# Glassdoor Company Reviews Scraper (`devilscrapes/glassdoor-reviews-scraper`) Actor

Scrape Glassdoor company reviews by company name: rating breakdowns, pros/cons, job title, employment status, review date, and CEO/recommend-to-friend signals, plus a company rating summary. Camoufox-powered to clear the target's bot defenses. Pay only per row scraped.

- **URL**: https://apify.com/devilscrapes/glassdoor-reviews-scraper.md
- **Developed by:** [DevilScrapes](https://apify.com/devilscrapes) (community)
- **Categories:** Jobs
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

<div align="center">
  <img src=".actor/icon.svg" width="160" alt="Devil Scrapes mark" />

## Glassdoor Company Reviews Scraper

**💰 $10.00 / 1 000 review rows**  ·  pay only for results  ·  no credit card to try

*The devil's in the data — we just get you the data.* 😈

Give this Actor a company name and get back structured Glassdoor reviews: rating breakdowns, pros/cons, job title, employment status, review date, and CEO/recommend-to-friend signals — plus a company-level rating summary.

</div>

***

### 🎯 What this scrapes

For each company you request, this Actor visits Glassdoor's public company Overview page and its Reviews pages and emits two kinds of rows into one dataset:

1. **`company_summary`** — one row per company: overall rating, CEO approval, business outlook, and recommend-to-friend percentage.
2. **`review`** — one row per individual review: overall + sub-category ratings (work-life balance, culture, career opportunities, compensation, senior management, diversity), pros, cons, advice to management, job title, employment status, review date, and helpful-vote counts.

No login, no account, no reviewer de-anonymization — only what Glassdoor's public pages show anonymously.

### 🔥 Features

- **We clear the bot defenses guarding Glassdoor's review pages** — a hardened browser layer with a proven navigation sequence gets through where a plain HTTP request gets turned away.
- **We rotate proxy sessions on every retry** — a fresh exit IP and session whenever a company's fetch fails.
- **Per-company fault isolation** — one company that fails to resolve or parse never fails the whole run; it's logged and skipped, and every other company still completes.
- **Pydantic v2 input + output validation** — every row is a typed, schema-enforced record; fields Glassdoor doesn't expose come back `null`, never guessed.
- **Pay-Per-Event pricing** — you're charged per row actually written to your dataset, not per request attempted.
- **Honest throughput** — Glassdoor's own anonymous-access limits cap full review text at a few reviews per page load; `maxReviewsPerCompany` and pricing reflect that reality rather than promising cheap bulk collection we can't deliver without a login we refuse to use.

### 💡 Use cases

- **Employer-brand monitoring** — track how your own company's reviews and ratings trend over time.
- **Recruiting intelligence** — pull culture and compensation signals for target companies before an outreach campaign.
- **Competitive talent research** — compare rating breakdowns and CEO approval across a peer set.
- **Investment due diligence** — culture and leadership signals as a qualitative input alongside financials.

### ⚙️ How to use it

1. Click **Try for free** at the top of the page.
2. Enter one or more `companyNames` — or, if you already know Glassdoor's internal `employerId` for a company, pass `employerIds` positionally alongside it to skip resolution entirely.
3. Set `maxReviewsPerCompany` to the depth you need (higher costs more — see Pricing).
4. Click **Start**. Output streams into the run's dataset.
5. Export from **Storage → Dataset** as JSON, CSV, or Excel — or fetch via the API.

### 📥 Input

| Field | Type | Required | Default | Notes |
|---|---|:--:|---|---|
| `companyNames` | `array` | yes | — | Company names to scrape, e.g. `["Google", "Meta"]`. |
| `employerIds` | `array` | no | — | Known Glassdoor employerId values, paired positionally with `companyNames`. Skips resolution — the most reliable path. |
| `maxReviewsPerCompany` | `integer` | no | `30` | 3-500. Anonymous access renders ~3 full reviews per page load; higher values cost proportionally more. |
| `includeCompanySummary` | `boolean` | no | `true` | Emit one `company_summary` row per company — no extra request. |
| `countryCode` | `string` | no | `"US"` | 2-letter ISO — pinned proxy exit country. |
| `proxyConfiguration` | `object` | no | `{"useApifyProxy": true}` | Apify Proxy editor. |

#### Example input

```json
{
  "companyNames": ["Google"],
  "employerIds": [9079],
  "maxReviewsPerCompany": 30,
  "includeCompanySummary": true,
  "countryCode": "US"
}
```

### 📤 Output

Every row is one dataset item — a `review` or a `company_summary`, distinguished by `row_type`.

| Field | Type | Notes |
|---|---|---|
| `row_type` | string | `"review"` or `"company_summary"`. |
| `company_name` / `employer_id` | string / integer | Requested company and resolved Glassdoor id. |
| `review_id` | string | null | Null for `company_summary` rows. |
| `review_date` | string | null | ISO-8601. |
| `job_title` / `employment_status` | string | null | Reviewer's stated role and status. |
| `overall_rating` + 6 sub-ratings | float | null | Work-life balance, culture, career, compensation, senior management, diversity. |
| `pros` / `cons` / `advice_to_management` / `summary` | string | null | Review text fields. |
| `recommend_to_friend` / `ceo_approval_response` | boolean | null | Per-review signals, when the reviewer set them. |
| `count_helpful` / `count_not_helpful` | integer | null | Helpful-vote counts. |
| `company_overall_rating`, `company_ceo_approval_pct`, `company_rating_business_outlook`, `company_recommend_to_friend_pct` | float | null | `company_summary` aggregate fields; null on `review` rows. |
| `source_url` / `scraped_at` | string | Fetched page URL, ISO-8601 timestamp. |

