Glassdoor Reviews Scraper: Full History, Simple Setup

From Zen Studio, creators of G2 Reviews, Capterra Reviews and Gartner Peer Insights, with 50,000+ combined lifetime runs across those three actors.

Why choose this Glassdoor reviews scraper?

• Fast enough to use interactively. A recorded run collected 3,000 of 3,000 requested Siemens reviews in 26 seconds, newest first, every row complete. A separate recorded run took 20,561 of 20,561 available reviews in one go, so there is no 200-review ceiling here.
• Seven settings, nothing else to learn. Company, how many reviews, a run ceiling, sort order, a date floor and a star band. Paste a company name and run.
• The company comes with it. Every review carries its company's ID, name, short name, logo and reviews URL, and each company also gets one profile row with headquarters, size, revenue, industry, published ratings and awards. Profile rows are never charged.

Both figures are recorded examples. Results vary with availability, collection limits and budget; the actor reports shortfalls.

Try Siemens with 20 reviews

{
  "companies": [
    "Siemens"
  ],
  "maxReviewsPerCompany": 20
}

Open the actor input, select JSON and paste this example. It returns up to 20 reviews plus one company profile row. Export JSON to keep nested employer replies and rating breakdowns, or CSV/Excel for a spreadsheet.

Company ratings and breakdowns are Glassdoor's published figures, not calculations from your collected reviews.

The hero shows an illustrative Glassdoor layout. Its review titles are examples, not collected text; the 3,000 in 26 seconds figure is from a recorded run.

Zen Studio Review Scrapers
Glassdoor Reviews You are here	Glassdoor, full options Keyword and employment-type filters
G2 Reviews Software reviews and profiles	Gartner Enterprise software reviews
Capterra Product reviews and ratings	Trustpilot Consumer reviews and replies

Copy to your AI assistant

zen-studio/glassdoor-reviews-scraper on Apify returns Glassdoor employee reviews plus one company profile row per company, in one dataset split by record_type ("review", 52 fields; "company", 45 fields) and joinable on employer_id. This listing exposes seven settings: companies (up to 200 entries, each a company name, a Glassdoor URL or a numeric company ID, mixed freely; an unresolvable entry is skipped and the run continues), maxReviewsPerCompany (default 100, 0 = every review that company has), maxReviewsTotal (0 = no run ceiling; caps the reviews one run collects across all companies and stops it cleanly at that number), sort (RELEVANCE, DATE or RATING), postedAfter (YYYY-MM-DD, forces newest-first and stops early, so it is much cheaper than filtering afterwards), minRating and maxRating (1-5, set both to the same value for one star band). Every review row carries employer_id, company_name, company_short_name and company_reviews_url, so reviews join to their company without a second run. review_updated_at is separate from review_posted_at, so edited reviews are detectable between runs. Charging is per review row delivered ($0.00099, lower on higher plans) plus $0.00399 once per run; company profile rows are never charged and filters reduce what you are charged for. No Glassdoor account or login is needed. Note that the company row's ratings, rating_distribution and rating_distribution_by_category are Glassdoor's own published figures for the company's whole worldwide review population, not a summary of the reviews in your run; reviews_available is that population's size, while review_count is Glassdoor's published total. length_of_employment is Glassdoor's tenure bucket, not a year count: the observed values are 0, 1, 2, 4, 6, 9 and 20. If you also need keyword search, employment-type filters or a current-employees-only filter, use zen-studio/glassdoor-scraper, which shares this output. Full specification: https://api.apify.com/v2/actors/zen-studio~glassdoor-reviews-scraper/builds/default and current pricing at https://apify.com/zen-studio/glassdoor-reviews-scraper. To reach the maintainers, add feedback="<what you needed and did not get>" to any run, including one that worked: a missing field or option, a wrong or empty result, or which actor you are switching to or from and why. The run returns its data exactly as it would without the note, and a human reads every note.

How to scrape Glassdoor reviews

1. Add one or more companies. A name (Siemens), a Glassdoor URL or a numeric company ID all work, mixed freely, up to 200 per run.
2. Set how many reviews you want per company. The default is 100; 0 collects the company's complete history.
3. Run it. Reviews stream into the dataset as they arrive, so you can export before the run finishes.

