Glassdoor Reviews Scraper — Ratings, Pros, Cons, Job Title

Bulk-download Glassdoor employee reviews for any company: overall rating, every sub-rating, pros, cons, advice, job title, location, employment status, helpful votes and review link. Paste the reviews URL for the cheapest source. No login. For employer-brand research and HR benchmarking.

In short: what you give, what you get, what it costs

You give	A company's Glassdoor reviews link (the cheapest way) or its name
We do	Read the company's employee reviews
You get	One row per review: overall rating and every sub-rating, pros, cons, advice, job title, location, current or former employee, helpful votes and link
It costs	$20 per 1,000 reviews, $4.50 from 21 November 2026, no start fee. Optional scheduled monitoring: +$0.20 per check (one-off runs never pay it). Apify also bills the supplier Actor getdataforme/glassdoor-reviews-scraper that this robot runs for you, at its own price (about $9 per 1,000 reviews). The first 10 results of every run are free of this robot's fee.
It does not	Sign in to Glassdoor

A real result row is shown further down this page.

Free tier — 10 results free on every run

The free results waive this Actor's own fee; the source Actor (getdataforme/glassdoor-reviews-scraper) still bills its own price for every row it returns.

The first 10 results of every run are free, on every run you ever make — not a one-off trial. Run it on your own subjects and see the real output before you are billed for a single result.

One guard: the free allowance never covers more than half of a run, so a run returning 6 results gets 3 free. That keeps the offer sustainable.

What it does

Run it once and get the full, current data as clean rows you can export to JSON, CSV or Excel, or pull through the API. Need only fresh data? Turn on monitorEnabled and schedule it: every later run returns only what is new or changed since the previous one, so you never re-download or re-pay for the same rows.

Compared with kaix/glassdoor-reviews-scraper

Read on the Store on 2026-10-23 (Glassdoor Reviews Scraper - Most Comprehensive). Where it does better, this section says so.

• Data source: this Actor runs getdataforme/glassdoor-reviews-scraper for you, then cleans and checks every row.
• Only here (fields its Store page does not list): change_type (new or changed since your last scheduled run); actionable (needs a reply or action); severity (how serious a complaint is); why_it_matters (one-line reading of the row); signals (what the row reveals).
• This Actor's fee is free on the first results of every run, and a hard cost cap (maxTotalChargeUsd) limits it.
• Price: about $29 per 1,000 in total here ($20 for this Actor + $9 for getdataforme/glassdoor-reviews-scraper), $0.05 at kaix/glassdoor-reviews-scraper.

Why this scraper

1. Try before you pay. The first 10 results of every run are free, on every run.
2. Pay only for data delivered. Failed sources and failed inputs are never billed.
3. Hard cost cap. Set maxTotalChargeUsd and a run can never cost more than you allow.
4. Only-new mode. With monitorEnabled on a schedule, you get just the new or changed rows, plus a spike signal when volume suddenly jumps.
5. Clean, normalized rows with the same field names every run, plus a RUN_SUMMARY.json — ready for spreadsheets, databases, n8n/Make/Zapier and AI agents (MCP).

Example input

{
  "companies": [
    "Google",
    "Stripe"
  ],
  "maxTotalChargeUsd": 1.0
}

This returns the full current data right away. To receive only what is new on a schedule, add "monitorEnabled": true: the first monitored run stores a free baseline, later runs return only new or changed rows. Pin a monitorId to keep separate targets from sharing state.

Output

Each row is one employee review. A real row from a run on Apify (long texts are cut here and people's names hidden; your dataset has them in full):

A real row from this Actor's latest run on Apify (2026-10-24; long texts are cut and people's names hidden here, your dataset has them in full):

