Kununu Scraper - Company Ratings & Employer Reviews (DACH)

What does Kununu Scraper do?

Kununu Scraper extracts structured employer reputation data from kununu.com across Germany, Austria, and Switzerland (DACH). It returns company ratings broken down by category, individual employee reviews, applicant/interview reviews with hire-or-rejected outcomes, salary ranges, benefits, awards, social-media handles, structured addresses, and a 24-month score trend benchmarked against the company's industry. Inputs accept a keyword, a list of company names, direct kununu URLs, or an existing Apify dataset — and incremental mode lets you re-run the same input to track only what has changed.

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Key features

• 🎤 Applicant interview reviews — toggle includeApplicantReviews to paginate kununu's /bewerbung feed for individual applicant reviews. Each entry includes the application year, the outcome (hired / rejected / offerDeclined), applicant-recalled interview questions, and applicant-specific rating dimensions (professionalism, responsiveness, interview atmosphere, etc.). Cap pagination depth with maxApplicantReviewPages.
• 📈 24-month score trend + industry benchmark — every detail-enriched company carries scoreTrend.newerThan24Months vs olderThan24Months with the delta, plus industryAverageScore — so you see at a glance whether reputation is rising or falling and how far above/below the industry baseline a company sits. Pairs with responseTimeInDays (HR engagement KPI) and firstReviewYear (profile credibility / age signal).
• ⭐ 4-bucket rating distribution — toggle includeRatingBuckets to get the 4-bucket star distribution (excellent 4-5★, good 3-4★, satisfactory 2-3★, subpar 1-2★) with per-bucket review counts and percentages — separately for employee reviews and applicant reviews. Catches polarised companies that average to "OK" while actually splitting into two camps.
• 🏆 Awards & badges with year history — every detail-enriched company emits awards[] with kununu Top Company seals, category Top Rated badges, and third-party awards (e.g. Die Zeit Most Wanted Employer). Each award groups its per-year badges with the badge image URL and a wasPaid flag — so you can distinguish earned reputation signals from sponsored placements.
• 📍 Structured addresses & company facts — detail enrichment surfaces profileLocations.locations[] with the company's curated HQ address (street, postal, city, country, lat/lng) and optional companyFacts.numberOfEmployees + companyFacts.revenue self-reported by the company — usable directly for CRM enrichment or geo-coded lead scoring without a separate geocoding step.
• ♻️ Incremental mode — recurring runs emit only listings whose ratings, reviews, or metadata changed — track reputation movement over time without re-processing the universe. Saves 80–95% on monitoring runs.
• 🔔 Notifications — Telegram, Slack, Discord, WhatsApp Cloud API, and generic webhook out of the box. Pair with incremental for daily new-listing alerts without pipeline glue.
• 📦 Compact mode — compact mode — core fields only, ideal when you're feeding an LLM or building a comparison sheet across many companies.
• 📤 Export anywhere — Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).
• 📧 Email + phone extraction — best-effort regex extraction of contact emails and phone numbers from descriptions — emitted as extractedEmails[] and extractedPhones[] on every record.

What data can you extract from kununu.com?

Every run emits one row per matched company. When optional flags are enabled, additional rows are emitted for individual reviews. Each company row is grouped into the following field categories:

