{"id":2044,"date":"2026-10-05T14:33:24","date_gmt":"2026-10-05T14:33:24","guid":{"rendered":"https:\/\/predictleads.com\/blog\/?p=2044"},"modified":"2026-10-05T14:33:26","modified_gmt":"2026-10-05T14:33:26","slug":"technology-dataset-provider-tech-stack-api","status":"publish","type":"post","link":"https:\/\/predictleads.com\/blog\/technology-dataset-provider-tech-stack-api\/","title":{"rendered":"Inside a Technology Dataset: What 2B+ Company Tech Stack Detections Reveal (October 2026)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Most teams buy technographic data to answer one narrow question: does this account use a given tool? A technology dataset provider can tell you far more than that, including which vendors hold the widest footprint in each buying category, how detection coverage has grown, and which companies show evidence of switching from one vendor to a direct competitor. This post walks through what PredictLeads&#8217; technology dataset shows across 2,003,496,359 active company-technology detections, how to read those numbers without overreaching, and how to pull the same data into your own systems through an API.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>TLDR:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A technology dataset pairs a catalog of technologies with timestamped detections that provide evidence of which technologies a company uses or has recently used.<\/li>\n\n\n\n<li>The overall top 20 is dominated by web infrastructure such as jQuery (46,082,903 detections) and PHP (43,868,272), so buying intent lives in category leaderboards, not raw totals.<\/li>\n\n\n\n<li>Detection counts measure the breadth of a technology&#8217;s public footprint, not revenue share: Zoho (583,779) and HighLevel (269,326) both outnumber Salesforce (240,716) in CRM detections.<\/li>\n\n\n\n<li>Change is the most valuable signal: a technology switch event shows evidence that a company moved from one technology to a direct competitor, which no current-state snapshot can show.<\/li>\n\n\n\n<li>PredictLeads tracks 73,189 live technologies across 302 categories, backed by 2B+ active detections over 133.5M+ companies, and has flagged 51,140 technology switch events to date, 9,322 of them high importance.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Figures in this post reflect PredictLeads&#8217; internal dataset as of October 2026, refreshed weekly. They are a point-in-time snapshot, not live counts.<\/em><\/p>\n\n\n\n<h2 id=\"h-what-a-technology-dataset-is-and-what-it-is-not\" class=\"wp-block-heading\">What a Technology Dataset Is (and What It Is Not)<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A technology dataset is a structured record of which technologies companies use or have recently used, built from public evidence and timestamped so you can see when each technology was first and last detected. It has two layers. The catalog (the Technologies dataset) defines each technology: its name, categories, pricing data, parent technology, and the direct competitors it is mapped against. The detections (the Technology Detections dataset) link one company to one technology, with the evidence and dates behind the match.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Because every detection is evidence rather than a confirmed install, the data has clear limits:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Not an install confirmation.<\/strong> A detection comes from public sources such as website code, Domain Name System (DNS) records, and job postings, not from inside a company&#8217;s systems.<\/li>\n\n\n\n<li><strong>Not a spend database.<\/strong> A detection count shows how widely a technology appears, not what anyone pays for it. Pricing data, where attached, gives you a range for estimating budget.<\/li>\n\n\n\n<li><strong>Not a contact database.<\/strong> Technographic records describe companies, not people.<\/li>\n\n\n\n<li><strong>Not a one-time scrape.<\/strong> The <code>first_seen_at<\/code> and <code>last_seen_at<\/code> fields turn the dataset into a timeline, and a timeline is what makes change detection possible.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">PredictLeads&#8217; catalog holds 73,189 live technologies (those that meet the detection-quality threshold and are not suppressed), drawn from 254,367 cataloged over time. Every live technology has at least one real-world detection behind it.<\/p>\n\n\n\n<h2 id=\"h-the-predictleads-technology-dataset-by-the-numbers\" class=\"wp-block-heading\">The PredictLeads Technology Dataset by the Numbers<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The PredictLeads technology dataset covers 73,189 live technologies and 2,003,496,359 active detections across 133,531,593 companies in 208 countries. The PredictLeads technology dataset by the numbers, October 2026 Six stat tiles: 2,003,496,359 active company-technology detections; 73,189 live technologies out of 254,367 ever cataloged; 302 categories in use; 133,531,593 companies tracked; 208 countries represented; 51,140 technology switch events graded by importance. 