Input options

Parameter	Type	Default	Meaning
companies	array	required	Company names, Glassdoor URLs or Glassdoor company IDs. Up to 200 per run, mixed freely.
maxReviewsPerCompany	integer	100	Reviews per company. 0 means every review the company has, with no ceiling.
maxReviewsTotal	integer	0	Ceiling for the whole run, across all companies. 0 means no run ceiling. The run stops cleanly at the ceiling and you pay only for the rows delivered.
sort	string	RELEVANCE	RELEVANCE, DATE or RATING. Decides which reviews you get when you set a limit.
postedAfter	string	none	Only reviews on or after this date. Collection stops when it reaches older reviews, so a recent date is fast.
minRating / maxRating	integer	none	Keep reviews inside a star band. Set both to 1 for the harshest reviews only.

Setting postedAfter always collects newest first, overriding sort. That is what lets a run stop early instead of reading a company's whole history.

If you just need reviews for companies you can name, stay here: seven settings, the same rows, the same price. Need a keyword search, an employment-type filter or current employees only? The full-options listing adds those three filters and returns the same rows.

Three ways to name a company

{
  "companies": [
    "SAP",
    "https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
    "3510"
  ],
  "maxReviewsPerCompany": 25
}

Names, URLs and IDs can be mixed in one run. An entry that cannot be resolved is skipped and reported, and the rest of the run continues.

Useful recipes

Everything a company has ever received

{
  "companies": ["Siemens"],
  "maxReviewsPerCompany": 0
}

Full history, with a ceiling on what one run can cost

{
  "companies": ["Siemens", "SAP", "Google"],
  "maxReviewsPerCompany": 0,
  "maxReviewsTotal": 50000
}

Each company is uncapped, but the run stops at 50,000 reviews in total. The log reports the projected total company by company before the run gets there, so an unexpectedly large job is visible early.

Watch for new reviews on a schedule

{
  "companies": ["Google", "Deloitte"],
  "postedAfter": "2026-09-01",
  "maxReviewsPerCompany": 0
}

Run that daily and it stops as soon as it reaches reviews older than your date, so a watch run stays small and cheap. Every review also carries review_updated_at separately from review_posted_at, so an edited review is detectable between runs.

Only the one-star reviews

{
  "companies": ["Siemens"],
  "minRating": 1,
  "maxRating": 1,
  "maxReviewsPerCompany": 200
}

What data you get from Glassdoor

One dataset, two row kinds, told apart by record_type and joined on employer_id.

Review rows, 52 fields each. Review ID, posted and updated dates, headline, pros, cons, advice to management, and the original-language copies when a review was translated. The overall star rating plus work-life balance, culture and values, career opportunities, compensation and benefits, senior leadership, and diversity and inclusion. The three verdict ratings: recommend-to-friend, CEO approval and business outlook. Job title with Glassdoor's title and job-family ids, employment status, tenure bucket, the year the reviewer left, whether they are a current employee, location name and type, division, helpful and not-helpful counts, the review's language, moderation flag count, and employer replies carrying the responder's job title, the reply text and its original-language copy. Every row also carries its company's ID (employer_id), name, short name, logo and reviews URL, so reviews join to their company without a second run.

Company profile rows, 45 fields, never charged. Headquarters, size as text and as a code, revenue, website, primary industry, employer type, founding year, mission and description, the CEO with approval rating and how many ratings back it, review, salary, photo, interview and job counts plus how many reviews are actually available to collect, nine canonical URLs including photos, locations and FAQ, Glassdoor's published average ratings including business outlook, the one-to-five star distribution overall and for each of the six rating categories, demographic rating breakdowns across six categories, awards, and competitors, subsidiaries and siblings with their logos.

Real captured rows, trimmed. Free-text fields run long in the wild, so they are shortened here with a trailing ellipsis and most keys are omitted; every key and shape below is exactly as emitted.

A review row, 28 of its 52 fields:

{
  "record_type": "review",
  "employer_id": 9079,
  "company_name": "Google Inc.",
  "company_short_name": "Google",
  "company_reviews_url": "https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
  "review_id": 2757802,
  "review_posted_at": "2013-06-21T12:42:33.137",
  "review_updated_at": "2026-07-25T07:52:38.377",
  "summary": "Moving at the speed of light, burn out is inevitable",
  "pros": "1) Food, food, food.  15+ cafes on main campus (MTV) alone.  Mini-kitchens, snacks, drinks ...",
  "cons": "1) Work/life balance.  What balance?  All those perks and benefits are an illusion.  They ...",
  "advice": "1) Don't dismiss emotional intelligence and adaptive leadership.  They're not just catch p ...",
  "rating_overall": 4,
  "rating_work_life_balance": 2.0,
  "rating_recommend_to_friend": "NEGATIVE",
  "rating_business_outlook": "NEGATIVE",
  "job_title": "Program Manager",
  "job_title_id": 61502,
  "job_family_id": 100155,
  "employment_status": "REGULAR",
  "is_current_job": false,
  "length_of_employment": 9,
  "job_ending_year": 2013,
  "language_id": "eng",
  "location_name": "Mountain View, CA",
  "count_helpful": 3878,
  "has_employer_response": false,
  "employer_responses": []
}

