Pricing

Pay per event

Preply Tutor Directory Scraper

Scrape the Preply tutor directory for language and subject tutors. Extracts name, subjects, native language, country of birth, hourly rate (USD), trial lesson price, rating, review count, lesson count, languages spoken, profile URL, and badges.

Pricing

Pay per event

Rating

0.0

(0)

Developer

BowTiedRaccoon

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

What data does it extract?

Each tutor record includes:

Field	Description
`tutor_id`	Unique Preply tutor ID
`name`	Tutor full name (first name + last initial)
`subjects_taught`	List of subjects taught (e.g. English, Spanish, Math)
`native_language`	Tutor's native language(s)
`country_of_birth`	Country of birth
`hourly_rate_usd`	Hourly lesson rate in USD
`trial_lesson_price_usd`	Trial lesson price in USD
`rating`	Average rating (0–5 scale)
`reviews_count`	Total number of student reviews
`lessons_count`	Total number of lessons completed
`languages_spoken`	Languages spoken with proficiency level (e.g. English (Native))
`profile_url`	Full URL to the tutor's Preply profile
`badges`	Achievement badges (e.g. Super Tutor, Top 5)

How does it work?

The actor uses Preply's Next.js __NEXT_DATA__ JSON embedded in each listing page — no JavaScript rendering required. It:

Loads the English tutors seed page to discover all 179 subject slugs
Paginates through each subject's tutor listing (e.g. /en/online/english-tutors?page=N)
Extracts all tutor fields directly from the embedded JSON — no per-profile page visits needed
Respects maxItems to cap the result count

Input

Parameter	Type	Default	Description
`maxItems`	integer	15	Maximum number of tutor records to scrape

Output

Results are saved to the default dataset. Example record:

{
  "tutor_id": "311304",
  "name": "Louise M.",
  "subjects_taught": ["English"],
  "native_language": "English",
  "country_of_birth": "United Kingdom",
  "hourly_rate_usd": 36,
  "trial_lesson_price_usd": 18,
  "rating": 4.99,
  "reviews_count": 25,
  "lessons_count": 3275,
  "languages_spoken": ["French (C2)", "Arabic (C2)", "English (Native)", "Spanish (A1)", "Italian (A1)"],
  "profile_url": "https://preply.com/en/tutor/311304",
  "badges": []
}

Use cases

Language school lead generation — identify tutors by subject and native language for outreach
Pricing intelligence — benchmark hourly rates across subjects and countries of origin
Marketplace research — analyze tutor supply, rating distributions, and lesson volume by subject
Competitor analysis — track tutor counts and quality signals across Preply's subject catalog

Notes

Prices are normalized to USD using Preply's seoPrice field (always factor=1)
179 subjects are crawled; tutors teaching multiple subjects appear on multiple subject listing pages
Set a low maxItems (e.g. 100–1000) for targeted sampling
robots.txt specifies crawl-delay: 10; the actor respects this with conservative concurrency

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