2B+ 73,189 302 133.5M+ 208 51,140 active detections live technologies categories in use companies tracked countries represented technology switch events 2,003,496,359 confirmed matches of 254,367 ever cataloged across the technology catalog 133,531,593 in total in the company dataset graded high, medium, and low Source: PredictLeads internal dataset as of October 2026, refreshed weekly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The catalog is where a raw count becomes a usable filter. Five attributes do most of the work:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Attribute<\/th><th class=\"has-text-align-left\" data-align=\"left\">Count<\/th><th class=\"has-text-align-left\" data-align=\"left\">What it lets you do<\/th><\/tr><\/thead><tbody><tr><td>Software-as-a-service (SaaS) products<\/td><td>34,721<\/td><td>Focus on subscription software with renewal cycles<\/td><\/tr><tr><td>Open-source technologies<\/td><td>1,350<\/td><td>Separate free tooling from paid vendors<\/td><\/tr><tr><td>Known pricing data<\/td><td>10,401<\/td><td>Estimate the budget behind a detection<\/td><\/tr><tr><td>Technology families<\/td><td>13,676 under 4,214 parent technologies<\/td><td>Roll related products up to one parent technology<\/td><\/tr><tr><td>Competitor pairs<\/td><td>115,412 pairs covering 24,636 technologies<\/td><td>Build displacement lists and detect switches<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 id=\"h-where-the-evidence-comes-from-five-signal-types\" class=\"wp-block-heading\">Where the Evidence Comes From: Five Signal Types<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">PredictLeads verifies detections through five independent signal types, and each one sees a different part of a company&#8217;s stack. Active detections by signal type, October 2026 Horizontal bar chart. Website pages and content: 1,793,655,828 detections (89.5%). DNS records: 159,905,514 (8.0%). Job postings: 30,036,330 (1.5%). Customer reviews: 821,925 (0.04%). Website-to-website connections: 16,845 (about 0.0008%). About 19,000,000 detections found before 2023 predate per-source tracking. Active detections by signal type Website pages and content DNS records Job postings Customer reviews Website-to-website connections 1,793,655,828 (89.5%) 159,905,514 (8.0%) 30,036,330 (1.5%) 821,925 (0.04%) 16,845 (0.0008%) About 19,000,000 detections found before 2023 predate per-source tracking and are not attributed to a signal type. Source: PredictLeads internal dataset as of October 2026.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Website pages and content.<\/strong> Script tags, HTML, cookies, headers, and meta tags on public pages, where analytics, chat, e-commerce, and content management tools show up.<\/li>\n\n\n\n<li><strong>DNS records.<\/strong> MX, TXT, CNAME, and NS records can reveal email providers and services that require domain verification.<\/li>\n\n\n\n<li><strong>Job postings.<\/strong> Tools named as required skills, the behind-the-firewall source for CRMs, data warehouses, and business intelligence tools that leave no script on a website.<\/li>\n\n\n\n<li><strong>Customer reviews and website-to-website connections.<\/strong> Smaller sources that add independent confirmation.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Small shares are not small value. Job postings account for 1.5% of detections, yet in many cases they are the clearest public evidence of back-office software. A job-description mention can indicate current usage, planned adoption, migration work, or client work, so weigh it as evidence rather than proof (see <a href=\"https:\/\/predictleads.com\/blog\/job-openings-data-technographics\/\">how job openings data complements technographics<\/a>). Each detection carries a <code>source_count<\/code>, the <code>behind_firewall<\/code> field, and <code>seen_on_*<\/code> flags (<code>seen_on_subpages<\/code>, <code>seen_on_dns_records<\/code>, <code>seen_on_job_openings<\/code>, <code>seen_on_reviews<\/code>, <code>seen_on_connection<\/code>), so you can see how many signal types agree.<\/p>\n\n\n\n<h3 id=\"h-how-detection-coverage-has-grown-since-2021\" class=\"wp-block-heading\">How Detection Coverage Has Grown Since 2021<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Active detections first found in 2025 total 429,761,662, nearly four times the 107,953,437 first found in 2021. Active detections by year first found, 2021 to 2025 Vertical bar chart of currently active PredictLeads technology detections grouped by the year they were first found: 2021, 107,953,437; 2022, 128,994,782; 2023, 196,625,181; 2024, 182,018,255; 2025, 429,761,662. The partial 2026 year is excluded because its figures are still being finalized. Active detections by year first found, 2021 to 2025 400M 300M 200M 100M 0 108.0M 129.0M 196.6M 182.0M 429.8M 2021 2022 2023 2024 2025 Currently active detections grouped by first-found year. 2026 excluded: partial year, figures still being finalized. Source: PredictLeads internal dataset as of October 2026.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Two caveats keep this chart honest. Each bar counts detections that are still active, grouped by the year PredictLeads first found them, so older years only include evidence that has held up to today, and the curve reflects survivorship as well as coverage growth. Growth has also not been a straight line: the 2024 cohort (182,018,255) came in below 2023 (196,625,181). The 2026 figure is excluded because the year is partial and its figures are still being finalized.