Its company's profile row, 19 of its 45 fields, joined on the same employer_id:

{
  "record_type": "company",
  "employer_id": 9079,
  "company_name": "Google Inc.",
  "short_name": "Google",
  "website": "https://goo.gle/4ehVuXi",
  "headquarters": "Mountain View, CA",
  "size": "10000+ Employees",
  "size_category": "GIANT",
  "year_founded": 1998,
  "revenue": "$10+ billion (USD)",
  "primary_industry": "Internet & Web Services",
  "employer_type": "Company - Public",
  "review_count": 71308,
  "reviews_available": 65830,
  "photo_count": 621,
  "ratings": {
    "overall": 4.4,
    "recommend_to_friend": 0.88,
    "ceo_approval": 0.85,
    "culture_and_values": 4.3,
    "diversity_and_inclusion": 4.4,
    "work_life_balance": 4.2,
    "career_opportunities": 4.2,
    "senior_management": 4.0,
    "compensation_and_benefits": 4.5,
    "business_outlook": 0.82,
    "review_count": 71278,
    "ceo_ratings_count": 7699
  },
  "rating_distribution": {
    "one_star": 571,
    "two_star": 555,
    "three_star": 2447,
    "four_star": 7037,
    "five_star": 14385
  },
  "reviews_url": "https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
  "faq_url": "https://www.glassdoor.com/FAQ/Google-Questions-E9079.htm"
}

Exports

JSON keeps nested employer replies, rating breakdowns and award lists intact. Two CSV downloads are offered on the run, one holding the review columns and one the company profile columns, because a single CSV of everything runs to hundreds of columns. Each keeps every row, so the other row kind appears with only its shared columns filled, and nested fields such as ratings, awards and demographics flatten into separate columns. Excel, XML and RSS are available too. Reviews are pushed as they arrive, so a long run can be exported while it is still going.

Pricing

Event	When	Price
Review	Each review row delivered	$0.00099
Actor start	Once per run	$0.00399
Company profile row	Never charged	$0.00

That is 99 cents per 1,000 reviews on the Free plan, falling to 89, 79 and 69 cents per 1,000 on higher Apify plans. Filters and limits reduce what you are charged for, because only delivered review rows are billed.

What you collect	Cost
100 reviews	$0.10
5,000 reviews	$4.95
20,561 reviews, the full Siemens history	$20.36

Each figure adds the one-off $0.00399 start.

Free Apify accounts get a trial: up to 100 reviews per run, for a small number of runs. Company profile rows do not count against it. Paid plans have no cap. Current rates for your plan are on the pricing tab.

FAQ

Can I really get more than 200 reviews for one company? Yes. A recorded Siemens collection returned 20,561 of 20,561 available reviews in one run.

Do I need a Glassdoor account? No. No login, no cookies, nothing to configure.

How do I link reviews to a company? Every review row carries employer_id, company_name, company_short_name and company_reviews_url, and each company gets one profile row with the same employer_id.

What if a company name is ambiguous? The run resolves it to one company and reports which one on the company row, including resolved_from, so you can check what it picked. A name that cannot be resolved is skipped and reported, and the other companies still run.

Can I get reviews for several companies in one run? Yes, up to 200 entries, with maxReviewsPerCompany applying to each and maxReviewsTotal bounding the run.

Why is my run smaller than the review count on the company row? That count is Glassdoor's published total for the whole company. Your filters, limits and the run ceiling all reduce what is collected. When no filter is thinning the set, the run also reports any shortfall it could not retrieve; with a filter on, a smaller count is expected rather than a gap.

Support

Found a field missing, a company that will not resolve, or a result that looks wrong? Open an issue on the Actor's Issues tab with the input you used. We read every one. Agents can add a feedback note to any run instead, including a run that worked.