guest search + guest detail APIs — no browser at all The no-browser guest-API path is what makes Thirdwatch cheap to run — and is the differentiator. | Established no-login approach; widely used incumbent |
| Cost engineering | ~96% cheaper to run than our prior Playwright build ($2.72/1K → ~$0.10/1K) — savings reflected in low PPE tiers Both are cheap; compare the live per-result rate on each Store listing at your volume. | Competitive low per-result pricing — long the value benchmark in this category |
| Pricing model | Pay per result, 4 volume tiers (FREE→GOLD). No subscription. | Pay per result. No subscription. |
| Fields per job | 25+ fields: title, company, location, parsed salary (min/max/currency), skills, description, experience level, job type, remote flag Thirdwatch parses salary into min/max/currency and flags remote — useful for filtering. | Core job fields: title, company, location, description, apply URL |
| Volume control | Driven by max_results (not max_pages) so you get exactly the count you pay for | Standard query + paging controls |
| Userbase (Apify Store) | Newer listing, smaller userbase, growing curious_coder is more battle-tested; Thirdwatch is the cost/structure-focused challenger. | ~45K users — the category incumbent |
| Multi-source from one vendor | One API + MCP across LinkedIn, Indeed, Naukri, Google Jobs, Reed, Adzuna, RemoteOK and more Thirdwatch wins if you want one billing relationship across multiple job boards. | LinkedIn-focused |
Pick Thirdwatch if…
- You want parsed salary (min/max/currency), skills, and a remote flag — not just raw text
- You're aggregating LinkedIn alongside Indeed / Naukri / Google Jobs / Reed and want one API + MCP
- You want volume driven by an exact result count (max_results), not page count
- You want the cost-engineered guest-API path with low PPE tiers
Pick curious_coder LinkedIn Jobs Scraper if…
- You want the most battle-tested LinkedIn Jobs actor with the largest userbase and review history
- Your pipeline is already built around curious_coder's output schema and switching cost outweighs the gains
- You only need core job fields and have no use for salary parsing or multi-board aggregation
Notes
The 96%-cheaper story is real and worth understanding because it explains the pricing. The first version of this actor drove a Playwright browser to render LinkedIn job pages — slow, heavy, and proxy-hungry at ~$2.72 per 1,000 jobs. We then found LinkedIn exposes a guest SEARCH endpoint (`/jobs-guest/jobs/api/seeMoreJobPostings/search`) that returns HTML job-card fragments, plus a guest DETAIL endpoint (`/jobs-guest/jobs/api/jobPosting/{id}`) for the full posting — both reachable with the right Sec-Fetch headers and no login. Moving the entire pipeline onto those APIs removed the browser, the proxy bandwidth, and most of the compute, taking the run cost to roughly $0.10 per 1,000 jobs. The actor keeps a Playwright fallback tier for the rare cases the guest API rate-limits, but HTTP is the default.
What that means for you: the low PPE tiers aren't a loss-leader — they reflect a genuinely cheap-to-run architecture, so they're durable. curious_coder remains an excellent, proven choice and is why this category is cheap at all; if you're already integrated with it, the honest answer is there's no urgent reason to switch unless you specifically want parsed salary fields, the remote flag, or to consolidate LinkedIn + Indeed + Naukri + Google Jobs under one vendor and MCP.
Legal note: both actors read LinkedIn's public job-listing pages. You remain responsible for compliance with LinkedIn's terms and applicable data-protection law when storing or sharing output. We aren't lawyers.
Try the LinkedIn Jobs Scraper for free
Sign in to Apify, run a test query, see the data yourself. Free credits cover ~100 results.