<\/p>\n\n\n\n<h2 id=\"h-what-technology-does-a-company-use-how-to-read-a-detection\" class=\"wp-block-heading\">What Technology Does a Company Use? How to Read a Detection<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To find out what technology a company uses, look up its domain and read each detection&#8217;s dates and sources rather than treating the result as a yes-or-no inventory. Four fields carry most of the meaning:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><code>first_seen_at<\/code>:<\/strong> when evidence first appeared. Tenure can help you estimate where an account sits in its contract cycle.<\/li>\n\n\n\n<li><strong><code>last_seen_at<\/code>:<\/strong> how recent the evidence is. An old date means the technology is no longer detected, which can come from a script change, a recrawl gap, or a signature change, not only from a company removing the tool.<\/li>\n\n\n\n<li><strong><code>source_count<\/code>:<\/strong> how many independent signal types support the match. A technology seen on the website and in job postings is stronger evidence than one seen once.<\/li>\n\n\n\n<li><strong><code>behind_firewall<\/code>:<\/strong> whether the evidence came from outside the website&#8217;s JavaScript and HTML, such as a job description.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The search tool further down this page does this lookup for any domain, and our walkthrough shows how to <a href=\"https:\/\/predictleads.com\/blog\/detect-company-technology-stack\/\">detect a company&#8217;s technology stack<\/a> step by step.<\/p>\n\n\n\n<h3 id=\"h-why-the-overall-top-20-is-mostly-web-plumbing\" class=\"wp-block-heading\">Why the Overall Top 20 Is Mostly Web Plumbing<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The most-detected technologies in the dataset are the building blocks of the web, not the tools you sell against. The top five are jQuery (46,082,903), PHP (43,868,272), Open Graph (41,959,547), Google Fonts API (38,373,446), and math.js (38,192,604). WordPress (34,653,062), MySQL (34,431,643), and Google Analytics (28,849,226) follow, alongside security settings such as HTTP Strict Transport Security (HSTS) at 27,007,308.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These counts are a useful stress test of coverage, but they rarely change a sales decision. <a href=\"https:\/\/ogp.me\/\">Open Graph<\/a> is a metadata protocol for rich link previews, and <a href=\"https:\/\/developer.mozilla.org\/en-US\/docs\/Web\/HTTP\/Reference\/Headers\/Strict-Transport-Security\">HSTS<\/a> is a response header that tells browsers to connect only over HTTPS. Neither says much about what a company buys. Filtering by category is what moves you from footprint to buying intent.<\/p>\n\n\n\n<h2 id=\"h-category-leaderboards-where-buying-intent-shows-up\" class=\"wp-block-heading\">Category Leaderboards: Where Buying Intent Shows Up<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Category leaderboards turn detection counts into a competitive map: which vendors hold the widest public footprint in the category you sell into, and how far ahead of the next vendor they sit. The catalog sorts live technologies into 302 categories, and a technology can carry more than one. Three reading rules keep the map accurate:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Footprint, not revenue.<\/strong> A detection count shows how widely a technology appears across companies, not how much those companies spend on it.<\/li>\n\n\n\n<li><strong>Suites span categories.<\/strong> Zoho, HighLevel, Jetpack, Cloudflare, and ServiceNow each appear in more than one leaderboard with identical counts, so never add numbers across categories.<\/li>\n\n\n\n<li><strong>Read the named vendors.<\/strong> Leaderboards also capture capabilities and versions (Cart Functionality, WooCommerce 3.0), which work as filters but are not competitors.<\/li>\n<\/ul>\n\n\n\n<h3 id=\"h-crm-why-footprint-is-not-the-same-as-spend\" class=\"wp-block-heading\">CRM: Why Footprint Is Not the Same as Spend<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">CRM: top 10 technologies by active detections Horizontal bar chart: Zoho 583,779; HighLevel 269,326; Salesforce 240,716; SAP 137,247; Intercom 136,868; Outseta 128,698; Microsoft Dynamics 62,966; vcita 55,844; ServiceNow 41,598; Agentforce 36,993. Zoho is the suite-level entry and also leads Accounting and Finance. CRM: top 10 technologies by active detections Zoho HighLevel Salesforce SAP Intercom Outseta Microsoft Dynamics vcita ServiceNow Agentforce 583,779 269,326 240,716 137,247 136,868 128,698 62,966 55,844 41,598 36,993 Zoho is the suite-level entry, so the same 583,779 also leads Accounting and Finance. Source: PredictLeads internal dataset as of October 2026.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Zoho leads CRM detections with 583,779, followed by HighLevel (269,326) and Salesforce (240,716). Zoho&#8217;s figure is the suite-level entry, which is why the identical number also leads Accounting and Finance. When you need one specific product, use product-level entries such as Zoho Books (18,463). Further down, Agentforce, Salesforce&#8217;s AI agent product, already shows 36,993 detections, close to ServiceNow&#8217;s 41,598.