Website Technology Stack Detector

Website Technology Stack Detector

Website Technology Stack Detector identifies the full technology stack powering any website — content management systems, JavaScript frameworks, analytics platforms, CDN providers, hosting services, payment processors, and more — directly from HTML and HTTP response analysis. Covers 30+ technology signatures across 10 categories with configurable batch scanning and optional deep audit mode.

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What does it do?

Every website is built on a combination of technologies — a CMS to manage content, a JavaScript framework for interactivity, an analytics platform to track visitors, a CDN for performance, and a hosting provider to serve it all. The Website Technology Stack Detector extracts this invisible infrastructure by analysing both HTML content patterns (script tags, meta elements, CSS classes, DOM structures) and HTTP response headers (server headers, cookies, X-Powered-By, set-cookie patterns).

The result is a structured, machine-readable report of every technology detected on the target URL, grouped by category and tagged with a confidence level so you know how reliable each detection is. Whether you need to analyse a single site or audit up to 20 URLs in one batch run, the actor returns clean JSON ready for downstream integration, dashboards, or enrichment pipelines.

The actor supports three modes:

• Single Analysis — one URL, one result.
• Batch Mode — up to 20 URLs in a single run, each independently analysed.
• Full Audit — extended HTML parsing for deeper detection on complex or heavily customised sites.

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Features

#	Feature	Description
1	Multi-Vector Detection	Analyses both HTML content (script tags, meta elements, CSS classes) and HTTP response headers (server headers, cookies, X-Powered-By)
2	30+ Technology Signatures	Pre-built detection patterns covering the most widely used web technologies
3	10 Category Groups	Technologies are automatically organised into CMS, JavaScript Frameworks, Analytics, CDN/Security, Hosting, E-commerce, Payment Processors, CSS Frameworks, and more
4	Confidence Scoring	Each detection is tagged high, medium, or low based on the number and specificity of signature matches
5	Batch Processing	Analyse up to 20 URLs in a single actor run with independent per-URL results
6	Full Audit Mode	Enable deeper HTML parsing for extended detection coverage on complex or obfuscated sites
7	Fast Execution	Typical single-URL analysis completes in seconds; batch runs scale linearly
8	No False Positives	Reports only signatures that positively match — no speculative or probabilistic detections
9	CI/CD Ready	REST API and ApifyClient SDK enable integration into automated pipelines
10	MCP Compatible	Can be called from any MCP-compatible AI agent (Claude, Cursor, Windsurf, etc.)

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Why use this?

Pain Point	How This Actor Solves It
Manually inspecting "View Page Source" for every competitor	Automated detection returns a structured report in seconds
No visibility into what tech your prospects use	Batch scan 20 URLs at once to qualify leads by technology fit
Security teams lack an accurate technology inventory	Full Audit mode uncovers hidden components for vulnerability mapping
Sales outreach relies on stale or incomplete tech data	Fresh analysis on demand — no stale spreadsheets
Integration pipelines need per-URL technology metadata	Clean JSON output with categories, confidence, and counts
AI agents need context about a website's stack before acting	MCP integration lets agents query the actor directly

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Who is it for?

Persona	What They Use It For
Security Engineer	Mapping technology inventory across owned domains to identify outdated or vulnerable components
Sales Development Rep (SDR)	Qualifying prospects by determining whether their stack includes target technologies (e.g., Shopify stores, Magento sites)
Market Researcher	Analysing technology adoption trends across industries for reports and strategy
Product Manager	Understanding competitor technology choices to inform product roadmap and positioning
Web Developer / Architect	Quick reconnaissance on a site's frontend and backend stack before planning integrations or migrations
AI / Automation Engineer	Feeding website technology data into downstream agents via MCP or API for context-aware workflows
Recruiter / Talent Sourcer	Identifying companies using specific technologies to target candidates and skills demand

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Input Parameters

Field	Type	Default	Description
url	string	—	Website URL to analyse (required for single mode, optional in batch mode)
fullAudit	boolean	false	Enable extended detection with deeper HTML parsing for maximum accuracy
batchMode	boolean	false	Enable batch URL analysis (set to true and provide batchData)
batchData	array	[]	List of URL strings or objects {"url": "..."} for batch analysis (max 20)

