Gartner Peer Insights Scraper

Gartner Peer Insights Scraper: enterprise software reviews with who actually wrote them

Paste a Gartner Peer Insights product link and every review comes back as a row: headline, full text, the 1 to 5 star rating, the date, and the reviewer's job title, function, industry and company size, alongside the product, vendor and Gartner market. No Gartner account, nothing to install.

The awkward part first. Gartner publishes verbatim pros and cons for only two reviews per market, the favourable one and the critical one it spotlights. Every other row leaves those fields null, and no setting changes that. The review body, rating and reviewer profile are there on all of them.

🏷️ What Gartner Peer Insights Scraper does

Three forms of input all work: the short product URL out of your address bar, the long market/vendor/product URL, or just the slug. A URL that names a market pins the run to that market. One that does not walks every market the product sits in, which is how you get the full review set for something like Azure Data Factory that Gartner files under three of them.

searchTerms takes a product name instead and uses Gartner's top match. It costs one extra request and it can land on the wrong product, so a URL is always the safer input.

Two ways to narrow it. recentMonths is applied by Gartner itself, so nothing older is even read. starRatings is not: Gartner has no rating filter, so the run reads reviews and drops the ones you did not ask for. That still means you only pay for rows you keep, but hunting a rare rating burns reading time, and scanning stops after roughly 12 reads per review you asked for.

Reviews arrive in Gartner's own helpfulness order. Gartner offers no date sort at all, so recentMonths is the way to get recent ones.

📥 What you give it

{
  "productUrls": [
    "https://www.gartner.com/reviews/product/azure-data-factory",
    "https://www.gartner.com/reviews/market/meeting-solutions/vendor/zoom/product/zoom-meetings/reviews",
    "snowflake"
  ],
  "maxReviewsPerProduct": 200,
  "recentMonths": 12
}

📤 What you get back

A real summary row from a run on 14 September 2026, with the long description cut short:

{
  "_summary": true,
  "productName": "Azure Data Factory",
  "productSeoName": "azure-data-factory",
  "productUrl": "https://www.gartner.com/reviews/product/azure-data-factory",
  "vendorName": "Microsoft",
  "vendorSeoName": "microsoft",
  "markets": ["Data Integration Tools", "Integration Platform as a Service", "Service Orchestration and Automation Platforms"],
  "averageRating": 4.4,
  "ratingsCount": 126,
  "ratingDistribution": { "3": 10, "4": 59, "5": 57 },
  "reviewsOnGartner": 132,
  "reviewsScanned": 9,
  "reviewsCollected": 3,
  "productDescription": "Azure Data Factory is a software that enables organizations to orchestrate and automate data movement and transformation across various sources and destinations...",
  "pricingModel": "Azure Data Factory software uses a consumption-based pricing model where customers are charged based on factors such as the number of pipeline activities executed...",
  "requestsUsed": 9,
  "_charged": false
}

Review rows carry these instead:

🧾 Reading the output

_charged marks which kind of row you are holding. It is stamped as the row is written, so read it as the row type rather than as a receipt.

▶️ How to run it

1. Open Gartner Peer Insights Scraper and click Try for free. 2. Paste product links into Gartner Peer Insights product URLs, one per line. 3. Set Only reviews from the last N months if you want recent ones, since there is no date sort. 4. Set Maximum reviews per product. Start around 25 to see what the rows look like. 5. Click Start, then download the dataset as JSON, CSV or Excel, or read it from the Apify API.

💰 How much does it cost?

$0.75 per 1,000 reviews. Flat on every Apify plan, with no volume tiers.

One billed row per review delivered. The product summary row is not billed, the pros and cons pass is not billed, diagnostic rows are not billed, and reviews dropped by your star filter never reach the dataset and are not billed.

💡 What people use it for

• Win-loss research: pulling the 1 and 2 star reviews of a rival product and reading what the

reviewers do for a living.

• Sizing a segment, because reviewerIndustry and companySize sit on every row.
• Watching a market on a schedule with recentMonths set to 1, and diffing on reviewId.
• Pulling the ratings distribution and Gartner's own review count for several products at once,

from the summary rows.

• Quoting real customer language in positioning work, with a link back to each review.

🚧 What it does not do

• No per-review pros, cons or sub-scores, except the two spotlight reviews per market. Gartner

keeps the rest behind its login.

• No date sorting. Gartner's API has none. Use the recency window instead.
• reviewDate is a display string, so it cannot be sorted or compared until you parse it.
• A product can sit in up to 12 markets, and a URL that names one market gets only that one.
• Reviews for one product are written at the end, after every market and the summary finish, so

a run that hits the platform time limit on a very large product delivers nothing for it. Split large products across runs.

• If the first product cannot be read, the rest of the run is skipped and those products come

back as skipped diagnostics. Re-run them.

• A rare star rating can come back short. Scanning is bounded, so asking for 1 star reviews on a

product that barely has any will return fewer than you asked for.

🧭 Which review scraper do you need?

❓ Questions people ask

Do I need a Gartner account? No. Everything here is what a logged-out visitor can read.

Why are pros and cons empty? Gartner only publishes them for the two reviews it spotlights in each market. Those rows get them; the rest cannot.

How do I get only recent reviews? Set Only reviews from the last N months. Gartner applies it, so older reviews are never read.

Why did I get fewer reviews than I asked for? Either the product has fewer, your recency window cut them, or a star filter is rarer than the scan budget allows.

Can I give it a product name instead of a link? Yes, with Or search by product name. It takes Gartner's top match, which can be the wrong product, so a URL is safer.

Is this legal? These are public review pages. The reviewer details Gartner publishes are pseudonymous job descriptions rather than names. Apify's write-up on scraping and the law is a good starting point, and we are not lawyers.

🆘 If something breaks

Open the Issues tab on the actor page. Send the product URL you used and the run ID. The reason on the diagnostic row usually names the problem on its own.