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">CRM counts also sit far below e-commerce counts, where Shopify alone has 15,777,484 detections. CRMs rarely leave a script on a public website, so in many cases their evidence comes from job postings and other off-site signals. That is why multi-source detection matters most for back-office software. For CRM-specific prospecting, see how to <a href=\"https:\/\/predictleads.com\/blog\/how-to-find-companies-using-hubspot-or-salesforce\/\">find companies using HubSpot or Salesforce<\/a>.<\/p>\n\n\n\n<h3 id=\"h-e-commerce-shopify-s-lead-and-the-version-layer\" class=\"wp-block-heading\">E-Commerce: Shopify&#8217;s Lead and the Version Layer<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Rank<\/th><th class=\"has-text-align-left\" data-align=\"left\">Technology<\/th><th class=\"has-text-align-right\" data-align=\"right\">Active detections<\/th><\/tr><\/thead><tbody><tr><td>1<\/td><td>Shopify<\/td><td class=\"has-text-align-right\" data-align=\"right\">15,777,484<\/td><\/tr><tr><td>2<\/td><td>Cart Functionality<\/td><td class=\"has-text-align-right\" data-align=\"right\">11,398,271<\/td><\/tr><tr><td>3<\/td><td>WooCommerce<\/td><td class=\"has-text-align-right\" data-align=\"right\">9,618,858<\/td><\/tr><tr><td>4<\/td><td>Magento<\/td><td class=\"has-text-align-right\" data-align=\"right\">3,658,271<\/td><\/tr><tr><td>5<\/td><td>Wix eCommerce<\/td><td class=\"has-text-align-right\" data-align=\"right\">3,391,746<\/td><\/tr><tr><td>6<\/td><td>PayPal<\/td><td class=\"has-text-align-right\" data-align=\"right\">3,251,145<\/td><\/tr><tr><td>7<\/td><td>WooCommerce 3.0<\/td><td class=\"has-text-align-right\" data-align=\"right\">2,489,012<\/td><\/tr><tr><td>8<\/td><td>1&amp;1<\/td><td class=\"has-text-align-right\" data-align=\"right\">2,442,823<\/td><\/tr><tr><td>9<\/td><td>Squarespace Commerce<\/td><td class=\"has-text-align-right\" data-align=\"right\">2,184,645<\/td><\/tr><tr><td>10<\/td><td>Afternic<\/td><td class=\"has-text-align-right\" data-align=\"right\">1,312,235<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Shopify&#8217;s 15,777,484 detections are roughly 1.6 times WooCommerce&#8217;s 9,618,858, with Magento (3,658,271), Wix eCommerce (3,391,746), and Squarespace Commerce (2,184,645) behind. WooCommerce 3.0 appears as its own entry at 2,489,012, which shows the practical value of version-level tracking: an agency that sells replatforming or upgrades can target companies showing evidence of an older release instead of every WooCommerce store.<\/p>\n\n\n\n<h3 id=\"h-marketing-automation-and-email-sizing-the-displacement-pool\" class=\"wp-block-heading\">Marketing Automation and Email: Sizing the Displacement Pool<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Rank<\/th><th class=\"has-text-align-left\" data-align=\"left\">Technology<\/th><th class=\"has-text-align-right\" data-align=\"right\">Active detections<\/th><\/tr><\/thead><tbody><tr><td>1<\/td><td>Google Ads<\/td><td class=\"has-text-align-right\" data-align=\"right\">3,122,688<\/td><\/tr><tr><td>2<\/td><td>Mailchimp<\/td><td class=\"has-text-align-right\" data-align=\"right\">2,745,738<\/td><\/tr><tr><td>3<\/td><td>MailChimp for WordPress<\/td><td class=\"has-text-align-right\" data-align=\"right\">1,190,271<\/td><\/tr><tr><td>4<\/td><td>HubSpot<\/td><td class=\"has-text-align-right\" data-align=\"right\">993,970<\/td><\/tr><tr><td>5<\/td><td>Klaviyo<\/td><td class=\"has-text-align-right\" data-align=\"right\">648,707<\/td><\/tr><tr><td>6<\/td><td>Mautic<\/td><td class=\"has-text-align-right\" data-align=\"right\">438,891<\/td><\/tr><tr><td>7<\/td><td>Tealium iQ Tag Management<\/td><td class=\"has-text-align-right\" data-align=\"right\">402,754<\/td><\/tr><tr><td>8<\/td><td>GoSquared<\/td><td class=\"has-text-align-right\" data-align=\"right\">398,919<\/td><\/tr><tr><td>9<\/td><td>MailerLite<\/td><td class=\"has-text-align-right\" data-align=\"right\">318,074<\/td><\/tr><tr><td>10<\/td><td>HighLevel<\/td><td class=\"has-text-align-right\" data-align=\"right\">269,326<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Mailchimp&#8217;s 2,745,738 detections are about 2.8 times HubSpot&#8217;s 993,970 and 4.2 times Klaviyo&#8217;s 648,707, while open-source Mautic (438,891) outpaces MailerLite (318,074). Google Ads tops the list (3,122,688) because the category also captures ad platforms. For a Klaviyo or HubSpot seller, Mailchimp&#8217;s footprint is the displacement pool. The size of that pool matters less than which companies in it show evidence of moving right now, and that is the job of switch detection.