G2 Data Alternative: Capterra, Gartner and Trustpilot Reviews via API and MCP

Looking for a g2 api alternative? NeuralVerge has no G2 data, and this article does not pretend otherwise. What it has is three other review platforms (Capterra, Gartner Peer Insights and Trustpilot), each reachable as a lookup that returns structured JSON over REST or as an MCP tool. For software buyer opinion as data, they cover much of the same ground from different angles. The source pages are the reference for exact fields and prices.

The short version

• —The verdict: this is a different set of sources, not a drop-in swap. You get ratings, pros, cons and reviewer details from three platforms, but not G2's numbers or G2's field names.
• —Who it is for: analysts, product marketers and agent builders who want buyer opinion as data and are happy to compare platforms rather than rely on one.
• —Who it is not for: teams that specifically need G2 reviews or G2 rankings. For that, go to G2 and check its current terms.
• —What it costs: review listings are 1 point per returned review ($1 per 1,000), capped by max_items. Profile lookups are 5 points per result ($5 per 1,000), and the Gartner and Capterra profile sources do not bill misses.
• —How you reach it: the same lookups work over REST and as MCP tools. The company intelligence article covers the wider catalog.

At a glance:

G2 and where it wins

G2 is one of the best-known software review marketplaces, and for many buyers and vendors it is the default place to look. That is a real advantage: a vendor that has put effort into collecting G2 reviews will often have more there than anywhere else, and the people you are researching probably already point to it.

G2 also documents its own access route. Its developer portal describes creating an access token, choosing an access level for each endpoint, and testing against the products endpoint. Review syndication for partners is set up separately, through a G2 representative. Which endpoints you can reach, and on what terms, depends on your relationship with G2, so check its current documentation rather than relying on this summary.

I am not going to describe G2's pricing, rate limits or coverage here, because those terms change. If G2 data is what you need, G2 is where to get it.

What NeuralVerge offers instead

NeuralVerge treats review platforms as data sources inside one platform, sharing one account and one points balance. The three platforms are not copies of each other, which is the useful part: a product's score on one is a different signal from its score on another.

Capterra: reviews as rows

Per its source page, Capterra is a software-selection catalog that leans toward small and mid-sized business buyers. The Capterra product reviews source takes a product ID or URL and returns the review listing. Each review includes the overall rating, sub-ratings (ease of use, customer support, functionality, value for money), pros, cons, free text, a likelihood-to-recommend score where given, the written date, whether the review was incentivized, the alternatives the reviewer considered, any vendor response, and reviewer details such as job title and industry. It costs 1 point per returned review, up to 500 per request.

Gartner: the enterprise rating and the vendor behind it

Gartner Peer Insights collects reviews from IT buyers reviewing enterprise software for other IT buyers, which skews toward larger, more considered purchases. Gartner product takes a Peer Insights product alias or URL and returns the profile: vendor, average rating, review count, the distribution of one- to five-star ratings, the market it is listed in, and the vendor's company profile. It does not return individual review text, so if you need the words, pair it with Capterra or Trustpilot reviews.

Trustpilot: the customer-experience side

Trustpilot is a general consumer-review platform, so its reviews cover the whole customer relationship (support, billing, delivery), not only software features. The Trustpilot reviews source takes a business domain, a Trustpilot review URL or a company website URL and returns reviews with rating, title, text, dates, whether the review was unprompted or verified, language, author details and the business reply where there is one. It costs 1 point per returned review, up to 300 per request.

The supporting lookups

Each platform also has search and listing sources, so you can start from a keyword or a category rather than an ID:

These are lookups, not filtered searches: to find every product in a category, start with the category sources, then fan out into per-product calls. Exact request shapes are in the docs at https://docs.neuralverge.ai.

REST and MCP: one real request

The mechanics are the same across the platforms. You resolve what you are asking about (if you only have a name, run the matching search source first and take the ID or alias from the result; searching is cheap and stops you paying for reviews of the wrong product). You send one request, and there is no job to poll. The response comes back with a session id, the input you sent, a short summary, and a machine object with the structured data. It ends with total_points.

Here is a Gartner Peer Insights product lookup, taken from the Gartner product source page. The product is Forcepoint DLP, a data loss prevention product, the same alias the page uses in its own example.

