Kununu Reviews Scraper — Employee Ratings & Feedback

Extract public Kununu employer reviews into structured JSON for DACH employer-brand monitoring, recruiting intelligence, culture research, and AI-agent workflows. Each dataset item is one public employee review with company context, rating, title, text, date, and a recommendation signal when Kununu displays it.

Use this actor for public employer review pages on kununu.com. It is designed as a focused public-review workflow alongside job, salary, and account-specific data tools.

Best fit

Use direct Kununu /kommentare URLs when you want a reproducible view of public employee feedback. This works well for tracking a company over time, comparing competitor sentiment, or supplying compact evidence to a recruiting or employer-brand analysis workflow.

For Glassdoor-specific reviews, continue with the Glassdoor Reviews Scraper when you need a cross-platform employer comparison. For ratings from local customers rather than employees, continue with the Google Maps Reviews Scraper.

Workflow story

An employer-brand team starts with the canonical Kununu review URL for a company it monitors. It collects a bounded review sample, then groups rating, reviewText, and recommended by month to identify recurring themes. Next, it passes the preserved sourceUrl and scrapedAt values to its internal report so every conclusion stays traceable to the public source page.

Input

{
    "companyUrls": ["https://www.kununu.com/at/kununu/kommentare"],
    "maxReviewsPerCompany": 25,
    "maxResults": 50,
    "ratingFilter": "all",
    "responseFormat": "detailed"
}

companyUrls accepts public German, Austrian, and Swiss Kununu employer review pages. maxResults is a global cap and also bounds the number of billable review events. The actor collects pages in their public page order; use ratingFilter to retain scores rounded to one whole rating after extraction.

Output

{
    "recordType": "review",
    "reviewId": "123ba638-1ded-4a7b-886c-26f26c7798b5",
    "companyName": "Example GmbH",
    "companyUrl": "https://www.kununu.com/at/example/kommentare",
    "companyRating": 4.2,
    "companyReviewCount": 16,
    "rating": 4,
    "reviewTitle": "Very good team",
    "reviewText": "Helpful colleagues. Clear feedback.",
    "author": "anonymous",
    "reviewDate": "2026-07-01T00:00:00.000Z",
    "recommended": true,
    "sourceUrl": "https://www.kununu.com/at/example/kommentare",
    "scrapedAt": "2026-07-19T14:17:21.000Z"
}

The dataset contains only review rows. The default key-value store holds OUTPUT and RUN_SUMMARY, each with a machine-readable outcome: COMPLETE, PARTIAL, VALID_EMPTY, INVALID_INPUT, or UPSTREAM_FAILED.

Field	Use in analysis
rating	Measure individual employee sentiment.
reviewText	Identify recurring culture and workplace themes.
recommended	Compare recommendation signals when publicly shown.
sourceUrl and scrapedAt	Preserve provenance and collection time.

API example

curl -X POST 'https://api.apify.com/v2/acts/khadinakbar~kununu-reviews-scraper/runs?token=YOUR_APIFY_TOKEN' \
  -H 'content-type: application/json' \
  -d '{
    "companyUrls": ["https://www.kununu.com/at/kununu/kommentare"],
    "maxReviewsPerCompany": 10,
    "maxResults": 10
  }'

Replace YOUR_APIFY_TOKEN with an environment-managed token and keep it in your secure secret store.

Pricing

This actor uses Pay per event + platform usage:

Event	Price
Actor start	$0.00005 per run
Kununu review extracted	$0.025 per unique review

The user also pays underlying Apify platform usage. The Actor charges only for unique review rows, while status-summary records are unbilled. Consult the live Pricing tab for the current effective price and platform-usage configuration before scheduling a large collection.

Best-results guidance

• Provide the canonical /kommentare page for each company.
• Start with a small review sample to validate a company and output shape, then raise the cap for longitudinal analysis.
• Keep the default DACH residential proxy settings, which are designed for Kununu's CloudFront-delivered pages.
• Treat public review content as point-in-time feedback. Preserve the sourceUrl and scrapedAt fields when sharing an analysis.

AI-agent prompt card

As an AI agent, fetch a bounded set of public Kununu employee reviews for the supplied company review URL. Return the company rating, review count, individual star rating, title, text, date, recommendation signal, source URL, and collection time. Read back OUTPUT.outcome before drawing a sentiment conclusion and report the configured cost scope.

Builder's note

I designed this actor around Kununu's public, server-rendered structured review content rather than a private endpoint. That makes provenance visible in each dataset row and keeps the contract focused on public employer reviews. The actor records an explicit outcome and machine-readable summary alongside the review dataset, which makes downstream monitoring and data-quality checks straightforward.

Legal and responsible use

Only collect and process public data you are allowed to access and use. You are responsible for complying with Kununu's terms, applicable privacy and employment laws, copyright/database-right restrictions, and any obligations around sharing or profiling employee-review content.