G2 Scraper

Scrape G2 reviews and export G2 data to CSV, JSON, or Excel for research and reporting. This G2 API alternative collects public reviews with clear inputs and structured output.

Collect recent public software product reviews, including ratings, headlines, likes, dislikes, and reviewer context. Enter product slugs or product review URLs and combine several products in one export.

What data can you extract from G2?

Each dataset row is one review. The field names below are the keys in the JSON export; optional fields remain null when the public source does not supply a value.

Field	Description	Example
id	Stable numeric G2 review ID from the review URL	"13613176"
rating	Star rating, including half stars	5
title	Review headline	"Versatile App with Plenty of Integrations for Personal and Bu…
likesText	Answer to what the reviewer likes best	"I like the number of integrations that can be done with exter…
dislikesText	Answer to what the reviewer dislikes	"The huddles don't have much capabilities that would allow us …
date	Published date and time with UTC offset	"2026-09-25T13:08:44-05:00"
reviewerName	Public reviewer label in the feed	"Verified User in Information Technology and Services"
reviewerRole	Reviewer's role, when provided	"Administrator"
reviewerIndustry	Reviewer's industry, when provided	"Information Technology and Services"
reviewerCompanySize	Company employee range, when provided	"51-200 employees"
isVerified	Verification status; null because G2 RSS does not expose its review verification badge	null
productName	G2 product name	"Slack"
reviewUrl	Direct G2 review URL	"https://www.g2.com/products/slack/reviews/slack-review-13613176"

How to scrape G2

1. Add one or more G2 product slugs, such as slack, or product review URLs to products.
2. Use product only when the products list is empty; choose maxItems for a total review cap.
3. Run the actor and check the returned productName and reviewUrl for each row.
4. Download the dataset as CSV, JSON, or Excel for analysis.

A run stores its results in the default dataset. Review a few rows there before exporting, especially if you plan to combine several searches or compare snapshots over time. Keep the record ID or source URL in your export so you can remove duplicates and return to the public page behind a result.

Input

Enter these values in the actor input. A prefilled example is a starting point, while a default is the value used when the field is omitted. Required inputs are identified below.

Name	Type	Default	Description
products	array	["slack", "https://www.g2.com/products/asana/reviews"]	G2 product slugs or product review URLs. One product returns up to 25 recent reviews; add more products for a larger dataset.
product	string	"—"	One G2 product slug or review URL. Used when the products list is empty.
maxItems	integer	100	Maximum reviews across all products. Set to 0 for no cap.
proxyConfiguration	object	{"useApifyProxy": true}	Apify Proxy settings. The actor starts with datacenter IPs and escalates to residential on blocks.

A realistic input:

{
  "products": [
    "https://www.g2.com/products/slack/reviews",
    "asana"
  ],
  "maxItems": 50
}

The example keeps the run small enough to inspect while showing the main search path. Increase the item limit only after the initial results match your intended scope. When multiple filters are available, start with the most important one and then refine the next run based on the public results.

Output example

The following item came from a successful published run. Long review or description text is shortened here for readability; the dataset keeps the full value.

{
  "id": "13613176",
  "rating": 5,
  "title": "Versatile App with Plenty of Integrations for Personal and Business Use",
  "likesText": "I like the number of integrations that can be done with external apps. I also appreciate that it can be used for both personal and business use.",
  "dislikesText": "The huddles don't have much capabilities that would allow us to move away from other video conferencing tools.",
  "date": "2026-09-25T13:08:44-05:00",
  "reviewerName": "Verified User in Information Technology and Services",
  "reviewerRole": "Administrator",
  "reviewerIndustry": "Information Technology and Services",
  "reviewerCompanySize": "51-200 employees",
  "isVerified": null,
  "productName": "Slack",
  "reviewUrl": "https://www.g2.com/products/slack/reviews/slack-review-13613176"
}

The example shows the shape of one record, not a guarantee that every optional value will be present in every result. CSV and Excel exports present the same fields as columns. JSON retains the native number, boolean, array, and null values that make further filtering easier.

Use cases

• Product teams can compare recent praise and complaints for competing software products.
• Customer research teams can group dislikes by industry or company size when those fields are supplied.
• Sales enablement teams can monitor current objections mentioned in public product reviews.
• Analysts can compare recent rating distributions across a list of product slugs.
• Software buyers can collect review links and read the original context before shortlisting.