<\/p>\n\n\n\n<h3 id=\"h-more-category-leaders-at-a-glance\" class=\"wp-block-heading\">More Category Leaders at a Glance<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Category<\/th><th class=\"has-text-align-left\" data-align=\"left\">Leader<\/th><th class=\"has-text-align-left\" data-align=\"left\">Runner-up<\/th><th class=\"has-text-align-left\" data-align=\"left\">Worth noting<\/th><\/tr><\/thead><tbody><tr><td>Cloud Infrastructure<\/td><td>Amazon Web Services: 13,079,159<\/td><td>Google Cloud: 8,743,060<\/td><td>Microsoft Azure (No. 6): 1,109,130<\/td><\/tr><tr><td>Business Intelligence<\/td><td>Power BI: 167,620<\/td><td>Sensors Data: 145,557<\/td><td>Tableau (No. 3): 99,365<\/td><\/tr><tr><td>Accounting and Finance<\/td><td>Zoho (suite): 583,779<\/td><td>Odoo: 108,213<\/td><td>QuickBooks (No. 3): 60,622<\/td><\/tr><tr><td>Payment Processing<\/td><td>PayPal: 3,251,145<\/td><td>Apple Pay: 1,990,482<\/td><td>Shop Pay (No. 5): 1,514,246<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Read the gaps as well as the leaders. Microsoft Azure&#8217;s 1,109,130 detections are about one-twelfth of Amazon Web Services&#8217; 13,079,159, a reminder that detections measure public evidence, not cloud spend, and platforms used mainly for internal workloads can leave less of it. In business intelligence, Power BI (167,620) leads Tableau (99,365) by about 1.7 to 1.<\/p>\n\n\n\n<h3 id=\"h-global-coverage-shows-up-in-the-leaderboards\" class=\"wp-block-heading\">Global Coverage Shows Up in the Leaderboards<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The dataset spans 208 countries, led by the United States (13,902,664 companies), Germany (3,853,331), and the United Kingdom (3,145,553), and that reach is why regional vendors make the leaderboards. DATEV (24,002) ranks in Accounting and Finance, Baidu Analytics (1,681,194) in Analytics, and OVH (1,112,661), Aruba.it (670,148), and Locaweb (164,220) in Cloud Infrastructure. For a team selling into Germany, a DATEV filter across 3,853,331 German companies is the start of a territory plan.<\/p>\n\n\n\n<h2 id=\"h-see-it-yourself-search-and-test-the-predictleads-technology-dataset\" class=\"wp-block-heading\">See It Yourself: Search and Test the PredictLeads Technology Dataset<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The fastest way to judge a technology dataset is to search it. Use the tool below to try and test the PredictLeads Technology dataset: see which companies show evidence of using a technology, or switch to a company domain to see the technologies detected on it. Start with a vendor from one of the leaderboards above, then try one of your own target accounts.<\/p>\n\n\n\n<div class=\"predictleads-tech-search-embed\">\n  <iframe\n    src=\"https:\/\/predictleads.com\/technologies\/embed\"\n    title=\"PredictLeads Technology Search\"\n    width=\"100%\"\n    height=\"350\"\n    style=\"border: none; display: block; width: 100%;\"\n    loading=\"lazy\"\n  ><\/iframe>\n<\/div>\n\n<script>\n(function () {\n  var ALLOWED_ORIGIN = 'https:\/\/predictleads.com';\n  var MESSAGE_TYPE = 'predictleads:iframe-height';\n\n  window.addEventListener('message', function (event) {\n    if (event.origin !== ALLOWED_ORIGIN) return;\n    if (!event.data || event.data.type !== MESSAGE_TYPE) return;\n\n    var height = event.data.height;\n    if (typeof height !== 'number' || height <= 0) return;\n\n    document\n      .querySelectorAll('.predictleads-tech-search-embed iframe[src^=\"' + ALLOWED_ORIGIN + '\"]')\n      .forEach(function (iframe) {\n        if (iframe.contentWindow === event.source) {\n          iframe.style.height = height + 'px';\n        }\n      });\n  });\n})();\n<\/script>\n\n\n\n<p class=\"wp-block-paragraph\">The same two lookups are available programmatically through the API, covered later in this post.<\/p>\n\n\n\n<h2 id=\"h-technology-switch-detection-change-beats-a-snapshot\" class=\"wp-block-heading\">Technology Switch Detection: Change Beats a Snapshot<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Technology switch detection flags when a company shows evidence of moving from one technology to a direct competitor, which turns a static tech stack into a running feed of competitive displacement events. Most technographic data shows a current-state snapshot. A snapshot tells you a company uses Klaviyo today. A switch event tells you the evidence points to a move from Mailchimp, and that context changes the conversation.<\/p>\n\n\n\n<h3 id=\"h-what-a-switch-event-is\" class=\"wp-block-heading\">What a Switch Event Is<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A switch event pairs two observations at the same company: an incumbent technology that is no longer detected, and a direct competitor that is newly detected. The competitor relationship comes from the catalog's 115,412 mapped competitor pairs, which cover 24,636 technologies with at least one tracked alternative. Switch signals refresh twice a month for every company that meets the data-quality bar.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A missing detection on its own does not prove a company removed a tool, because script changes, recrawl gaps, and signature changes can all make a technology stop appearing. A switch event is stronger evidence because it pairs the gap with a competitor's arrival. A matching shift in Job Openings, such as new postings that name the incoming tool, is stronger supporting evidence still. Treat each event as a reason to look closer, not as a confirmed replacement.<\/p>\n\n\n\n<h3 id=\"h-how-switch-events-are-graded\" class=\"wp-block-heading\">How Switch Events Are Graded<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">51,140 technology switch events by importance Horizontal bar chart: high importance, 9,322 events, for example replacing a CRM, ERP, or cloud platform; medium importance, 30,855 events, such as email, analytics, or helpdesk swaps; low importance, 10,963 events, such as font loaders or cookie banners. Refreshed twice a month. 