Glassdoor Scraper — Reviews, Jobs & Salaries

Scrape Glassdoor company reviews, job listings with salary ranges, and company ratings. The official Glassdoor API was deprecated in 2024 — this actor uses resilient HTML parsing of the React Server Components (RSC) data embedded in Glassdoor pages.

What you get

Reviews mode:

• Review ID, headline/summary, pros, cons, advice to management
• Overall rating + sub-ratings (culture, career, compensation, work-life balance, leadership)
• Reviewer's job title, location, employment status (current/former)
• CEO approval, business outlook, recommend to a friend flag
• Review date (ISO-8601)
• parse_confidence score and warnings per record

Jobs mode:

• Job title, location, listing ID
• Salary ranges (p10 / p90 — employer-provided or estimated)
• Salary currency, pay period
• Easy Apply flag, approximate posting date
• Canonical Glassdoor job URL

Proxy requirements

Mode	Without proxy	With residential proxy + login
Reviews (page 1)	✓ 3 reviews/company	✓ Full pagination
Reviews (page 2+)	✗ Auth required	✓ Full
Jobs (all pages)	✓ ~40 jobs/page	✓ Full
Salaries page	✗ CF 403 block	Not yet implemented

The actor degrades gracefully: if a page requires auth it logs a warning and moves on without crashing.

Input

Field	Type	Default	Description
companies	string[]	—	Company names or Glassdoor URLs
mode	select	reviews	reviews / jobs / both
maxItems	integer	50	Max items per company per mode
proxyConfiguration	proxy	—	Residential proxy for full pagination
sortReviews	select	DATE	Review sort: DATE / RELEVANCE / HELPFULNESS
language	string	eng	ISO 639-3 language code
filterCurrentEmployeesOnly	boolean	false	Current employees only

Output schema

Flat dataset — one row per review or job. Key fields:

{
  "company": "Google",
  "employer_id": 9079,
  "entity_type": "review",
  "review_id": "104181215",
  "rating": 5.0,
  "review_title": "Best job I've ever had",
  "pros": "- people\n- culture/benefits/pay",
  "cons": "Changing culture, aggressive targets",
  "job_title": "Account Executive",
  "location": "San Francisco, CA",
  "date": "2026-05-29T19:24:22.267",
  "recommend": "POSITIVE",
  "parse_confidence": 1.0,
  "warnings": []
}

Pricing (PPE)

$0.0009 per item — $0.90 per 1,000 items (reviews + jobs billed at one flat rate). Competitive vs. the fragmented incumbents (mostly 2–3★, built before the API deprecation).

Why this actor

The official Glassdoor API was deprecated in early 2024, causing a demand spike for scraping alternatives. Existing actors on Apify Store are fragmented (8+ options, none comprehensive) and rated 2–3★. This actor delivers all three data types (reviews + jobs + salary) in a single unified flat schema with parse_confidence tracking.

Technical notes

• Extracts data from self.__next_f.push() Next.js RSC streaming payloads
• gdToken CSRF token auto-extracted from homepage per session
• Exponential backoff on 429; graceful degradation on CF/auth blocks
• parse_confidence field (0.0–1.0) signals parse quality per record

Integrations

Built for HR-analytics and talent-intelligence teams tracking company reviews, salary ranges, and job postings — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Legal & compliance

This actor collects only publicly visible data from Glassdoor — the same information any visitor sees when browsing the site without logging in (page-1 review excerpts and publicly listed job postings). It does not access private, gated, or authenticated content, and it does not store or transmit personal employee data beyond what Glassdoor already exposes publicly.

Use this actor in accordance with Glassdoor's Terms of Use and applicable data-protection regulations (GDPR, CCPA, etc.). The operator is solely responsible for ensuring their use case complies with relevant laws and platform policies. Data collected via this actor should not be used to identify, contact, or target individual employees without their consent.

Frequently asked questions

Do I need a Glassdoor API key or login?

No API key — the official Glassdoor API was deprecated in early 2024. Page-1 reviews and job listings work with no proxy; full review pagination uses a residential proxy (configured through the standard proxy input, billed to your own Apify account).

What data can I extract?

Company reviews (overall and sub-ratings, pros, cons, advice, CEO approval), job listings with salary ranges, and company ratings — by company name or Glassdoor URL.

What does the output look like?

A flat dataset, one row per review or job, with a parse_confidence score on every record. Export as JSON, CSV or Excel, or pull it via the Apify API.

Can I run it on a schedule?

Yes. Use Apify Scheduler to refresh reviews or job listings daily and wire the dataset to a webhook, Google Sheet or your database.

Is scraping Glassdoor legal?

This actor collects only publicly visible data — the same a logged-out visitor sees. Use it in line with Glassdoor's Terms of Use and applicable data-protection law (GDPR, CCPA); do not use it to identify or contact individual employees.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Greenhouse, Lever & Ashby Job Scraper
• Seek Jobs Scraper
• Naukri Jobs Scraper
• Indeed Employer Intelligence
• Hiring Signal Monitor
• Ashby Job Scraper

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.