• Identity — companyName, simpleName, slug, uuid, portalUrl, countryCode, industry, location, profileType, previewType (EBP_Pro / EBP / Claimed / Non_EBP).
• Match quality (keyword/companyNames/datasetId modes) — matchStatus, matchConfidence, queryCompanyName.
• Rating + trend — rating, ratingRounded, reviewCount, industryAverageScore, scoreTrend (newer vs older 24-month averages with delta), firstReviewYear.
• Rating distribution (with includeRatingBuckets) — ratingBuckets.employees and ratingBuckets.applicants, each with excellent / good / satisfactory / subpar buckets carrying percentage and totalReviews.
• Recommendation — recommendationRate, recommendationTotalReviews, recommendationRecommended, recommendationNotRecommended, responseTimeInDays.
• Review volume splits — totalReviewsEmployees, totalReviewsCandidates, totalReviewsLastTwoYears, totalReviewsWithResponseLastTwoYears, totalNegativeReviewsLastTwoYears, totalReviewsWithText, totalCultureReviews, totalEmployerReviewsWithText, totalApplicationReviewsWithText, totalApprenticeshipReviewsWithText.
• Score breakdown (with includeDetails) — scoreBreakdown[] with per-category scores (career, culture, work-environment, diversity) and nested factor-level scores.
• Benefits + salary — benefits[] (with percentage of reviews mentioning each), salaryRanges[], salarySatisfaction. includeFullSalary extends salary ranges to the full per-job-title list.
• Awards + verification — awards[] (kununu Top Company seals, Top Rated badges, third-party awards with per-year badges + image URLs + wasPaid), isTopCompany, isVerified, isClaimed, isTopCompanyPaid.
• Company facts + social — companyFacts.numberOfEmployees, companyFacts.revenue, officialSocialMedia (curated handles for Facebook, X/Twitter, YouTube, Instagram, Xing, LinkedIn).
• Locations — locationSummary (main city + state + additional locations count), hasLocationsContent, and profileLocations.locations[] with structured address, postalCode, city, country, latitude, longitude, optional website / email / phone, plus a German freeText footprint description.
• Contact (with includeContactData=true, default on) — extractedEmails, extractedPhones, socialProfiles (regex-discovered from page HTML).
• Culture (with includeCulture) — kulturKompass with Kulturkompass compass score, MODERN/TRADITIONAL classification, 4 culture dimensions, strength/weakness factors, statements, and tagged review comments.
• Rich content (with includeRichContent) — richContent block carrying slogan, narrative HTML (whatIsSpecial, benefitsStatement, whoWeAre, whoWeAreLookingFor, legalInformation), image gallery, video gallery, and jobInsights Q&A interviews with named employees. HTML-heavy; off by default. The HTML strings are emitted as the company submitted them to kununu — sanitise/escape on your end before rendering in a browser, an email template, or an LLM prompt.
• Individual reviews (with includeReviews=true) — separate dataset rows with type: "review". reviewType: "employer" covers employees / apprentices / interns / students / managers (discriminated by the position field); fields include title, score, roundedScore, ratings[] per dimension, textsPositive, textsNegative, textsSuggestion, recommended, former, responses[] (company replies).
• Applicant reviews (with includeApplicantReviews=true) — same type: "review" rows with reviewType: "application" carrying applicant-specific fields: applicantYear, applicantResult (e.g. "hired" / "rejected" / "offerDeclined"), interviewQuestions[] (applicant-recalled question text), and applicant rating dimensions (professionalism, responsiveness, predictability, interviewAtmosphere, etc.).
• Incremental metadata (with incrementalMode=true) — changeType (NEW / UPDATED / UNCHANGED), scrapedAt, and a content-hash that detects same-length text edits.

In standard mode, all fields are always present — unavailable data points return null, never omitted. In compact mode, only core fields are returned.

Input

The main inputs are a search keyword and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• mode — Which input to use. 'keyword' paginates search results for a query. 'companyNames' looks up a list of company names. 'companyUrls' uses direct kununu URLs. 'datasetId' reads names from an existing Apify dataset. Leave blank to auto-detect from the fields you fill in. (default: "keyword")
• query — Search term for keyword mode (e.g. 'software'). Required when mode=keyword. Leave blank for other modes.
• companyNames — List of company names to look up on kununu. Best match from first search page is returned.
• companyUrls — List of kununu company URLs (e.g. https://www.kununu.com/de/bosch-gruppe). Country code and slug are parsed from each URL — URLs can mix DE/AT/CH. Skips search and matching. Incremental mode works per-slug.
• startUrls — Alias for Company URLs. Paste direct kununu company URLs (e.g. https://www.kununu.com/de/bosch-gruppe). If set, the actor uses URL mode automatically.
• datasetId — ID of an existing Apify dataset. Actor reads company names from this dataset and enriches with kununu ratings.
• companyFieldName — Field in the dataset that contains the company name. Default: companyName. (default: "companyName")
• deduplicateField — Optional field to deduplicate by before lookup (e.g. 'companyId'). Useful when multiple jobs share the same company.
• countryCode — Kununu country: de (Germany), at (Austria), ch (Switzerland). (default: "de")
• industryId — Filter results by industry ID. Only applies in keyword search mode. Common IDs: 1=Banking, 2=Construction, 3=Consulting, 4=Education, 5=Publishing, 6=Finance, 7=Healthcare, 8=Insurance, 9=Pharma, 10=IT/Software, 11=Engineering, 12=Automotive, 13=Chemicals, 14=Transport, 15=Retail, 16=Telecom, 17=Energy, 18=Media, 19=Public sector, 20=NGO, 43=Hotels, 44=Gastronomy. Find other IDs by filtering on kununu.com and checking the URL.
• maxResults — Maximum number of results to return. 0 = no limit. This is the primary cost control — Apify charges per emitted result event. (default: 100)
• maxPages — Maximum number of search result pages to scrape in keyword mode. 0 = no limit. Defensive safety bound — maxResults is the primary record cap. (default: 10)
• ...and 27 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "mode": "keyword",
  "query": "software",
  "maxResults": 50
}