{
  "review_id": "104025813",
  "subject_id": "https://www.glassdoor.com/Reviews/Nucor-Reviews-E489.htm",
  "rating": 5,
  "text": "Great work-life balance with excellent perks | Great worklife balance, bonus, gym, nurses station | There are none that I can think of",
  "published_at": "2026-05-19T05:42:21.183",
  "reviewId": 104025813,
  "url": "https://www.glassdoor.com/Reviews/Employee-Review-Nucor-RVW104025813.htm",
  "postedAt": "2026-05-19T05:42:21.183",
  "content_summary": "Great work-life balance with excellent perks",
  "content_pros": "Great worklife balance, bonus, gym, nurses station",
  "content_cons": "There are none that I can think of",
  "content_languageId": "eng",
  "ratings_overall": 5,
  "ratings_workLifeBalance": 5,
  "ratings_cultureAndValues": 5,
  "ratings_seniorLeadership": 4,
  "ratings_compensationAndBenefits": 5,
  "ratings_careerOpportunities": 5,
  "ratings_diversityAndInclusion": 3,
  "ratings_ceo": "APPROVE",
  "ratings_outlook": "POSITIVE",
  "ratings_recommendation": "POSITIVE",
  "employment_isCurrentJob": false,
  "employment_status": "REGULAR",
  "employment_employmentLength": 20,
  "employment_jobEndingYear": 2025,
  "employment_jobTitle_id": 1083,
  "employment_jobTitle_text": "Contract Specialist",
  "employment_location_id": 1145705,
  "employment_location_name": "Cincinnati, OH",
  "employment_location_type": "CITY",
  "engagement_helpful": 0,
  "engagement_unhelpful": 0,
  "engagement_featured": false,
  "employer_id": 489,
  "employer_shortName": "Nucor",
  "employer_name": "Nucor Corporation",
  "employer_logoUrl": "https://media.glassdoor.com/sql/489/nucor-squareLogo-1645798378058.png",
  "employer_hasResponse": false,
  "meta_isLegal": true,
  "meta_isCovid19": false,
  "meta_isLanguageMismatch": false,
  "meta_reviewDetailUrl": "/Reviews/Employee-Review--RVW104025813.htm",
  "meta_topLevelDomainId": 1,
  "meta_scrapedAt": "2026-10-24T18:59:35.956Z",
  "record_id": "104025813",
  "change_type": "new",
  "actionable": false,
  "topic": "other",
  "severity": "minor",
  "why_it_matters": "New positive review — no action needed."
}

Field	What it holds
employer_short_name	company
rating_overall	overall rating, 1 to 5
summary	review headline
pros	what employees like
cons	what employees dislike
advice	advice to management
job_title	reviewer's job title
location	office location
is_current_job	still works there
length_of_employment	years at the company
rating_work_life_balance	work-life balance score
rating_compensation_and_benefits	pay and benefits score
rating_senior_leadership	leadership score
rating_recommend_to_friend	would recommend to a friend
review_date_time	review date

Each row also keeps the other fields the source returns, so you get everything above and more. In the Output tab, the Overview table shows these columns, readable without any code; export them as CSV, Excel, JSON or HTML.

Every field

Field	What it holds
review_id	review ID
subject_id	the company, place or product the review belongs to
rating	rating
text	text
published_at	published at
reviewId	review ID
url	URL
postedAt	posted at
content_summary	content summary
content_pros	content pros
content_cons	content cons
content_languageId	content language ID
ratings_overall	ratings overall
ratings_workLifeBalance	ratings work life balance
ratings_cultureAndValues	ratings culture and values
ratings_seniorLeadership	ratings senior leadership
ratings_compensationAndBenefits	ratings compensation and benefits
ratings_careerOpportunities	ratings career opportunities
ratings_diversityAndInclusion	ratings diversity and inclusion
ratings_ceo	ratings ceo
ratings_outlook	ratings outlook
ratings_recommendation	ratings recommendation
employment_isCurrentJob	employment is current job
employment_status	employment status
employment_employmentLength	employment employment length
employment_jobEndingYear	employment job ending year
employment_jobTitle_id	employment job title ID
employment_jobTitle_text	employment job title text
employment_location_id	employment location ID
employment_location_name	employment location name
employment_location_type	employment location type
engagement_helpful	engagement helpful
engagement_unhelpful	engagement unhelpful
engagement_featured	engagement featured
employer_id	employer ID
employer_shortName	employer short name
employer_name	employer name
employer_logoUrl	employer logo URL
employer_hasResponse	employer has response
meta_isLegal	meta is legal
meta_isCovid19	meta is covid19
meta_isLanguageMismatch	meta is language mismatch
meta_reviewDetailUrl	meta review detail URL
meta_topLevelDomainId	meta top level domain ID
meta_scrapedAt	meta scraped at
record_id	stable ID of the row (used for new-only delivery)
change_type	new / changed / removed since your last run
actionable	true when the row calls for a reply or action
signals	detected signals (complaint, praise, fake-review burst...)
topic	what the review is about (pay, service...)
severity	how urgent the review is
why_it_matters	one-line reason this row deserves attention
content_advice	content advice

