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Capterra Reviews Scraper

Software reviews,
and a column that says what happened.

Give it a Capterra product page and get that listing's reviews back as rows: the product, the author, the rating, the headline, the review text and the date, with the product's own overall rating and review count alongside. A tenth column, status, records how the query itself went - so a run that returns nothing tells you why in writing.

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one-time 500 free rows$0.002 per row after10 columnsCSV · XLSX · JSON
How it works

A list of products in,
their reviews out.

The input is the listing, not the review - you name the software you want feedback on, pick the order, and the job collects what buyers wrote.

  1. STEP 1Sign in to the platform.
  2. STEP 2Open the Capterra Reviews Scraper.
  3. STEP 3Paste Capterra product page URLs, one per line - or upload a CSV, XLSX, TXT or Parquet file.
  4. STEP 4Set a limit per query, or clear the box to take everything.
  5. STEP 5Choose the sort order: most helpful, most recent, highest rating or lowest.
  6. STEP 6Click Get Data.

One row per review, each tagged with the query it came from - so a run across fifty products still reconciles back to your input list.

Why teams use it

A quiet run
is not a silent one.

The status column reports back

Most scrapers tell you a query failed by returning nothing, which is indistinguishable from a product that has no reviews. This one writes a row either way and puts the outcome in status as readable text - so an empty result is something you detect by reading a column, not by guessing at a row count.

Two grains in one row

rating, title, review, author and date describe one review. aggregate_rating and review_count describe the product it sits on. A two-star review among hundreds is a different fact from a two-star among nine, and only the second pair tells you which you are reading.

Four orders, and the limit decides the bill

Most helpful, most recent, highest rating or lowest - chosen before the run. Pair lowest with a small limit and the first rows are the complaints. Worth knowing that the limit arrives pre-filled at 100, so taking everything means clearing the box rather than leaving it as delivered.

What you get back

Ten columns,
one row per review.

Each row carries the review as Capterra displays it, the product it belongs to, and the outcome of the query that fetched it.

Review text comes back verbatim, with no preprocessing applied. The column list below is unusually well attested for this family - the platform's published array, the JSON keys of the archived runs and the header row of their workbooks all carry the same ten names in the same order. Read the note under the table before you write a parser, because the names being solid is not the same as the values being known.

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Data dictionary

Ten columns,
and what each one holds.

The platform's own column list, in export order, matched against the JSON keys and the workbook header row of real runs. Descriptions say what each field is for; they claim nothing about its format - see the note underneath.

query
The Capterra product URL the job requested, repeated on every row that came from it. It echoes what was actually fetched rather than only what you typed - the chosen sort is appended to it - so it is the record of the request as well as the join key.
product
The product the review belongs to, as the listing names it.
author
Who wrote the review, as the site publishes it: a display name rather than a full identity.
rating
The score this reviewer gave. Read the scale off your own first rows rather than assuming one.
title
The review headline - the short verdict a reviewer writes above the explanation.
review
The free-text review body, verbatim and unprocessed.
date
When the review was posted.
aggregate_rating
The product's overall score, not this review's. It repeats on every row belonging to that product, which is what lets you tell a low review among many from a low review among few.
review_count
How many reviews that overall score is built on - again a property of the product rather than of the row.
status
How the query itself went, in readable words. This is the column to check first: when a query returns no reviews, the row still arrives and the reason is written here.

The names are solid; the values are not described here, and that is deliberate. Three independent sources carry the same ten names in the same order - the platform's published column array, the JSON keys of the archived runs, and the header row of their workbooks. What none of them gives is a review: every archived run of this scraper came back with the review columns empty and a message in status, one that named an anti-bot wall and a residential proxy. The platform's own note on the form says the same thing in general terms - anti-bot sites return a blocked status on the free pool, which is residential-only. So this page states no date pattern, no rating scale, no format for aggregate_rating or review_count, and no fill rates, because nothing has been seen in them. What it does tell you is where to look: run one product on the free tier and read status on the first row before you build anything on the rest.

Run controls

Set on the job,
not in the spreadsheet.

A per-query limit decides what a run costs and how much lands in the file. The sort order decides which reviews that limit spends itself on - both are chosen before the job starts.

Capterra product URL One per line Limit per query, pre-filled at 100 Clear it to take everything Most Helpful Most recent Highest Rating Lowest Rating Through the proxy pool CSV upload XLSX upload TXT upload Parquet upload
Common workflows

Three jobs people
most often run here.

A few examples of how teams use software review data to answer a question they actually have.

Product

Read the one-star reviews of your category

Pull every product you compete with, sorted by lowest rating with a modest limit. What buyers complain about in a category tends to repeat, and the repeated complaint is either your differentiator or your next bug report.

Product · Research
Competitive

Weigh a rating by how much is behind it

Keep aggregate_rating and review_count next to each review and a four-point-five stops being one number. A young product with a handful of glowing reviews and an established one with thousands are not comparable, and this export makes that visible in the same row.

Strategy
Ops

Check the status column before the analysis

Filter on status first. A query that came back without reviews still produced a row, so a quick pass over that column separates products that genuinely have nothing from queries that did not get through - before either becomes a conclusion.