Incremental tracking — Only emit reviews that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed.

{
  "query": "software",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "software-tracker"
}

Compact output for AI agents — Return only core fields for AI-agent and MCP workflows.

→ Small payload with the most important fields — ideal for piping into LLMs without token overhead.

{
  "query": "software",
  "maxResults": 50,
  "compact": true
}

Full enrichment — Pull awards, score trends, structured addresses, official social handles, rating-distribution buckets, and per-category scores for a list of named companies.

→ Adds the Tier-A enrichment block (scoreTrend, industryAverageScore, awards, companyFacts, officialSocialMedia, locationSummary, profileLocations) plus 4-bucket rating distribution. One request per company beyond the base lookup.

{
  "mode": "companyNames",
  "companyNames": [
    "Bosch Gruppe",
    "Siemens",
    "SAP SE"
  ],
  "includeDetails": true,
  "includeRatingBuckets": true
}

Applicant interview insights — Get individual applicant reviews with interview questions, hire/reject outcomes, and application-year.

→ Auto-enables includeReviews and includeDetails. Emits one company row plus N applicant-review rows per company (reviewType: "application"). Cap depth with maxApplicantReviewPages (default 10 pages ≈ 100 reviews per company).

{
  "mode": "companyUrls",
  "companyUrls": [
    "https://www.kununu.com/de/bosch-gruppe"
  ],
  "includeApplicantReviews": true,
  "maxApplicantReviewPages": 5
}

Output

Each run produces a dataset of structured job records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example job record