51,140 technology switch events by importance High importance Medium importance Low importance CRM, ERP, or cloud platform email, analytics, or helpdesk swaps font loaders, cookie banners 9,322 30,855 10,963 Refreshed twice a month for every company that meets the data-quality bar. Source: PredictLeads internal dataset as of October 2026.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>High importance (9,322):<\/strong> replacing a CRM, enterprise resource planning (ERP) system, or cloud platform, where meaningful spend sits behind the change.<\/li>\n\n\n\n<li><strong>Medium importance (30,855):<\/strong> swapping email, analytics, or helpdesk tools. About 3 in 5 events land here.<\/li>\n\n\n\n<li><strong>Low importance (10,963):<\/strong> peripheral tools such as a font loader or cookie banner.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Grading is what makes the feed usable at volume. Send high-importance events to account executives, batch medium-importance events into sequences, and keep low-importance events out of outbound.<\/p>\n\n\n\n<h3 id=\"h-three-ways-to-use-switch-events\" class=\"wp-block-heading\">Three Ways to Use Switch Events<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Three-step switch-event workflow Step 1, switch event: an incumbent technology is no longer detected and a direct competitor is newly detected. Step 2, corroborate: look for job postings that name the incoming tool. Step 3, prioritize: high-importance events to sales, medium to sequences, low held back. 1 2 3 Switch event Corroborate Prioritize Incumbent no longer detected, direct competitor newly detected Look for job postings that name the incoming tool High to sales, medium to sequences, low held back<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Sell into the transition.<\/strong> A company that has just moved to a new platform often needs implementation help, integrations, and adjacent tools that fit the new stack. If you sell around the incoming technology, the switch event is your timing signal.<\/li>\n\n\n\n<li><strong>Defend your customer base.<\/strong> Watch events where your own product, or one you integrate with, is the outgoing technology. Which competitor shows up in its place, and in which segments, is competitive intelligence a snapshot cannot give you.<\/li>\n\n\n\n<li><strong>Map competitive flow.<\/strong> Aggregate switch events by technology pair to see which vendors are gaining footprint from which, a market view no single detection count provides.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">For the full workflow, including the false positives to filter out, see our guide to <a href=\"https:\/\/predictleads.com\/blog\/find-companies-switching-platforms\/\">finding companies switching platforms<\/a>. For outreach plays built on the same signal, read our playbook on <a href=\"https:\/\/predictleads.com\/blog\/technographic-data-b2b-competitor-displacement\/\">competitor displacement with technographic data<\/a>.<\/p>\n\n\n\n<h2 id=\"h-how-to-get-company-tech-stack-data-via-api\" class=\"wp-block-heading\">How to Get Company Tech Stack Data via API<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Getting company tech stack data via API comes down to three lookups: what a company uses, which companies use a technology, and what a technology is. Authenticate with the <code>X-Api-Key<\/code> and <code>X-Api-Token<\/code> headers against the base URL <code>https:\/\/predictleads.com\/api\/v3<\/code>.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Question<\/th><th class=\"has-text-align-left\" data-align=\"left\">Endpoint<\/th><th class=\"has-text-align-left\" data-align=\"left\">What comes back<\/th><\/tr><\/thead><tbody><tr><td>What does this company use?<\/td><td><code>GET \/companies\/{company_id_or_domain}\/technology_detections<\/code><\/td><td>Detections with <code>first_seen_at<\/code>, <code>last_seen_at<\/code>, <code>source_count<\/code>, and <code>behind_firewall<\/code><\/td><\/tr><tr><td>Which companies use this technology?<\/td><td><code>GET \/discover\/technologies\/{technology_id_or_fuzzy_name}\/technology_detections<\/code><\/td><td>Companies with matching detections, newest first<\/td><\/tr><tr><td>What is this technology?<\/td><td><code>GET \/technologies\/{id_or_fuzzy_name}<\/code><\/td><td>Name, description, categories, parent categories, and pricing data<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<pre class=\"wp-block-code\"><code>curl --request GET \\\n  --url https:\/\/predictleads.com\/api\/v3\/companies\/example.com\/technology_detections \\\n  --header 'X-Api-Key: {your_api_key}' \\\n  --header 'X-Api-Token: {your_api_token}'<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Company-specific endpoints typically cost 1 credit per response, discovery endpoints typically cost 1 credit per record returned, and the technologies list endpoint costs 3 credits per record. Our <a href=\"https:\/\/predictleads.com\/blog\/technology-detection-api-find-companies-using-specific-technologies\/\">technology detection API guide<\/a> covers the discovery endpoint in depth. The <a href=\"https:\/\/docs.predictleads.com\/guide\/technology_detections_dataset\">Technology Detections documentation<\/a> covers the full data model. To feed detections into CRM records, see our guide to a <a href=\"https:\/\/predictleads.com\/blog\/technographic-data-api-for-b2b-enrichment\/\">technographic data API for B2B enrichment<\/a>.