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Capterra Reviews Scraper

Give it Capterra product URLs and it returns one JSON row per review: overall and sub-ratings, pros, cons, review text, the reviewer's role, industry and company size, how long they used the product and the incentivized flag. $1.79 per 1,000 reviews on the free plan, and you pay only for the rows you get.

$1.79 per 1,000 reviews on the Free plan, down to $1.16 on higher plans

Why this actor

Drop-in input

Input names of other Capterra review scrapers (productUrl, maxResults, sort, starRating) work as they are. Free error rows with a machine-readable code (not_found, blocked, unavailable...), so an agent knows whether to retry.

As stated in the actor’s README. The full list of accepted input names is on the Store page.

What you get

A few fields worth knowing about:

reviewId is Capterra's own ID, so it stays the same between runs. Use it to dedupe when you scrape the same product every week.

incentivized tells you whether Capterra marked the review as written for a gift card or similar reward. Plenty are. If you're doing sentiment analysis you probably want to look at both groups separately.

switchedFrom, alternativesConsidered and reasonForSwitching are filled only when the reviewer answered those questions, which is maybe one review in four. They're the most useful fields for competitive research, so the gaps are a pity, but that's the data.

product repeats on every review, so each row stands on its own when you load it into a spreadsheet or a database. The same product, plus pricing plans, alternatives, features and integrations, is saved once per product in the PRODUCT record of the run's key-value store.

Sub-ratings are left out when the reviewer skipped them (you won't see a fake 0). likelihoodToRecommend is on a 0 to 10 scale; the rest are 1 to 5.

Reviewer names and profile photos are never collected. You get the role and company details, not the person.

Sample row (from the README example; long text shortened, lists cut to two items):

{
  "rowType": "review",
  "platform": "capterra",
  "productName": "Slack",
  "productUrl": "https://www.capterra.com/p/135003/Slack/",
  "entityId": "135003",
  "reviewId": "Capterra___7224147",
  "reviewUrl": "https://www.capterra.com/p/135003/Slack/#Capterra___7224147",
  "title": "Very strong communication tool.",
  "text": "Generally very good, quick and easy. Good options to integrate, easy to file share. I typically run multiple Slack instances.",
  "pros": "Ability to communicate in real time with colleagues when we're working remotely. Also for file sharing.",
  "cons": "The threads feature is not something that I find particularly intuitive...",
  "ratingOverall": 5,
  "ratings": {
    "easeOfUse": 5,
    "customerService": 4,
    "features": 5,
    "valueForMoney": 5,
    "likelihoodToRecommend": 9
  },
  "date": "2026-10-02",
  "reviewerRole": null,
  "reviewerIndustry": null,
  "reviewerCompanySize": null,
  "reviewerTimeUsed": null,
  "switchedFrom": "Google Workspace",
  "reasonForSwitching": "Google Workspace chat works well. However, several of our technical staff wanted to use Slack.",
  "incentivized": true,
  "incentive": "NominalGift",
  "reviewerVerified": null,
  "product": {
    "platform": "capterra",
    "entityId": "135003",
    "name": "Slack",
    "url": "https://www.capterra.com/p/135003/Slack/",
    "rating": 4.7,
    "reviewCount": 24229,
    "category": "Team Communication",
    "vendor": "Slack",
    "website": null
  },
  "scrapedAt": "2026-10-20T17:49:58.237Z"
}

Price

Live Apify price on 2026-10-31, per 1,000 rows, by Apify plan. You pay only for rows delivered; error and summary rows marked free in the README cost nothing. Plus $0.001 per GB of memory when a run starts.

Per 1,000FreeBronzeSilverGoldPlatinumDiamond
Review (main)$1.79$1.34$1.25$1.16$1.16$1.16

Run it from code or an AI agent

Actor ID arcadium/capterra-reviews. Set APIFY_TOKEN to your Apify API token. The input below is the actor’s prefilled example; see the guide for every field.

curl
curl -X POST "https://api.apify.com/v2/acts/arcadium~capterra-reviews/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.capterra.com/p/135003/Slack/reviews/"}],"maxReviewsPerProduct":20}'
JavaScript
// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/capterra-reviews').call({
  "startUrls": [
    {
      "url": "https://www.capterra.com/p/135003/Slack/reviews/"
    }
  ],
  "maxReviewsPerProduct": 20
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
Python
# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/capterra-reviews").call(run_input={
    "startUrls": [{
        "url": "https://www.capterra.com/p/135003/Slack/reviews/",
    }],
    "maxReviewsPerProduct": 20,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)
MCP
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/capterra-reviews",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}
LangChain
# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/capterra-reviews")
rows = tool.invoke({"run_input": {
    "startUrls": [{
        "url": "https://www.capterra.com/p/135003/Slack/reviews/",
    }],
    "maxReviewsPerProduct": 20,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])
LlamaIndex
# pip install llama-index-readers-apify
import os
from llama_index.core import Document, VectorStoreIndex
from llama_index.readers.apify import ApifyActor

reader = ApifyActor(os.environ["APIFY_TOKEN"])
documents = reader.load_data(
    actor_id="arcadium/capterra-reviews",
    run_input={
        "startUrls": [{
            "url": "https://www.capterra.com/p/135003/Slack/reviews/",
        }],
        "maxReviewsPerProduct": 20,
    },
    dataset_mapping_function=lambda item: Document(text=str(item.get("text") or ""), metadata={"url": item.get("url")}),
)
index = VectorStoreIndex.from_documents(documents)
CrewAI
# pip install crewai crewai-tools langchain-apify   (reads APIFY_API_TOKEN from the environment)
from crewai import Agent
from crewai_tools import ApifyActorsTool

tool = ApifyActorsTool(actor_name="arcadium/capterra-reviews")
researcher = Agent(
    role="Research analyst",
    goal="Answer with data collected by the tool",
    backstory="Uses public web data.",
    tools=[tool],
)
n8n
{
  "nodes": [
    {
      "name": "Run arcadium/capterra-reviews",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~capterra-reviews/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"startUrls\":[{\"url\":\"https://www.capterra.com/p/135003/Slack/reviews/\"}],\"maxReviewsPerProduct\":20}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}
Google Sheets
// Extensions > Apps Script. Store your token once: Project Settings > Script properties > APIFY_TOKEN.
function importLastRun() {
  const token = PropertiesService.getScriptProperties().getProperty('APIFY_TOKEN');
  const url = 'https://api.apify.com/v2/acts/arcadium~capterra-reviews/runs/last/dataset/items?status=SUCCEEDED&clean=true&format=json';
  const res = UrlFetchApp.fetch(url, { headers: { Authorization: 'Bearer ' + token } });
  const rows = JSON.parse(res.getContentText());
  if (!rows.length) return;
  const keys = Object.keys(rows[0]);
  const values = rows.map((r) => keys.map((k) => (typeof r[k] === 'object' ? JSON.stringify(r[k]) : r[k])));
  const sheet = SpreadsheetApp.getActiveSheet();
  sheet.clearContents();
  sheet.getRange(1, 1, 1, keys.length).setValues([keys]);
  sheet.getRange(2, 1, values.length, keys.length).setValues(values);
}

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