{
  "type": "company",
  "companyName": "Software AG Deutschland",
  "slug": "software-ag",
  "uuid": "5da1ad26-46cd-4955-97fb-7ec2b68f0ca7",
  "portalUrl": "https://www.kununu.com/de/software-ag",
  "matchStatus": null,
  "matchConfidence": null,
  "queryCompanyName": null,
  "rating": 3.6,
  "ratingRounded": 3.5,
  "reviewCount": 328,
  "recommendationRate": 26,
  "recommendationTotalReviews": 38,
  "recommendationRecommended": 10,
  "recommendationNotRecommended": 28,
  "industry": "GENERAL_IND_IT",
  "location": "Darmstadt",
  "country": "Deutschland",
  "countryCode": "de",
  "isTopCompany": false,
  "isVerified": true,
  "profileType": "claimed",
  "totalJobs": 0,
  "website": "https://www.softwareag.com/de_de/company.html",
  "followerCount": 286,
  "logoUrl": null,
  "coverImageDesktop": null,
  "coverImageMobile": null,
  "searchImage": null,
  "extractedEmails": [],
  "extractedPhones": [],
  "socialProfiles": [],
  "scoreBreakdown": [
    {
      "id": "AP_CATEGORY_CAREER",
      "score": 3.3,
      "roundedScore": 3.5,
      "totalReviews": 289,
      "factors": [
        {
          "id": "salary",
          "score": 3.5,
          "roundedScore": 3.5,
          "totalReviews": 288
        },
        {
          "id": "image",
          "score": 3.3,
          "roundedScore": 3.5,
          "totalReviews": 286
        },
        {
          "id": "career",
          "score": 3.1,
          "roundedScore": 3,
          "totalReviews": 289
        }
      ]
    },
    {
      "id": "AP_CATEGORY_CULTURE",
      "score": 3.7,
      "roundedScore": 3.5,
      "totalReviews": 291,
      "factors": [
        {
          "id": "atmosphere",
          "score": 3.6,
          "roundedScore": 3.5,
          "totalReviews": 290
        },
        {
          "id": "communication",
          "score": 3.1,
          "roundedScore": 3,
          "totalReviews": 291
        },
        {
          "id": "teamwork",
          "score": 4.2,
          "roundedScore": 4,
          "totalReviews": 290
        },
        {
          "id": "workLife",
          "score": 3.8,
          "roundedScore": 4,
          "totalReviews": 286
        },
        {
          "id": "leadership",
          "score": 3.5,
          "roundedScore": 3.5,
          "totalReviews": 289
        },
        {
          "id": "tasks",
          "score": 4,
          "roundedScore": 4,
          "totalReviews": 289
        }
      ]
    },
    {
      "id": "AP_CATEGORY_WORK_ENVIRONMENT",
      "score": 3.7,
      "roundedScore": 3.5,
      "totalReviews": 288,
      "factors": [
        {
          "id": "workConditions",
          "score": 3.8,
          "roundedScore": 4,
          "totalReviews": 288
        },
        {
          "id": "environment",
          "score": 3.6,
          "roundedScore": 3.5,
          "totalReviews": 277
        }
      ]
    },
    {
      "id": "AP_CATEGORY_DIVERSITY",
      "score": 3.9,
      "roundedScore": 4,
      "totalReviews": 282,
      "factors": [
        {
          "id": "equality",
          "score": 3.9,
          "roundedScore": 4,
          "totalReviews": 277
        },
        {
          "id": "oldColleagues",
          "score": 3.8,
          "roundedScore": 4,
          "totalReviews": 282
        }
      ]
    }
  ],
  "benefits": [
    {
      "id": "flexWorkingHours",
      "total": 202,
      "percentage": 68
    },
    {
      "id": "parking",
      "total": 201,
      "percentage": 68
    },
    {
      "id": "homeOffice",
      "total": 200,
      "percentage": 68
    },
    {
      "id": "cantine",
      "total": 180,
      "percentage": 61
    },
    {
      "id": "internet",
      "total": 177,
      "percentage": 60
    },
    {
      "id": "pensionPlan",
      "total": 170,
      "percentage": 58
    },
    {
      "id": "healthProgram",
      "total": 168,
      "percentage": 57
    },
    {
      "id": "doctor",
      "total": 160,
      "percentage": 54
    },
    {
      "id": "mobilePhone",
      "total": 150,
      "percentage": 51
    },
    {
      "id": "car",
      "total": 144,
      "percentage": 49
    },
    {
      "id": "meals",
      "total": 134,
      "percentage": 45
    },
    {
      "id": "events",
      "total": 133,
      "percentage": 45
    },
    {
      "id": "discounts",
      "total": 123,
      "percentage": 42
    },
    {
      "id": "reachability",
      "total": 120,
      "percentage": 41
    },
    {
      "id": "coaching",
      "total": 86,
      "percentage": 29
    },
    {
      "id": "accessibility",
      "total": 79,
      "percentage": 27
    },
    {
      "id": "stockOptions",
      "total": 65,
      "percentage": 22
    },
    {
      "id": "dogs",
      "total": 39,
      "percentage": 13
    },
    {
      "id": "daycare",
      "total": 34,
      "percentage": 12
    }
  ],
  "salaryRanges": [
    {
      "jobRoleTitleId": 15019,
      "jobRoleTitle": "Softwareentwickler:in",
      "jobRoleTitleSlug": "softwareentwickler-in",
      "profileRangeMin": 46800,
      "profileRangeMax": 93100,
      "profileMedian": 65000,
      "profileAverage": 68100,
      "profileNumberOfEntries": 23
    },
    {
      "jobRoleTitleId": 12077,
      "jobRoleTitle": "IT Berater:in",
      "jobRoleTitleSlug": "it-berater-in",
      "profileRangeMin": 52900,
