G2 Reviews Scraper — SaaS Ratings, Switching & Monitor

Unofficial tool for publicly accessible G2 product and review pages. G2 and related names are trademarks of their respective owners. Not affiliated with, sponsored by, or endorsed by G2.com, Inc. For informational, research, and competitive-intelligence use only. Respect applicable terms of use and law.

G2 Reviews Scraper ◄── you are here

Scrape G2 product reviews into structured JSON: star ratings, pros/cons, switching data, reviewer role, company size, and an LLM-ready markdown field. Paste product slugs or review / seller / category / search URLs. Run incremental monitor mode so scheduled jobs emit only new reviews and rating deltas. A practical G2 API alternative — call from Python, Node.js, cURL, or Apify MCP.

Ideal for SaaS competitive intel, win-loss research, sales battlecards, product ops, and review monitoring.

Crawloop Reviews Suite — B2B G2 + consumer Trustpilot + employer Glassdoor.

G2 (SaaS product reviews)	Trustpilot (consumer reputation)	Glassdoor (employer intel)
G2 Reviews Scraper ◄── you are here	Trustpilot Scraper	Glassdoor Scraper

When to use this Actor

• You need a G2 reviews scraper for one or many products as JSON
• You want switching / pros / cons fields for battlecards without cleaning HTML
• You schedule new-review monitoring with webhooks instead of re-downloading full histories
• You want a G2 API alternative from Python, Node.js, or an MCP / AI assistant
• You map a G2 category or search first, then pull reviews for the shortlist

When not to use this Actor

• Consumer Trustpilot reviews — use Trustpilot Scraper
• Employer reviews / salaries / jobs — use Glassdoor Scraper
• G2 vendor portal, authenticated surveys, or writing reviews — public pages only
• Guaranteed tens of thousands of reviews per product in one cheap HTTP run — G2 uses DataDome; this Actor uses a stealth browser + residential proxies

Modes

Mode	What it does
reviews	Product reviews (optional product profile + alternatives)
products	Search or category catalog rows
monitor	Named KV watermarks; emit only new reviews and rating/count deltas

Key features

• Reviews + catalog + monitor in one Actor — product URLs/slugs, search, category, seller pages
• Typed dataset rows — product, review, alternative, monitor_event
• Battlecard fields — likes/dislikes, problems solved, recommendations, switched-from
• Star / date / keyword filters — publishedAfter accepts 2026-08-01 or 7 days
• Incremental monitor — named KV watermarks, early-stop, Slack/Discord/Telegram alerts
• LLM-ready markdownContent per review (omit with compact mode)
• Camoufox + US residential — DataDome bypass; no G2 login

Input

Parameter	Description
mode	reviews / products / monitor
startUrls / productSlugs	Review pages or slugs (slack)
searchQuery / categorySlug	Catalog discovery (products mode)
maxReviews / maxProducts / maxItems	Caps
sortOrder	newest / helpful / highest / lowest
starRatings / publishedAfter / reviewSearchQuery	Filters
includeProductProfile / includeAlternatives	Extra row kinds
monitorStoreName / monitorBaselineOnly / earlyStopAfterKnown	Monitor KV
webhookUrl / Telegram fields	Alerts
proxyConfiguration	Use Apify Residential (US)

Example — recent Slack reviews

{
  "mode": "reviews",
  "productSlugs": ["slack"],
  "maxReviews": 100,
  "sortOrder": "newest",
  "includeProductProfile": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }
}

Example — only 1–2 star reviews since last week

{
  "mode": "reviews",
  "startUrls": [{ "url": "https://www.g2.com/products/jira/reviews" }],
  "sortOrder": "lowest",
  "starRatings": ["1", "2"],
  "publishedAfter": "7 days",
  "maxReviews": 200
}

Example — monitor baseline then schedule

{
  "mode": "monitor",
  "productSlugs": ["slack", "microsoft-teams"],
  "monitorStoreName": "g2-reviews-monitor",
  "monitorBaselineOnly": true,
  "earlyStopAfterKnown": 5,
  "notifyOnNewReviews": true,
  "notifyOnNegativeReviewsOnly": true
}

After the baseline run, set monitorBaselineOnly to false. Schedule hourly or daily — later runs emit only new reviews and rating/count deltas.