<\/p>\n\n\n\n<h3 id=\"h-delivery-api-flat-files-webhooks-and-mcp\" class=\"wp-block-heading\">Delivery: API, Flat Files, Webhooks, and MCP<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Pick the delivery method that matches your pipeline. The Model Context Protocol (MCP) server lets AI agents query PredictLeads data in natural language, and setup is covered in the <a href=\"https:\/\/docs.predictleads.com\/mcp_integration\/introduction\">MCP integration docs<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Method<\/th><th class=\"has-text-align-left\" data-align=\"left\">Best for<\/th><th class=\"has-text-align-left\" data-align=\"left\">How to set it up<\/th><\/tr><\/thead><tbody><tr><td>API<\/td><td>Real-time lookups and enrichment at request time<\/td><td>Self-serve with an API key and token<\/td><\/tr><tr><td>Flat files<\/td><td>Bulk delivery, warehouse loads into Snowflake or BigQuery, and backfills<\/td><td>Through the sales team; typically JSONL via AWS S3, Google Cloud Storage, or SFTP<\/td><\/tr><tr><td>Webhooks<\/td><td>Push alerts when new technology detections appear for companies you follow<\/td><td>Through the sales team<\/td><\/tr><tr><td>MCP<\/td><td>AI agents and natural-language queries such as \"Find 10 companies using HubSpot\"<\/td><td>Connect to mcp.predictleads.com with your API key and token<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 id=\"h-what-to-look-for-in-a-technology-dataset-provider\" class=\"wp-block-heading\">What to Look For in a Technology Dataset Provider<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A strong technology dataset provider shows you its live coverage, its evidence, and its change over time, not just a headline technology count. Use these six checks when you compare vendors:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Check<\/th><th class=\"has-text-align-left\" data-align=\"left\">Why it matters<\/th><th class=\"has-text-align-left\" data-align=\"left\">How PredictLeads answers it<\/th><\/tr><\/thead><tbody><tr><td>Live vs. cataloged technologies<\/td><td>A big catalog can include tools with no current evidence<\/td><td>73,189 live of 254,367 cataloged, each with at least one detection<\/td><\/tr><tr><td>Source transparency<\/td><td>You need to know why a match exists<\/td><td>Five signal types, <code>seen_on_*<\/code> flags, and <code>source_count<\/code>; Extended Technology Detections add <code>detection_source<\/code> and <code>detection_source_type<\/code><\/td><\/tr><tr><td>Timestamps<\/td><td>Tenure and recency drive timing<\/td><td><code>first_seen_at<\/code> and <code>last_seen_at<\/code> on every detection<\/td><\/tr><tr><td>Change detection<\/td><td>Snapshots miss displacement<\/td><td>51,140 graded switch events, refreshed twice a month<\/td><\/tr><tr><td>Taxonomy and competitor mapping<\/td><td>Category share and displacement lists depend on it<\/td><td>302 categories and 115,412 competitor pairs<\/td><\/tr><tr><td>Delivery<\/td><td>Your pipeline sets the format<\/td><td>API, flat files, webhooks, and MCP<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For a vendor-by-vendor comparison, see our roundup of the best <a href=\"https:\/\/predictleads.com\/blog\/technographic-data-providers\/\">technographic data providers<\/a>. For what drives detection quality, read our guide to <a href=\"https:\/\/predictleads.com\/blog\/technographic-data-accuracy\/\">technographic data accuracy<\/a>.<\/p>\n\n\n\n<h2 id=\"h-how-predictleads-delivers-technology-data\" class=\"wp-block-heading\">How PredictLeads Delivers Technology Data<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">PredictLeads is a data provider, not a platform: it supplies the technology layer that CRMs, enrichment tools, and AI agents build on. Its technology dataset covers 73,189 live technologies across 302 categories, backed by 2,003,496,359 active detections over 133,531,593 companies in 208 countries. Five signal types verify those detections, the <code>behind_firewall<\/code> field marks evidence from outside a company's website, and 51,140 graded switch events show evidence of where companies are moving between direct competitors.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Technology Detections get stronger when you stack them with other PredictLeads datasets in the same API. Job Openings (classified with O*NET codes and seniority) can corroborate a possible transition, News Events and Financing Events add timing, and Similar Companies extends a list of switchers to lookalike accounts. Companies such as Clay, Common Room, and FactSet use PredictLeads data. Everything comes from public sources only and is delivered through API, flat files, webhooks, and MCP, backed by SOC 2 Type II certification and GDPR and CCPA compliance.<\/p>\n\n\n\n<h2 id=\"h-ready-to-see-this-in-your-own-data\" class=\"wp-block-heading\">Ready to see this in your own data?