      "profileRangeMax": 98000,
      "profileMedian": 70000,
      "profileAverage": 71600,
      "profileNumberOfEntries": 11
    },
    {
      "jobRoleTitleId": 7517,
      "jobRoleTitle": "IT-Projektmanager:in",
      "jobRoleTitleSlug": "it-projektmanager-in",
      "profileRangeMin": 53300,
      "profileRangeMax": 130700,
      "profileMedian": 88800,
      "profileAverage": 91100,
      "profileNumberOfEntries": 7
    }
  ],
  "competitors": [
    {
      "slug": "wyn1",
      "name": "wyn",
      "uuid": "a5f2bd27-02e8-4ffb-bbd5-8ad33acfd2bf",
      "countryCode": "de",
      "score": 4.8,
      "roundedScore": 5,
      "industryId": 36
    },
    {
      "slug": "hatraco",
      "name": "Hatraco GmbH",
      "uuid": "7ffea214-6d3b-4b15-827a-6369d3132e49",
      "countryCode": "de",
      "score": 4.1,
      "roundedScore": 4,
      "industryId": 13
    },
    {
      "slug": "mez-technik",
      "name": "MEZ-TECHNIK GmbH",
      "uuid": "44f31bcf-96fe-42d8-9dcd-b26ab2b5e3d1",
      "countryCode": "de",
      "score": 4.4,
      "roundedScore": 4.5,
      "industryId": 29
    },
    {
      "slug": "alpen-glasfaser1",
      "name": "Alpen Glasfaser GmbH",
      "uuid": "da450421-db65-48ce-9124-bf450c637304",
      "countryCode": "at",
      "score": 4.4,
      "roundedScore": 4.5,
      "industryId": 26
    },
    {
      "slug": "dataschalt-engineering1",
      "name": "DATASCHALT engineering GmbH",
      "uuid": "50484b03-d098-4b5a-ad3d-151d10f39021",
      "countryCode": "de",
      "score": 4.3,
      "roundedScore": 4.5,
      "industryId": 7
    },
    {
      "slug": "jupus",
      "name": "JUPUS GmbH",
      "uuid": "f4aa303f-91be-458b-8540-536170b4c9d5",
      "countryCode": "de",
      "score": 4.5,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "soft-consult-haege",
      "name": "SOFT-CONSULT Häge GmbH",
      "uuid": "28c14089-58d6-4a33-9c60-e03bc074593c",
      "countryCode": "de",
      "score": 4.4,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "firstcolo",
      "name": "firstcolo Datacenters GmbH",
      "uuid": "24d92ed6-af62-4d98-8a84-0971f18ca100",
      "countryCode": "de",
      "score": 4.1,
      "roundedScore": 4,
      "industryId": 6
    },
    {
      "slug": "lynqtech1",
      "name": "LYNQTECH GmbH",
      "uuid": "ea7b9e44-36f7-458e-8cd4-5bb77bb5221f",
      "countryCode": "de",
      "score": 4.6,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "enmore-consulting",
      "name": "enmore consulting ag",
      "uuid": "d808300f-e88b-444b-b2ae-22a89e30ee02",
      "countryCode": "de",
      "score": 3.8,
      "roundedScore": 4,
      "industryId": 6
    },
    {
      "slug": "accountone",
      "name": "AccountOne GmbH",
      "uuid": "5822dc4e-ade3-4d4d-a481-16f6bf361db6",
      "countryCode": "de",
      "score": 4.7,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "protofy2",
      "name": "Protofy GmbH & Co. KG",
      "uuid": "3a41d711-9157-4854-93bf-f3c2173c6b26",
      "countryCode": "de",
      "score": 4.6,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "wescaleit1",
      "name": "wescaleIT AG",
      "uuid": "60ade542-16bb-450f-ba37-74ed87b3c13b",
      "countryCode": "de",
      "score": 4.7,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "scdsoft",
      "name": "scdsoft AG",
      "uuid": "f55725c2-176b-49be-b3a3-15280e9d45cc",
      "countryCode": "de",
      "score": 4.1,
      "roundedScore": 4,
      "industryId": 6
    },
    {
      "slug": "ares-consulting1",
      "name": "ARES Consulting GmbH",
      "uuid": "26de8fe6-9e61-47c2-82cb-1897bf9f48a9",
      "countryCode": "de",
      "score": 4.8,
      "roundedScore": 5,
      "industryId": 6
    },
    {
      "slug": "sovanta1",
      "name": "sovanta AG",
      "uuid": "171966aa-ab1b-4eb8-8f1c-29a3b3e9ce46",
      "countryCode": "de",
      "score": 4.1,
      "roundedScore": 4,
      "industryId": 6
    },
    {
      "slug": "cos5",
      "name": "COS GmbH",
      "uuid": "c1bbb968-5200-47f4-91f3-3a4add881904",
      "countryCode": "de",
      "score": 3.8,
      "roundedScore": 4,
      "industryId": 6
    },
    {
      "slug": "melos-medizinische-labor-organisations-systeme1",
      "name": "LABLIONS software & solutions GmbH",
      "uuid": "b1297274-363d-4419-8eba-ecbfff18bf0c",
      "countryCode": "de",
      "score": 4.3,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "applied-technologies-gesellschaft-fuer-angewandte-informationstechnologien-mbh",
      "name": "applied technologies Gesellschaft für angewandte Informationstechnologien mbH",
      "uuid": "b94cc4fb-4172-4a60-afb6-9f4d7d810f13",
      "countryCode": "de",
      "score": 4.5,
      "roundedScore": 4.5,
      "industryId": 6
    },
    {
      "slug": "seeburger",
      "name": "SEEBURGER AG",
      "uuid": "b299ce1c-1ae2-45ad-b2dc-f42087c10ae7",
      "countryCode": "de",
      "score": 3.9,
      "roundedScore": 4,
      "industryId": 6
    }
  ],
  "topCompanyYears": [
    2023,
    2022
  ],
  "salarySatisfaction": {
    "score": null,
    "roundedScore": null,
    "percentage": null,
    "totalReviews": 288
  },
  "kulturKompass": null,
  "searchQuery": "software",
  "scrapedAt": "2026-05-11T10:32:04.560Z",
  "contentHash": "0c4bc8723fa4ca42d896a80ec14a3b77fac7e12b4534903f2320273d4c105015",
  "changeType": null
}