Data · Ops
Get started
Pricing

Pay only for the reviews
you actually pull.

No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.

Free tier

500 free rows - $0

Every new account, one-time. No credit card required. Per-query limits, every sort order, file upload and every export format included.

$0 forever
Pay-as-you-go

$0.002 per row, after the free tier

Roughly $2 per 1,000 reviews. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.

Most popular
Volume

Custom · high volume

Volume pricing, dedicated workers and an SLA for continuous monitoring or very large historical pulls. Tell us your numbers and we will quote.

Talk to us
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10% off your first paid run.Use code LIVESCRAPER10 at checkout.
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Pairs well with

The same buyers,
on another directory.

GetApp Reviews Scraper

Another software directory behind the same ten columns in the same order, so the two exports stack in one table without reshaping - and the status column works the same way on both.

Explore

G2 Reviews Scraper

The third software directory, and a deliberately different export: G2 splits pros and cons into their own columns and adds reviewer title and company size. Different shape, complementary question.

Explore
The legal bit

Is it legal to scrape
Capterra reviews?

Short answer: yes for the public review content - and this export carries nothing about the reviewer beyond what the listing shows.

Reviews on Capterra are published to be read. The rating, the headline, the review text and the date are shown to anyone who opens the product page, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login, an account or a paywall.

The author field carries what the site itself publishes, which is a display name rather than a full identity - there is no email and no address in this output. If you are processing the review text in the EU, the usual rules still apply to what you do with it downstream.

Capterra's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the platform, check it. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.

Sign up now
livescraper.app · principles
Public review content only✓
No logins, no accounts touched✓
Display names, not identities✓
GDPR-aligned by default✓
Exports auto-delete (30 days)✓
Check Capterra's own terms before scaling.
Common questions

Things people
ask before signing up.

The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.

How do I scrape Capterra reviews?+
Using the Capterra Reviews Scraper:
  1. Sign in to the platform.
  2. Open the Capterra Reviews Scraper.
  3. Paste Capterra product page URLs, one per line - or upload a CSV, XLSX, TXT or Parquet file.
  4. Set a limit per query, or clear the box to take everything.
  5. Choose the sort order: most helpful, most recent, highest rating or lowest.
  6. Click Get Data.
What comes back for each review?+
Ten columns: the query, the product, the author, the rating, the headline, the review text, the date, the product's overall rating, its review count, and the status of the query itself.
What is the status column for?+
It records how the query went, in readable words. A query that returns no reviews still produces a row, and the reason is written into that column rather than left for you to infer - which is what separates a product with nothing to say from a request that did not get through.
What if a run comes back with no reviews?+
Read the status column on the row it produced. The platform's own note on the form is that requests go out through the proxy pool, and that anti-bot sites return a blocked status on the free pool, which is residential-only. Run one product on the free tier and look at that column before planning a larger pull.
How do I take every review a product has?+
Clear the limit box. It ships pre-filled with 100, so an untouched first run is capped rather than complete - emptying it is a deliberate step, and the placeholder reads "all" once it is empty.
Can I choose the order the reviews come back in?+
Yes, and it is set before the run: most helpful, most recent, highest rating or lowest rating. Pairing a sort order with a limit is what lets you buy the slice you want - lowest first with a small limit puts the complaints at the top of the file.
What is the difference between rating and aggregate_rating?+
Rating is the score in that one review. Aggregate_rating is the product's overall score, and review_count is how many reviews it is built on - both repeat on every row for that product. Keeping them side by side is what lets you weigh a single low review against how much sits behind the average.
How much does it cost?+
The first 500 rows are free and one-time, with no credit card. After that it is $0.002 per row - about $2 per 1,000 reviews - which is the same flat rate as every other scraper on the platform. The estimator shows the cost of a run before it starts.

Your first 500 reviews,
on the house.

500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.

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Scrape Capterra software reviews at scale

Livescraper's Capterra Reviews Scraper turns a list of software listings into review data. You submit Capterra product page URLs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - set a limit per query or clear it to take everything, choose the sort order, and download what buyers wrote as a clean CSV, Excel or JSON file.

Each row carries the review as Capterra displays it - the author, the rating, the headline, the full text and the date - plus the product it belongs to and that product's own overall rating and review count. Two grains in one row is what makes the export analysable: a low review among thousands is a different fact from a low review among nine, and only the product-level pair tells you which one you are reading.

The tenth column is the one worth knowing about. Status records how the query itself went, in readable words, and a query that returns no reviews still produces a row rather than vanishing. That turns a silent failure into something you can filter on - which is why the honest first step here is to run one product on the free tier and read that column before building anything on the rest.

One practical note up front: this page describes the ten columns and their order, which the platform's published list, the JSON keys of real runs and their workbook header rows all agree on exactly. It claims no date pattern, no rating scale and no fill rates, because no archived run of this scraper contains a review to base them on - every one came back with the review columns empty and a message in status. The platform's own note says anti-bot sites return a blocked status on the free pool, which is residential-only. Your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.