These workflows use public listing or review data as a starting point. Check the linked source page before acting on a single record, because public content, counts, and availability can change after a run. Store the run date alongside each export when comparing data over time.

How much does it cost to scrape G2?

The price is $1.00 per 1,000 results, with platform usage included. Charges scale with the number of results produced: 100 results cost $0.100, 1,000 results cost $1.00, and 5,000 results cost $5.00. The 5,000-result figure is a pricing illustration, not a promise that a single search exposes that many public results. Set the item limit for the amount of data you need and check the actual result count after the run.

Tips for best results

Add several products when you need more than one feed can provide. One product exposes up to 25 recent entries, so a maxItems value above 25 does not expand that product’s coverage. A total limit applies across all selected products, and the list order determines which products are read first if the cap is reached. Use productName to separate records after export. Repeat a run later to capture newly published entries; store reviewUrl or id to remove duplicates across snapshots. Use a product slug or a full supported product URL, not a category or search URL.

When creating a repeatable workflow, save the input that produced a useful dataset. Compare later exports by stable ID where available, and inspect a few source links whenever a value matters to a decision. A narrow input usually makes the output easier to validate and reduces time spent processing unrelated results.

Limits

G2’s public product review feed exposes up to 25 recent reviews per product and does not paginate. This actor therefore cannot provide a full review history for one product. Empty likes or dislikes answers, roles, industries, and size ranges are represented as null when the feed lacks them. isVerified is always null because the public feed does not expose the review verification badge; a reviewer label containing “Verified User” does not change that field. Review date includes the source UTC offset when available.

These limits come from the public data exposed to the actor at run time. Source pages can change, so a run can return fewer rows than its requested maximum. A maximum item count is a stop condition, not a minimum or a guarantee of coverage. Use the source URL in each row when you need to verify a particular detail.

Use with AI agents (MCP)

The actor can be called through the Apify MCP connection with the same input fields shown above. Ask for a bounded result set and name the fields that matter to the task. A useful prompt also asks for source links, so each conclusion can be checked against its public record.

• “Collect the latest G2 reviews for Slack and Asana, then group recurring dislikes by product.”
• “Export recent G2 ratings and review URLs for three software products and compare the themes in likesText.”

For recurring research, keep the search input and the requested output columns consistent. Compare records by their IDs or source URLs when results from different runs are combined. If an optional field is missing, keep it empty in downstream analysis instead of guessing a value.

FAQ

Is it legal to scrape this data? This actor reads public data. Follow the source site’s terms, applicable law, and GDPR requirements. Do not misuse personal data or republish material without the rights to do so.

Do I need to provide proxies? No. The default proxy configuration is already supplied. You can override proxyConfiguration if your workflow requires different settings.

How fast is a run? Time depends on the number of pages or products, the selected limits, and how quickly public pages respond. Start with a small limit to estimate the time for your search; fetching optional detail pages can add time.

Can I schedule and monitor runs? Yes. Use Apify scheduling and run monitoring for repeat collections. Save each run’s dataset so you can compare snapshots and spot missing or changed records.

Can I export to Google Sheets or Excel? Yes. Download CSV, JSON, or Excel from the dataset, or connect the dataset to Google Sheets through an integration. Keep IDs or URLs as columns for deduplication.

What if a field is empty? The public source did not supply that value for the record, or the value was unavailable in the view used. Treat null as missing data, not as zero or a negative answer.

Can I scrape more than 25 reviews for one product? No. The public feed has at most 25 recent entries for one product; add more products for a larger combined dataset.

Can I pass a product URL instead of its slug? Yes. A G2 product page or product reviews URL is accepted.

Does isVerified identify verified reviewers? No. The public feed omits the verification badge, so the field is null.

Integrations

Use the Apify API to start runs and read the default dataset. Webhooks can notify another system when a run finishes; Zapier, Make, and n8n can move results into reporting workflows or Google Sheets. Scheduling supports regular snapshots, and exported CSV or Excel files work for manual analysis. Keep the source record ID and URL in downstream systems so updates can be matched to earlier exports.

Support

Open an issue on the Issues tab; we reply within 24h and add fields on request.