Incremental fields

When incremental: true, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED.
• firstSeenAt, lastSeenAt — ISO-8601 timestamps tracking the listing across runs.

How to scrape kununu.com

1. Go to Kununu Scraper in Apify Console.
2. Enter a search keyword.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need full descriptions, company data.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Reputation monitoring — track employer ratings, recommendation rates, score trends, and negative-review velocity across a portfolio of competitors or target employers; incremental mode flags only what's changed since the last run.
• Talent acquisition intelligence — applicant-review data (interview questions, hire/reject outcomes, applicant-year) gives recruiters and candidates ground-truth on hiring processes at specific companies.
• HR benchmarking — compare a company against its industryAverageScore, scoreTrend, response-time KPIs, and per-category scores (career, culture, work-environment, diversity).
• Salary benchmarking — pull full salary ranges with includeFullSalary for 20+ job titles per company across Germany, Austria, and Switzerland.
• Lead enrichment — augment a CRM with structured kununu addresses (street + postal + lat/lng), official social handles, employee-count, and revenue facts for DACH employers.
• AI agents and MCP workflows — compact mode strips to core fields for token-efficient LLM input; rich-content mode delivers narrative HTML (slogan, company overview, jobInsights Q&A) when needed.
• Awards and credibility signals — surface kununu Top Company seals, Top Rated badges, and third-party awards with per-year history and wasPaid flag to distinguish earned vs sponsored badges.
• Export anywhere — JSON, CSV, or Excel from Apify Console; or stream via API to dashboards, data warehouses, n8n / Make / Zapier.

How much does it cost to scrape kununu.com?

Kununu Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.005 per run
• Per result: $0.001 per job record

Example costs:

• 10 results: $0.01
• 100 results: $0.11
• 500 results: $0.51

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of listings that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 results per run, daily polling (30 runs/month). Event-pricing examples scale linearly with result count.

Numbers below are for the primary Company profile event. Other events (Review extracted) are billed separately when they fire and follow the same incremental logic — when the underlying record has not changed, no charge is emitted.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.26	$0.02	$0.24 (93%)	$0.53
15% — moderate broad query	$0.26	$0.04	$0.21 (83%)	$1.27
30% — high-volume aggregator	$0.26	$0.08	$0.17 (69%)	$2.40

Full re-scrape monthly cost at daily polling: $7.65. First month with incremental costs $0.76 / $1.49 / $2.57 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage (compute and proxies) is billed separately by Apify based on actual consumption. Incremental runs consume less on result processing, though fixed per-run overhead stays the same.

FAQ

How many results can I get from kununu.com?

The number of results depends on the search query and available listings on kununu.com. Use the maxResults parameter to control how many results are returned per run.

Does Kununu Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed listings on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Kununu Scraper with other apps?

Yes. Kununu Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Kununu Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Kununu Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode and descriptionMaxLength to keep payloads manageable for LLM context windows.

Is it legal to scrape kununu.com?

This actor extracts publicly available data from kununu.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.