Pay-per-review: $2.00/1K reviews. Reviewer role + company name included. No login, no proxy, no monthly fee.

B2B software buyers trust TrustRadius more than any other review platform. This scraper gives you full review text, reviewer job title, company name, and rating — without a TrustRadius account. Perfect for competitive intelligence, NPS analysis, and building targeted account lists.

Scrape B2B software reviews from TrustRadius — the leading platform for peer reviews of enterprise software.

No proxy required — works from Apify cloud IPs without external credentials.

What you get

Field	Description
review_id	Stable MD5 hash of review slug
product	Product name
title	Review headline
rating / rating_max	Numeric rating (scale shown in rating_max)
body	Full use-cases / main review text
pros	List of listed pros
cons	List of listed cons
author	Reviewer display name
reviewer_role	Job title / role (e.g. "Director in Information Technology")
reviewer_company	Company name (e.g. "Devtics Technologies")
date	ISO 8601 review date
url	Direct link to review
aggregate_rating	Product overall rating
aggregate_count	Total number of ratings on the product
parse_confidence	0–1 quality score per record
warnings	Machine-readable quality flags

Input

{
  "productUrls": ["slack", "hubspot", "https://www.trustradius.com/products/salesforce/reviews"],
  "maxReviews": 25,
  "maxPages": 10
}

• productUrls — TrustRadius product slugs or full review URLs
• maxReviews — max reviews per product (0 = no limit)
• maxPages — max pages to fetch per product (~5 reviews/page)

Pricing example

Reviews scraped	Cost
100 reviews	$0.20
500 reviews	$1.00
1,000 reviews	$2.00
10,000 reviews	$20.00

No proxy, no login, no monthly fee — pay only for results returned.

Output sample

{
  "review_id": "a3f8b1c2d4e5f6a7",
  "product": "Slack",
  "title": "Great for async teams, but noisy at scale",
  "rating": 8,
  "rating_max": 10,
  "body": "We use Slack across 5 departments. The thread model helps async collaboration but notification fatigue is real above 20 channels.",
  "pros": ["Deep integrations", "Search history", "Mobile app quality"],
  "cons": ["Notification overload", "Channel sprawl", "Expensive at enterprise tier"],
  "author": "Jane D.",
  "reviewer_role": "Director of Engineering",
  "reviewer_company": "Acme SaaS Inc.",
  "date": "2025-11-04",
  "url": "https://www.trustradius.com/reviews/slack-2025-11-04-1",
  "aggregate_rating": 8.4,
  "aggregate_count": 1247,
  "parse_confidence": 0.95,
  "warnings": []
}

FAQ

Do I need a TrustRadius account or login? No — TrustRadius renders reviews in static HTML accessible without authentication. No account, no cookie, no proxy needed.

What export formats are available? JSON, CSV, Excel, and XML — all downloadable from the dataset page or via the Apify REST API.

Can I track competitor reviews on a schedule? Yes — enable monitoringMode: true and set up a recurring Apify Scheduler run. A low-cost monitoring charge is applied per run instead of per-review charges.

What if the actor returns 0 reviews for a product? Confirm the product slug exists on trustradius.com (e.g. slack, salesforce, hubspot). TrustRadius only serves ~5 reviews per page in static HTML; some niche products may have fewer pages.

Why this beats the competition

	This actor	Other review scrapers
reviewer_role (job title)	Yes	Rarely
reviewer_company	Yes	No
pros / cons structured	Yes (lists)	Raw text only
No login required	Yes	Often requires cookie
Rating scale preserved	Yes (rating_max)	Normalized only
parse_confidence	Yes	No
Price	$2.00/1K	$3-5/1K

Every record ships a parse_confidence score (0.0–1.0). Below 0.7 is a machine-readable signal that the page structure has drifted — your data pipeline can filter automatically.

Use with AI agents (MCP)

This scraper is callable as a tool by AI agents (Claude Desktop, Cursor, VS Code, n8n, LangGraph, CrewAI, or any MCP-compatible client) via Apify's hosted Model Context Protocol server. An agent can pull competitor reviews mid-conversation: "Give me the top 20 TrustRadius reviews for Salesforce, sorted by reviewer company size."

Point your MCP client at this tool:

{
  "mcpServers": {
    "apify": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.apify.com/?tools=bovi/trustradius-reviews",
        "--header",
        "Authorization: Bearer <YOUR_APIFY_TOKEN>"
      ]
    }
  }
}

Monitoring mode — recurring brand tracking

Set monitoringMode: true and schedule a weekly run to track competitor review velocity and rating changes. A monitoring-run event is charged once per run (priced lower than scraping new reviews) — ideal for ongoing competitive intelligence without paying full scrape cost every time.

{
  "productUrls": ["salesforce", "hubspot"],
  "maxReviews": 10,
  "monitoringMode": true
}

Schedule via Apify Scheduler (or n8n/Zapier) for hands-off brand tracking.

Notes

• TrustRadius renders ~5 reviews per page in static HTML; the rest are loaded client-side. This actor harvests the server-side rendered batch. Static HTML = fast, stable, cheap — no JS execution needed.
• reviewer_role and reviewer_company are available when the reviewer has filled in their profile.
• Not affiliated with TrustRadius.

Integrations

Built for B2B competitive-intelligence and sales teams mining enterprise software reviews with reviewer role and company data — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.