#### Example output

```json
{
  "row_type": "review",
  "company_name": "Google",
  "employer_id": 9079,
  "review_id": "105125064",
  "review_date": "2026-08-09T23:03:51.940",
  "job_title": "Hair Transplant Technician",
  "employment_status": "Current Employee, 2 year(s)",
  "overall_rating": 3,
  "pros": "Good learning experience and flexible working environment.",
  "cons": "Long working hours and sometimes limited career growth.",
  "advice_to_management": "Provide better training opportunities.",
  "count_helpful": 0,
  "count_not_helpful": 0,
  "source_url": "https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
  "scraped_at": "2026-08-12T09:00:00+00:00"
}
```

### 💰 Pricing

Pay-Per-Event — you pay only when these events fire:

| Event | USD | What it is |
|---|---:|---|
| `actor-start` | $0.02 | One-off warm-up charge per run. |
| `review-row` | $0.01 | Per review row written to your dataset. |
| `company-summary-row` | $0.01 | Per company-summary row written (when `includeCompanySummary=true`). |

Example: 1 000 review rows ≈ **$10.00**. This sits at the top of our usual $1-10/1,000 range on purpose — Glassdoor renders full review text only a few rows per page load without a login, and we refuse to use one, so collecting real volume costs real browser-navigation compute. No subscription, no minimum, no card to start.

### 🚧 Limitations

Anonymous access only — no login, ever. Glassdoor renders roughly 3 full reviews per page load for unauthenticated visitors, so `maxReviewsPerCompany` above a few hundred means real added runtime and cost, not a cheap bulk export. Company-name-to-`employerId` resolution is still being hardened — pass `employerIds` directly for the most reliable results today. Some review sub-fields (recommend-to-friend, CEO-approval vote) are only present when a reviewer chose to set them and come back `null` otherwise.

### ❓ FAQ

**Is this legal?**

We only fetch content Glassdoor makes publicly available on its company and review pages, anonymously — no login, no reviewer de-anonymization. Respect Glassdoor's terms of service before using output commercially.

**Why is a field `null`?**

Either Glassdoor didn't collect that field for a given review (recommend-to-friend and CEO-approval votes are optional for reviewers), or resolution for that company failed and it was skipped — check the run's status message for a breakdown.

**Do I need my own proxy?**

No — a proxy is wired in by default; `proxyConfiguration` is optional if you want to bring your own.

**Why did I get fewer reviews than `maxReviewsPerCompany`?**

Glassdoor stops rendering full review text past a point for anonymous visitors on some companies; the Actor stops cleanly and reports how many rows it actually collected rather than erroring out.

### 💬 Your feedback

Spotted a bug, hit a weird edge case, or need a new field? Open an
issue on the Actor's **Issues** tab on Apify Console — we ship
fixes weekly and we read every report.

***

<div align="center">

Built by **[Devil Scrapes](https://apify.com/DevilScrapes)** 😈 — a small fleet of
opinionated public-data Actors. Honest pricing, real engineering, zero fine print.

</div>

# Actor input Schema

## `companyNames` (type: `array`):

Company names to scrape reviews for, e.g. \['Google', 'Meta']. Required.

## `employerIds` (type: `array`):

Power-user shortcut: known Glassdoor employerId values, paired positionally with companyNames (one employerId per company). Skips company-search resolution entirely — the always-reliable path.

## `maxReviewsPerCompany` (type: `integer`):

Hard cap on review rows fetched per company. Anonymous (no-login) access renders only ~3 full reviews per Reviews-page load — each page costs one full browser navigation pair, so higher values cost proportionally more compute and review-row events.

## `includeCompanySummary` (type: `boolean`):

When true, emits one company\_summary row per company from the mandatory warm-up page's aggregate rating fields — no extra request.

## `countryCode` (type: `string`):

Pinned proxy exit country (2-letter ISO), never geo-random — Glassdoor locale-routes by exit geography.

## `proxyConfiguration` (type: `object`):

Apify Proxy configuration. WEBSHARE\_PROXY\_URL env var wins over Apify Proxy whenever set.

## Actor input object example

```json
{
  "companyNames": [
    "Google"
  ],
  "employerIds": [
    9079
  ],
  "maxReviewsPerCompany": 30,
  "includeCompanySummary": true,
  "countryCode": "US",
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `datasetItems` (type: `string`):

All dataset items as JSON.

## `datasetItemsCsv` (type: `string`):

Same data exported to CSV.

## `datasetView` (type: `string`):

Open the run dataset in the Console.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "companyNames": [
        "Google"
    ],
    "employerIds": [
        9079
    ],
    "proxyConfiguration": {
        "useApifyProxy": true
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("devilscrapes/glassdoor-reviews-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "companyNames": ["Google"],
    "employerIds": [9079],
    "proxyConfiguration": { "useApifyProxy": True },
}

# Run the Actor and wait for it to finish
run = client.actor("devilscrapes/glassdoor-reviews-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "companyNames": [
    "Google"
  ],
  "employerIds": [
    9079
  ],
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}' |
apify call devilscrapes/glassdoor-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,devilscrapes/glassdoor-reviews-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Hl3Tih4wPzgHYQsYQ/builds/RLsFGaO7phLgcyCiX/openapi.json