Example — category catalog

{
  "mode": "products",
  "categorySlug": "crm",
  "maxProducts": 50
}

Output

Each dataset row has a type field: product, review, alternative, or monitor_event.

Field	Description
type	Row kind
productSlug / productName	G2 product identity
reviewId	Stable review id
starRating	1–5 score
date	Publish date (YYYY-MM-DD)
title / text	Headline and full body
likes / dislikes	Pros / cons when present
problemsSolved / recommendations	Extra survey answers
switchedFrom / switchedReason	Prior product and why
reviewerName / reviewerTitle	Reviewer
companySize / reviewerIndustry	Firmographics
reviewUrl	Canonical review URL when G2 exposes it
markdownContent	LLM-ready block (omit with compact)
reviewCount / vendorName / categoryNames	Product-row fields

{
  "type": "review",
  "reviewId": "1234567",
  "productSlug": "slack",
  "productName": "Slack",
  "starRating": 4.5,
  "date": "2026-05-12",
  "title": "Best team chat we have used",
  "likes": "Channels keep work organized; integrations with Jira are seamless.",
  "dislikes": "Notification volume is high until you tune it.",
  "switchedFrom": "Microsoft Teams",
  "reviewerName": "Sarah M.",
  "reviewerTitle": "Marketing Manager",
  "companySize": "Mid-Market (51-1000 emp.)",
  "reviewUrl": "https://www.g2.com/products/slack/reviews/slack-review-1234567"
}

Use cases

Use case	What you get
Competitor battlecards	Pros/cons + switched-from on your product and 3 rivals
Detractor mining	1–2★ sort + negative-only webhooks
Win-loss research	Switching reasons and firmographics (role, size, industry)
RAG / LLM pipelines	markdownContent chunks with product/rating metadata
Scheduled monitoring	Only new reviews after a KV baseline
Category mapping	Top products in a G2 category before a review pull

Integration examples

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('crawloop/g2-reviews-scraper').call({
  mode: 'reviews',
  productSlugs: ['slack'],
  maxReviews: 100,
  sortOrder: 'newest',
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient

client = ApifyClient(token="YOUR_APIFY_TOKEN")
run = client.actor("crawloop/g2-reviews-scraper").call(
    run_input={
        "mode": "reviews",
        "productSlugs": ["hubspot"],
        "maxReviews": 200,
        "sortOrder": "helpful",
    }
)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item.get("title"), item.get("starRating"))

cURL

curl -X POST \
  "https://api.apify.com/v2/acts/crawloop~g2-reviews-scraper/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"products","searchQuery":"CRM","maxProducts":25}'

MCP and AI assistants

Use this Actor from AI tools via Apify MCP. Connect your Apify account, then call crawloop/g2-reviews-scraper.

Example prompts:

• "Run G2 Reviews Scraper for Slack, max 50 newest reviews, and return stars, likes, dislikes, and switchedFrom as JSON"
• "Scrape 1- and 2-star Jira G2 reviews from the last 30 days and summarize the top complaints"
• "Chain G2 Reviews Scraper for HubSpot then Trustpilot Scraper for hubspot.com and compare B2B vs consumer sentiment"

Suite next step

After a G2 pull, run Trustpilot Scraper on the same vendor domain for consumer reputation, or Glassdoor Scraper for employer-brand context on the vendor.

FAQ

Do I need a G2 login or official API key? No. The Actor reads public product and review pages only. G2’s partner API is separate and not required.

Can I scrape G2 reviews with Python or Node.js? Yes. Use the Apify client snippets above, or call the Actor from an MCP / AI assistant.

Why residential proxies? G2 sits behind DataDome. Datacenter IPs usually get a CAPTCHA. Use Apify Residential (US).

How is this different from other G2 scrapers? Monitor mode with KV watermarks and webhooks, switching/pros/cons fields, catalog + reviews in one Actor, and a suite chain to Trustpilot and Glassdoor.

Can I scrape a whole category? Yes — mode=products with categorySlug or searchQuery, then feed the slugs back in reviews mode.

Does monitor mode re-download the full history every run? No. After a baseline, later runs emit new reviews and rating/count deltas and can stop early once known reviews appear.

Related Actors

• Trustpilot Scraper
• Glassdoor Scraper