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Search a technology in the tool above, then pull the same detections for your own account list.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Get 100 free API requests when you create an account - no credit card, no sales call.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/predictleads.com\/sign_up?utm_source=blog&amp;utm_medium=cta&amp;utm_campaign=technology-dataset-provider\">Create your free PredictLeads account<\/a><\/p>\n\n\n\n<h2 id=\"h-frequently-asked-questions\" class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n\n\n\n<h3 id=\"h-how-can-i-find-out-what-technology-a-company-uses-in-2026\" class=\"wp-block-heading\">How can I find out what technology a company uses in 2026?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Look up the company's domain in a technology dataset and read each detection's dates and sources, not just the list of tool names. PredictLeads returns a company's detections through <code>GET \/companies\/{company_id_or_domain}\/technology_detections<\/code>, each with <code>first_seen_at<\/code>, <code>last_seen_at<\/code>, and <code>source_count<\/code>. Detections provide evidence of which technologies a company uses or has recently used, drawn from website content, DNS records, job postings, customer reviews, and website connections. The dataset covers 73,189 live technologies across 133,531,593 companies.<\/p>\n\n\n\n<h3 id=\"h-what-should-a-technology-dataset-provider-include\" class=\"wp-block-heading\">What should a technology dataset provider include?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A technology dataset provider should include a live catalog, multi-source evidence, timestamps, a category and competitor taxonomy, and change detection. Live coverage matters more than a headline count: PredictLeads reports 73,189 live technologies out of 254,367 cataloged over time, sorted into 302 categories. Each detection carries <code>seen_on_*<\/code> flags and a <code>source_count<\/code>, and 51,140 graded switch events capture movement between direct competitors. Delivery should match your pipeline, which for PredictLeads means API, flat files, webhooks, and MCP.<\/p>\n\n\n\n<h3 id=\"h-does-a-technology-s-detection-count-equal-its-market-share\" class=\"wp-block-heading\">Does a technology's detection count equal its market share?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. A detection count measures how widely a technology shows public evidence across companies, not revenue or seat share. In PredictLeads data, Zoho (583,779) and HighLevel (269,326) both outnumber Salesforce (240,716) in CRM detections, which reflects footprint breadth rather than spend. Use detection counts to size an addressable pool, and use pricing data, attached to 10,401 live technologies, to estimate budget.<\/p>\n\n\n\n<h3 id=\"h-how-does-technology-switch-detection-work\" class=\"wp-block-heading\">How does technology switch detection work?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Technology switch detection pairs an incumbent technology that is no longer detected with a direct competitor that is newly detected at the same company. PredictLeads maps 115,412 competitor pairs across 24,636 technologies and has flagged 51,140 switch events to date, graded high (9,322), medium (30,855), and low (10,963) importance. Switch signals refresh twice a month for every company that meets the data-quality bar. Treat each event as strong evidence, and look for job postings that name the incoming tool as stronger supporting evidence before you act.<\/p>\n\n\n\n<h3 id=\"h-can-i-get-company-tech-stack-data-via-api-flat-files-or-mcp\" class=\"wp-block-heading\">Can I get company tech stack data via API, flat files, or MCP?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. PredictLeads delivers technology data through API, flat files, webhooks, and MCP. Use <code>GET \/discover\/technologies\/{technology_id_or_fuzzy_name}\/technology_detections<\/code> to find companies using a technology, flat files (typically JSONL) for warehouse loads, webhooks for new detections on companies you follow, and the MCP server at mcp.predictleads.com for AI agents. New accounts get 100 free API requests with no credit card required.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most teams buy technographic data to answer one narrow question: does this account use a given tool? A technology dataset [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[157,162,86],"tags":[34,91,121,57],"class_list":["post-2044","post","type-post","status-publish","format-standard","hentry","category-data-reports","category-statistics","category-technology-dataset","tag-predictleads","tag-tech-stacks","tag-technographic-data","tag-technology-data"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.4 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Technology Dataset Provider: 2B+ Detections | Oct 2026<\/title>\n<meta name=\"description\" content=\"Technology dataset provider insights, October 2026: what 2B+ tech stack detections reveal for GTM and data teams about category leaders and vendor switches.\" \/>\n<meta name=\"robots\" content=\"index, follow, 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