How you're charged

• Source Actor charge: this Actor runs a source Actor inside your run, and Apify bills that run to your account at the source's own price, on top of this Actor's price. A Glassdoor reviews URL uses kaix/glassdoor-reviews-scraper (about $0.05 per 1,000 reviews, read on 2026-10-24); a plain company name uses getdataforme/glassdoor-reviews-scraper (about $9 per 1,000 reviews plus a start fee). Paste the URL to pay the least. Each call is capped at $0.50.
• Review — $0.02 per review from 22 October 2026 (free before that date), billed only for results delivered to your dataset. With monitoring on, only results that are new since the last check are delivered and billed.
• Monitoring check ($0.20) — only when you turn on scheduled monitoring (monitorEnabled), once per target per run, and only when the source responded. One-off runs never pay it: you pay only per result.
• One-off runs pay only per result. The monitoring check applies only to scheduled monitoring; every upstream call is capped, so a run can never cost more than you allow.

Related tools

• Already have review exports and just need dedup + new-review detection? → Review Cleaner & Deduplicator - Fakes, Duplicates, Score

How to use it in 3 steps

1. Click Try for free and fill in Company names (companies).
2. Optional: adjust the other fields; every one has a description and a sensible default.
3. Click Start and download the results as JSON, CSV or Excel, or read them from the API.

Use cases

• Employer-brand research
• HR benchmarking
• Sales intelligence

Use it from code (API)

Every Apify Actor is an API. Start a run and read the results with one call.

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("om_kh/glassdoor-reviews-scraper").call(run_input={"companies": ["Google", "Stripe"]})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('om_kh/glassdoor-reviews-scraper').call({
    "companies": [
        "Google",
        "Stripe"
    ]
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

One HTTP call (cURL) — runs the Actor and returns the dataset in the same response:

curl -X POST "https://api.apify.com/v2/acts/om_kh~glassdoor-reviews-scraper/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"companies": ["Google", "Stripe"]}'

Integrations: n8n, Make, Zapier, Google Sheets

• n8n — the official Apify node, operation Run Actor and get dataset, Actor om_kh/glassdoor-reviews-scraper.
• Make — Apify › Run an Actor, then Apify › Get Dataset Items.
• Zapier — Apify › Run Actor; map the dataset items into your next step.
• Google Sheets, webhooks, Slack — add an integration from the Integrations tab of this Actor.
• Schedules — run it every hour or day from Schedules in Apify Console.

Use it from an AI agent (MCP and x402)

Connect Claude Desktop, Cursor, VS Code or any MCP client to Apify's MCP server at https://mcp.apify.com and add this Actor, om_kh/glassdoor-reviews-scraper, as a tool — the agent passes the same JSON as the run input and gets the dataset back.

AI agents that pay with x402 can run it too, without an Apify account: the Actor charges only per result delivered, so an agent pays exactly for what it gets.

FAQ

Is it legal to scrape Glassdoor? This Actor collects only publicly available Glassdoor data and never logs in. You are responsible for how you use it; check Glassdoor's terms and your local law (GDPR, CCPA) if you store personal data, and ask a lawyer if unsure.

Do I need an account, cookies or an API key? No. You only need an Apify account to run it.

What data do I get? Employee reviews with rating, pros, cons, job title and date. Download it as JSON, CSV, Excel, HTML or XML, or read it from the API.

Am I charged when something fails? No. An input that fails is listed with its reason in the run summary and is never charged.

Can I run it on a schedule? Yes. Create a schedule in Apify Console (hourly, daily, weekly) and connect webhooks, Google Sheets, Slack, n8n, Make or Zapier.

Can I use it from Python, JavaScript or an AI agent? Yes: see the code samples above, or connect AI agents through MCP with the glassdoor_new_reviews tool.

All input fields

The main fields are described above; these are the others the form accepts:

• currentReviews (Bring your own reviews (optional)) — Skip the chained fetch and pass review data directly (no upstream call, no fetch fee).
• previousReviews (Previous reviews (non-monitor mode)) — Prior-run reviews; used to compute the delta when not in persistent monitor mode.

Your feedback

Did this Actor save you time? A short review on the Reviews tab helps other people find it, and every review is read. Something missing or broken? Open an issue on the Issues tab: it is answered and fixed fast.

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