How to Scrape G2 Data: Complete Guide for 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

To scrape G2 reviews with AlterLab, send a POST request to the scrape endpoint with the target URL and your API key. The response returns the rendered HTML, which you can parse with Python libraries like BeautifulSoup to extract review titles, ratings, and dates. Use the Python SDK or cURL examples below to get started in under five minutes.

Why collect reviews data from G2?

G2 hosts verified user reviews for thousands of SaaS products, making it a rich source for market research. Teams extract this data to monitor competitor sentiment, identify feature gaps, and inform product roadmaps. Analysts also aggregate ratings over time to track market trends and validate pricing strategies.

Technical challenges

Scraping G2 presents three main obstacles. First, the site enforces strict rate limits on IP addresses, returning 429 responses after a few requests per minute. Second, many review sections load via JavaScript after the initial HTML, so a raw HTTP request misses the content. Third, G2 employs bot detection that challenges headless browsers with CAPTCHAs or JavaScript checks. AlterLab's Smart Rendering API addresses these by rotating residential proxies, executing pages in a real Chromium environment, and automatically solving challenges while preserving compliance with public‑data access.

Quick start with AlterLab API

Begin by installing the AlterLab Python SDK and making your first request. See the Getting started guide for installation details.

```python title="scrape_g2-com.py" {3-5}

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://g.com/project-management")
print(response.text[:500])

```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://g2.com/project-management"}'

The response contains the fully rendered page, ready for parsing.

Extracting structured data

Once you have the HTML, use CSS selectors to pull the visible review fields. G2 structures each review inside a <div class="review-item">. Within that, the title lives in an <h3>, the rating in a <span class="rating">, and the date in a <time> tag.

```python title="parse_g2.py" {4-8}
from bs4 import BeautifulSoup
soup = BeautifulSoup(response.text, "html.parser")
reviews = []
for block in soup.select("div.review-item"):
title = block.select_one("h3").get_text(strip=True)
rating = block.select_one("span.rating").get_text(strip=True)
date = block.select_one("time").get("datetime")
reviews.append({"title": title, "rating": rating, "date": date})
print(reviews[:3])

For JSON‑oriented endpoints, you can also request `formats=["json"]` to receive pre‑extracted fields, though the HTML route works for any custom data point.

## Best practices
Respect G2's crawling policies by throttling requests to no more than one per second. Use AlterLab's built‑in rate limiting via the `max_concurrency` parameter to avoid 429 errors. Always check `/robots.txt` before scaling; the file permits scraping of `/product/*` pages with a crawl delay of 10 seconds. Handle dynamic content by enabling JavaScript rendering (`render_js: true`) and waiting for network idle. Finally, store results in a time‑series database to facilitate trend analysis without re‑scraping unchanged pages.

## Scaling up
For large‑scale jobs, batch URLs into chunks of 100 and process them with asynchronous calls. Schedule nightly runs using AlterLab's cron‑based scheduling to keep datasets fresh. Monitor usage and costs on the dashboard; the [pricing](/pricing) page shows tiered rates that drop as volume increases. If you need to extract data from thousands of product pages, consider enabling the `min_tier` parameter to skip levels that do not require JavaScript, reducing both time and expense.

## Key takeaways
- Use AlterLab's Smart Rendering API to bypass JavaScript and bot defenses on G2.
- Parse the returned HTML with CSS selectors to extract review titles, ratings, and dates.
- Apply rate limiting, respect robots.txt, and cache unchanged pages to scrape responsibly.
- Automate recurring jobs with scheduling and track expenses via the pricing page.

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  <div data-stat data-value="99.2%" data-label="Success Rate"></div>
  <div data-stat data-value="1.2s" data-label="Avg Response"></div>
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  <div data-step data-number="1" data-title="Request page" data-description="Send URL to AlterLab API with rendering enabled"></div>
  <div data-step data-number="2" data-description="Receive fully rendered HTML"></div>
  <div data-step data-number="3" data-title="Parse data" data-description="Extract review fields via CSS selectors"></div>
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