# Capterra Reviews Scraper - $0.60 / 1000 Reviews (`scrapeunblocker/capterra-reviews-scraper`) Actor

Scrape Capterra software reviews: overall and 4 sub-ratings, likelihood to recommend, pros, cons, reasons for choosing and switching, alternatives, reviewer role, industry, company size, incentive badges, vendor reply, plus product rating and pricing. Up to 2500 per product. $0.60 per 1000 reviews.

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

## Pricing

from $0.60 / 1,000 reviews

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Capterra Reviews Scraper

Scrape **Capterra software reviews** as clean structured JSON - review title, overall rating and the four sub-ratings (ease of use, customer support, features, value for money), likelihood to recommend (0-10), date, the overall comment, pros, cons, reasons for choosing and for switching, the products considered or switched from, advice, every question-and-answer of the review, reviewer name, job title, industry, company size, time using the product and LinkedIn / proof validations, the incentive and source badges and the vendor's reply. Each product also gets a summary: vendor, rating, review count, sub-rating averages, categories, description, target market and pricing plans. No Capterra account, no API key, no proxies to configure.

### Features

- **Any product input** - a Capterra product URL (the `/reviews/` page works too), `id/slug` (`135003/Slack`) or the numeric product id.
- **Up to 2500 reviews per product** - 25 reviews per Capterra page, at most 100 pages per product (Capterra's own limit), in Capterra's **Most Helpful** order.
- **Full review data** - all ratings, pros, cons, comments, switching reasons, alternatives considered, switched-from products, advice and the full list of answers.
- **Reviewer data** - name (or anonymous), job title, industry, company size, time used, profile picture, LinkedIn verification and validation methods.
- **Trust signals** - incentive type, incentivized and vendor-referred flags, the review source badge (organic, vendor invite, gift card ...) and where the review was collected (Capterra, GetApp or Software Advice).
- **Product summary** - the `PRODUCTS` record with vendor, rating, review count, sub-rating averages, categories, description, target market, starting price, pricing model, free trial / free version and every pricing plan.
- **Deduplicated** - the same product given twice (URL and id) is collected once.

### Use cases

- **Competitive analysis** - what users like and dislike about competing tools, and why they switch.
- **Product research** - mine pros, cons and switching reasons for feature gaps.
- **Sales and marketing** - find which alternatives buyers considered, by industry and company size.
- **Review monitoring and LLM pipelines** - feed structured reviews into BI tools, spreadsheets or an LLM.

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `products` | array | Slack | Capterra product URLs, `id/slug` or product ids. |
| `maxReviewsPerProduct` | integer | `50` | Reviews per product (1-2500). |

#### Example input

```json
{
  "products": [
    "https://www.capterra.com/p/135003/Slack/",
    "233108/deskbird"
  ],
  "maxReviewsPerProduct": 500
}
```

### Output

One dataset item per review:

```json
{
  "productId": "135003",
  "productSlug": "Slack",
  "productName": "Slack",
  "productUrl": "https://www.capterra.com/p/135003/Slack/",
  "id": "7202161",
  "globalId": "Capterra___7202161",
  "url": "https://www.capterra.com/p/135003/Slack/#Capterra___7202161",
  "title": "Corporate Social Media platform",
  "rating": 5.0,
  "easeOfUse": 5.0,
  "customerSupport": 5.0,
  "features": 5.0,
  "valueForMoney": 5.0,
  "likelihoodToRecommend": 10,
  "date": "2026-08-20",
  "reviewerName": "Abhishek M.",
  "reviewerAnonymous": false,
  "reviewerTitle": "Subject Matter Expert",
  "reviewerIndustry": "Financial Services",
  "reviewerCompanySize": "201-500 employees",
  "reviewerTimeUsedProduct": "2+ years",
  "reviewerVerifiedLinkedIn": true,
  "reviewerValidated": true,
  "reviewerValidations": ["LinkedIn", "ProofOfLink"],
  "comments": "Wonderful. The chat feature is great. ...",
  "pros": "Channel creation. Could tag channels in other channels. ...",
  "cons": "The delete message option should be enabled for Admins. ...",
  "reasonsForChoosing": null,
  "reasonsForSwitching": null,
  "advice": null,
  "alternativesConsidered": [],
  "switchedFrom": [],
  "answers": [{"question": "Overall", "answer": "Wonderful. ..."}, {"question": "Pros", "answer": "..."}, {"question": "Cons", "answer": "..."}],
  "incentive": "NoIncentive",
  "incentivized": false,
  "vendorReferred": false,
  "reviewSourceCode": "NO",
  "reviewSourceDescription": "No Incentive Offered: This review was submitted organically.",
  "source": "Capterra",
  "vendorResponseText": null,
  "vendorResponseDate": null,
  "productInput": "https://www.capterra.com/p/135003/Slack/"
}
```

The key-value store also holds:

- `PRODUCTS` - per product: id, slug, name, vendor, rating, review count, sub-rating averages, categories, description, target market, pricing, logo, URLs, the reviews collected and `viewLimitReached` (the product has more reviews than Capterra's 100 pages show).
- `ERRORS` - products that returned no reviews (unknown product, no reviews yet, blocked) with the reason. These are **not billed**.

### Pricing

**$0.60 per 1000 reviews.** You pay only for reviews delivered; failed products are not charged.

### Notes

- Reviews come in Capterra's **Most Helpful** order (the order of its review pages); there is no date sort or rating filter. Filter the dataset by `date` or `rating` after the run.
- Capterra lists at most 100 review pages (2500 reviews) per product. For products with more reviews, the oldest are not reachable this way.
- Some reviews are syndicated from GetApp or Software Advice; `source` tells where each one was written.

# Actor input Schema

## `products` (type: `array`):

Capterra products to collect reviews for: a product URL (https://www.capterra.com/p/135003/Slack/ - the /reviews/ page works too), the id with its slug (135003/Slack) or the numeric product id (135003).

## `maxReviewsPerProduct` (type: `integer`):

How many reviews to collect for each product (1-2500). Capterra lists 25 reviews per page and at most 100 pages per product, in its 'Most Helpful' order.

## Actor input object example

```json
{
  "products": [
    "https://www.capterra.com/p/135003/Slack/"
  ],
  "maxReviewsPerProduct": 50
}
```

# Actor output Schema

## `results` (type: `string`):

One item per review, with the product id, name and URL

## `products` (type: `string`):

Per product: vendor, rating, review count, sub-rating averages, categories, description, target market, pricing plans and the reviews collected

## `errors` (type: `string`):

Products that returned no reviews (unknown product, no reviews, or blocked), with the reason (not billed)

# 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 = {
    "products": [
        "https://www.capterra.com/p/135003/Slack/"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapeunblocker/capterra-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 = { "products": ["https://www.capterra.com/p/135003/Slack/"] }

# Run the Actor and wait for it to finish
run = client.actor("scrapeunblocker/capterra-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 '{
  "products": [
    "https://www.capterra.com/p/135003/Slack/"
  ]
}' |
apify call scrapeunblocker/capterra-reviews-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapeunblocker/capterra-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/bYKAfIMrpcQGBhXIg/builds/cBz4OGld6EGYZ6HBU/openapi.json