Apify job scraper comparison reveals unit economics from $0.36 to $12 per thousand

We analyze unit economics and technical architecture across Apify job scrapers, comparing raw LinkedIn scrape costs against official ATS board integrations for HR tech founders.

The answer up front

If you need high-volume, low-cost raw LinkedIn data and can tolerate noise, valig/linkedin-jobs-scraper delivers the lowest baseline pricing at $0.36 per 1,000 jobs. If you build recruitment automation or target company monitoring where location and filter precision matter, Aurelix's scrapers ($1.05–$1.50 per 1,000 jobs) cost more up front but eliminate wasted spend by dropping filtered results and querying official public ATS endpoints. Skip fantastic-jobs if you only target standard tech startups, as its $12.00 per 1,000 jobs tier is expensive unless you strictly require its 58-ATS coverage for enterprise platforms like Workday and iCIMS.

Methodology

This analysis evaluates published benchmark data and pricing structures authored by developer Aurelix on Dev.to. The underlying signal documents cost structures, filter enforcement, and scraping techniques across six popular actors on the Apify Store as of November 2026.

Our scope covers unit costs per 1,000 job records, start fees, geographic filtering mechanics, and ATS board integration models across LinkedIn and ATS scrapers (including Greenhouse, Lever, Ashby, SmartRecruiters, Workday, and iCIMS). v0 review draws on the founder's published claims at https://dev.to/aurelix_solutions/linkedin-and-career-site-job-scrapers-on-apify-compared-prices-filters-and-coverage-52k; independent benchmarks pending. Update cadence: re-tested when claims diverge from observed behavior. We did not independently measure proxy burn rates, scraping success rates under heavy anti-bot enforcement, or API response latency under load.

What it does

LinkedIn scraping cost and precision trade-offs

LinkedIn scraping actors on Apify operate without user login requirements, charging on a per-job basis rather than platform execution time. Pricing spans $0.36 to $2.00 per 1,000 records depending on tier and provider.

valig/linkedin-jobs-scraper leads on price, charging $0.40 per 1,000 jobs on free plans and $0.36 on paid tiers, with a $0.001 start fee ($0.0009 paid). However, location filtering relies on raw city, state, or zip text strings, which increases irrelevance rates. curious_coder/linkedin-jobs-scraper charges $2.00 per 1,000 jobs (free) and $1.00 (paid) with a $0.00005 per GB memory start fee, offering the longest operational track record but using manual geoId inputs without secondary page validation.

Aurelix LinkedIn Jobs Scraper charges $1.50 per 1,000 jobs (free) and $1.05 (paid) with a $0.00005 start fee. It enforces strict geographic criteria by setting LinkedIn's explicit country ID, re-checking job type and seniority level on individual job pages, dropping non-matching entries, and explicitly waiving charges for dropped results.

Career-site and ATS board ingestion

Direct employer site scraping splits between narrow official API readers and broad multi-platform indexers.

Aurelix Greenhouse Jobs Scraper charges $1.05 to $1.50 per 1,000 jobs with a $0.00005 start fee. It targets 4 core developer platforms (Greenhouse, Lever, Ashby, and SmartRecruiters) by dynamically discovering job boards from company domain inputs and reading directly from official ATS APIs. It tracks incremental changes to bill only for newly posted jobs.

fantastic-jobs/career-site-job-listing-api charges a flat $12.00 per 1,000 jobs across free and paid plans, alongside a $0.01 start fee ($0.0001 on paid). It trades high cost for coverage across 58 ATS platforms, including enterprise systems such as Workday and iCIMS. Its sister actor, fantastic-jobs/career-site-job-listing-feed, offers a lower $2.00 to $2.50 per 1,000 jobs rate ($0.10 start fee; $0.001 paid) for batched feed access across the same 58 ATS platforms.

What's interesting and what's missing

The critical engineering trade-off across these Apify actors is the distinction between unvalidated scraper throughput and payload accuracy. In raw scraping, paying $0.36 per 1,000 items with valig looks dramatically cheaper on paper than $1.05 with Aurelix. However, if 40% of scraped LinkedIn listings contain mismatched geographic tags or stale role levels, downstream pipeline cleaning and LLM parsing costs rapidly erase that initial scraping margin. Aurelix's business model innovation is absorbing the cost of dropped records, aligning pricing with actual usable data yield.

What is missing from the comparison data is real-world durability under LinkedIn rate limiting and IP blocking. Scrapers using official public endpoints (like Aurelix querying Greenhouse or Ashby APIs) offer low failure rates and clean structured JSON. Scrapers scraping public web pages directly face constant DOM changes and proxy rotation expenses. The post does not disclose total cost of ownership when proxy bandwidth consumption increases during heavy scraping runs.

Pricing snapshot (November 2026)

• valig/linkedin-jobs-scraper: $0.40 / 1k (free), $0.36 / 1k (paid); start fee $0.001 ($0.0009 paid).
• curious_coder/linkedin-jobs-scraper: $2.00 / 1k (free), $1.00 / 1k (paid); start fee $0.00005/GB.
• Aurelix LinkedIn & Greenhouse Scrapers: $1.50 / 1k (free), $1.05 / 1k (paid); start fee $0.00005. Free plan includes standard Apify limits.
• fantastic-jobs/career-site-job-listing-feed: $2.50 / 1k (free), $2.00 / 1k (paid); start fee $0.10 ($0.001 paid).
• fantastic-jobs/career-site-job-listing-api: $12.00 / 1k (free and paid); start fee $0.01 ($0.0001 paid).

Verdict

For early-stage recruiting tools monitoring standard tech ecosystem companies, pair Aurelix's Greenhouse scraper with valig's LinkedIn scraper. Use Aurelix for live board monitoring across Greenhouse, Lever, Ashby, and SmartRecruiters where official API efficiency matters. Use valig for broad market mapping where raw data volume outweighs item-level precision. Upgrade to fantastic-jobs only when enterprise sales demand coverage of legacy systems like Workday or iCIMS, where $12.00 per 1,000 jobs can be passed directly through to enterprise contract margins.

What we'd test next

In a controlled benchmark, we plan to measure actual HTTP success rates and end-to-end execution times for 10,000 job extraction runs across all six actors. We will evaluate real proxy consumption costs, payload schema stability over a 30-day window, and rate-limiting resilience across regional proxy pools.

The investor read

The Apify ecosystem benchmark highlights how unbundled data scraping actors are shifting from simple HTTP extraction to specialized data pipeline primitives. Job scraping unit economics ($0.36 to $12.00 per 1,000 jobs) demonstrate a 33x price spread driven by target platform complexity. Broader ATS platforms like Workday and iCIMS command premium $12.00 per 1,000 rates due to anti-bot protection and fragmented web forms, while official ATS API scrapers (Greenhouse, Ashby, Lever) collapse marginal extraction costs near $1.00. For investors in HR tech and AI recruiting agents, infrastructure costs scale rapidly if systems rely on raw web scrapers rather than direct ATS API endpoints. Tools that absorb cleaning costs and handle incremental diffing at the ingestion layer will capture higher gross margins in HR data workflows.

Pull quote: “Aurelix's business model innovation is absorbing the cost of dropped records, aligning pricing with actual usable data yield.”