The request over REST

curl -X POST https://api.neuralverge.ai/functions/v1/run-gartner-products \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "product": "forcepoint-dlp" }'

The same request over MCP

Each source is a tool on the hosted MCP server, with the same arguments as the REST call. Tool names use underscores:

{ "tool": "run_gartner_product", "arguments": { "product": "forcepoint-dlp" } }

An agent with the server connected can call this itself, which suits ad hoc questions; REST suits scheduled pipelines. For setup, see connecting the MCP server to Claude.

The response

This is a captured response from the source page. Long text fields in the sample are cut off at the length the page shows them, and the session id is a placeholder, as it is on the page:

{
  "session_id": "11111111-1111-1111-1111-111111111111",
  "kind": "gartner_product",
  "product": "forcepoint-dlp",
  "human": "# Gartner Peer Insights product\n\n- **Product:** forcepoint-dlp\n- **Response size:** 632.9 KB\n- **Name:** Forcepoint DLP\n- **Vendor:** Forcepoint\n- **Rating:** 4.3 (613 reviews)\n- **Market:** Data Loss Prevention",
  "machine": {
    "product": {
      "id": "11424",
      "alias": "forcepoint-dlp",
      "name": "Forcepoint DLP",
      "vendor_id": "4226",
      "vendor_name": "Forcepoint",
      "vendor_alias": "forcepoint",
      "image": null,
      "average_rating": 4.3,
      "review_count": 613,
      "rating_distribution": {
        "star_1": 1,
        "star_2": 8,
        "star_3": 48,
        "star_4": 284,
        "star_5": 272
      },
      "description": "Forcepoint Data Loss Prevention helps organizations prevent sensitive data loss across endpoints, AI interactions, web, cloud applications, email, and networks. Forcepoint provides unified policy mana",
      "pricing_description": null,
      "is_service": null,
      "is_legacy": false,
      "updated_at": "24th September 2026",
      "screenshots": [
        "/pi/vendorimages/productScreenshots/forcepoint_forcepoint-dlp_enterprise-data-loss-prevention_1652469757383_1.png",
        "/pi/vendorimages/productScreenshots/forcepoint_forcepoint-dlp_enterprise-data-loss-prevention_1652469757383_2.png"
      ],
      "market": {
        "id": "412",
        "name": "Data Loss Prevention",
        "alias": "data-loss-prevention",
        "review_count": 3526,
        "url": "https://www.gartner.com/reviews/market/data-loss-prevention"
      },
      "company": {
        "name": "Forcepoint",
        "alias": "forcepoint",
        "description": "The Forcepoint AI Data Security platform knows sensitive data as it is created, adapts the moment risk changes and protects continuously across cloud, web, networks, email, endpoints and AI through a ",
        "company_type": "Private",
        "founded_year": 1994,
        "headquarter_city": "Austin",
        "headquarter_country": "United States",
        "min_employee_range": 2500,
        "max_employee_range": 4999,
        "revenue": null,
        "website_url": "www.forcepoint.com",
        "parent_company": null
      },
      "url": "https://www.gartner.com/reviews/product/forcepoint-dlp"
    },
    "proxy_traffic_bytes": 648045
  },
  "total_points": 5
}

How to read it

Start with average_rating and review_count: 4.3 across 613 reviews. The rating_distribution tells you more than the average does. Here, most of the weight sits at four and five stars, with very few at one and two. A product with the same 4.3 and a lumpier distribution would be a different story.

The market object tells you which category Gartner lists the product in, and how many reviews that whole category has. The company object gives the vendor's profile: type, founding year, headquarters, employee range. Fields the profile does not publish come back as null (here revenue, pricing_description and parent_company), so your code should expect gaps rather than treating them as errors.

The cost is on the last line: total_points is 5, which matches the source page's 5 points per result. A product that cannot be found is not billed.

A worked example: two platforms side by side

A single platform gives you a rating. Two give you a comparison. Here is an illustrative scenario; the company and workflow are made up, and no figures are implied.

Acme Oy, a Finnish software company, sells to both small businesses and large enterprises and wants to know how buyers talk about the two competitors it meets most often. An analyst:

1. —Runs Capterra products search for each competitor's name and takes the product IDs.
2. —Calls Capterra product reviews with a modest max_items for each, to get the pros, cons and the alternatives reviewers say they considered.
3. —Runs Gartner products search for the same names, then Gartner product for the aliases, to see the enterprise rating and rating distribution.
4. —Compares the two: does the small-business crowd complain about the same things the enterprise crowd rates well?

The point is not the answer. It is that the analyst did four cheap, repeatable steps instead of tabbing between sites, and every record carries a session id. An AI agent doing the same job over MCP reads the same fields, and the AI research pipeline can summarize the records into a written brief if you want one.

Moving from G2: mapping, cost and limits

Mapping your existing code

If you have code built around G2's response shape, you will need a mapping layer to these platforms' fields, and some fields will not have an equivalent. Rating scales, reviewer populations and record shapes differ, so do not expect the same values. Keep each platform's rating as its own column rather than merging them into one score.

What it costs

• —Review listings (Capterra product reviews, Trustpilot reviews) are 1 point per returned review, which the pages state as $1 per 1,000. You set max_items to cap the spend. Capterra defaults to 10 reviews and allows up to 500; Trustpilot defaults to 25 and allows up to 300.
• —Profile lookups (Gartner product, Capterra product, Trustpilot) are 5 points per result, $5 per 1,000.
• —Misses: the Gartner product page states that a product that cannot be found is not billed, and the Capterra review page states that a product with no reviews costs nothing. For other sources, check the page.
• —Plan prices are on the pricing page; this article only quotes the per-lookup figures the source pages state.

Limits to plan for

• —Reviews are self-selected. They skew toward strong opinions in both directions.
• —Vendors influence volume. A vendor that asks every customer for a review will have more of them, so volume is not a neutral signal.
• —Thin samples mislead. A product with few reviews gives a sample that may not represent its users.
• —Time budgets. A review lookup that hits its time budget returns partial: true with what it collected so far. Re-run with a smaller max_items for a complete result.
• —Terms of use. Review data is publicly visible, but how you may use it depends on your situation. Read the platforms' terms and the documentation, and ask a lawyer if the use is commercial or sensitive.

How to decide

1. —Do you need G2 specifically? If yes, this is not the route. If you need buyer opinion on software and the platform matters less, read on.
2. —Which population do you need? Small-business buyers, enterprise IT buyers, or general customers. Pick the platform that matches the question.
3. —Do you need text or only scores? Gartner product gives scores and distribution; Capterra and Trustpilot reviews give text.
4. —How many reviews, and how spread out? A rating from a handful of reviews is weak evidence, and an average hides polarization, so check the count and the distribution.
5. —Is the sample recent? Check the written dates, and re-run on a schedule if freshness matters. Capterra reviews also carry an incentive field you can filter on.
6. —Can your code tolerate nulls? Fields the platform does not publish come back as null.
7. —What is the spend cap? Set max_items deliberately and estimate points before a large run.
8. —What will you use it for? The good fits are voice-of-customer research (mining the phrases buyers use across hundreds of reviews), competitive tracking (re-running the same products on a schedule), win-loss context, vendor due diligence, and giving an agent a review lookup as a tool so answers come from a record, not from memory.
9. —REST or MCP? Use REST for scheduled pipelines and MCP when an agent should call a source itself.

Frequently asked questions

Does NeuralVerge offer G2 data?

No. NeuralVerge has no G2 source, so this article is not about replacing G2 data one for one. It is about getting comparable software-review data from other review platforms: Capterra, Gartner Peer Insights and Trustpilot.

Is this a drop-in replacement for the G2 API?

No. The review platforms have different populations of reviewers, different rating scales and different fields, so the numbers are not interchangeable. Treat each platform as its own signal and compare them side by side rather than expecting the same values you would get from G2.

Which source should I use to read review text?

Capterra product reviews returns review text with pros, cons, ratings and reviewer details for software products. Trustpilot reviews returns review text for a business from the customer-experience side. Gartner product returns the rating, the rating distribution and the vendor profile rather than individual review text.

What does a review lookup cost?

Capterra product reviews and Trustpilot reviews cost 1 point per returned review, which is $1 per 1,000, and you set the cap with max_items. Gartner product costs 5 points per result, which is $5 per 1,000, and misses are not billed.

Can I use these review sources over MCP?

Yes. Each of these sources is available over REST and as a tool on the hosted MCP server, with the same arguments in both, for example run_gartner_product. Add the server to an MCP client, sign in, and an agent can call the lookup itself.

Are review ratings reliable enough to base decisions on?

Treat them as one signal, not a verdict. Reviews are self-selected and skew toward strong opinions in both directions, vendors can influence how many reviews they collect, and products with few reviews give a thin sample. Check the review count and read a sample of the text before relying on an average.

Does G2 have an API I can use directly?

G2 documents its own developer portal, with access tokens and per-endpoint access levels, and review syndication access is arranged through a G2 representative. What you can reach, and on what terms, depends on your relationship with G2, so check its current documentation. NeuralVerge does not resell or proxy G2 data.