Example Input JSON

Single analysis:

{
  "url": "https://example.com"
}

Single analysis with full audit:

{
  "url": "https://example.com",
  "fullAudit": true
}

Batch mode (20 URLs):

{
  "batchMode": true,
  "batchData": [
    "https://example.com",
    "https://example.org",
    {"url": "https://example.net"},
    "https://example.edu"
  ],
  "fullAudit": false
}

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Output Format

Field	Type	Description
url	string	The analysed website URL
technologies	array	List of detected technology entries
technologies[].name	string	Technology name (e.g., "Next.js", "Cloudflare")
technologies[].category	string	Technology category (e.g., "JavaScript Framework", "CDN")
technologies[].confidence	string	Detection confidence: high, medium, or low
tech_count	integer	Total number of technologies detected
categories	object	Category-to-count mapping (e.g., {"CMS": 1, "Hosting": 2})
error	string	Error message if analysis failed (absent on success)

Example Output JSON

{
  "url": "https://www.shopify.com",
  "technologies": [
    {
      "name": "Shopify",
      "category": "E-commerce",
      "confidence": "high"
    },
    {
      "name": "React",
      "category": "JavaScript Framework",
      "confidence": "high"
    },
    {
      "name": "jQuery",
      "category": "JavaScript Framework",
      "confidence": "medium"
    },
    {
      "name": "Cloudflare",
      "category": "CDN/Security",
      "confidence": "high"
    },
    {
      "name": "Google Analytics",
      "category": "Analytics",
      "confidence": "high"
    },
    {
      "name": "Stripe",
      "category": "Payment Processor",
      "confidence": "medium"
    }
  ],
  "tech_count": 6,
  "categories": {
    "E-commerce": 1,
    "JavaScript Framework": 2,
    "CDN/Security": 1,
    "Analytics": 1,
    "Payment Processor": 1
  }
}

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Supported Technologies

Category	Technologies Detected
CMS	WordPress, Drupal, Joomla, Wix, Squarespace
E-commerce	Shopify, Magento, WooCommerce, BigCommerce
JavaScript Frameworks	React, Next.js, Vue.js, Angular, Svelte, jQuery, Nuxt.js, Gatsby
CSS Frameworks	Tailwind CSS, Bootstrap, Foundation, Bulma
Analytics	Google Analytics, Google Tag Manager, Hotjar, Cloudflare Analytics, HubSpot, Segment, Mixpanel
CDN / Security	Cloudflare, Akamai, Fastly, CloudFront, Imperva
Cloud / Hosting	AWS, Netlify, Vercel, Heroku, DigitalOcean, Azure
Payment Processors	Stripe, PayPal, Square, Braintree
Miscellaneous	Font Awesome, reCAPTCHA, Typekit/Adobe Fonts, Mapbox, YouTube Embedded

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API Usage

cURL

# Single analysis
curl -X POST "https://api.apify.com/v2/acts/perryay~website-tech-stack-detector/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com"}'

# Batch analysis (20 URLs)
curl -X POST "https://api.apify.com/v2/acts/perryay~website-tech-stack-detector/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"batchMode": true, "batchData": ["https://example.com", "https://example.org"]}'

# Full audit
curl -X POST "https://api.apify.com/v2/acts/perryay~website-tech-stack-detector/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com", "fullAudit": true}'

Python (ApifyClient)

from apify_client import ApifyClient

# Initialize the client with your API token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Prepare the actor input
run_input = {
    "url": "https://example.com",
    "fullAudit": False,
    "batchMode": False,
    "batchData": [],
}

# Run the actor and wait for results
run = client.actor("perryay/website-tech-stack-detector").call(run_input=run_input)

# Fetch and print the results
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"URL: {item['url']}")
    print(f"Technologies detected: {item['tech_count']}")
    for tech in item['technologies']:
        print(f"  - {tech['name']} ({tech['category']}) [{tech['confidence']}]")
    print(f"Categories: {item['categories']}")
    print("---")

Node.js (ApifyClient)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const input = {
  url: 'https://example.com',
  fullAudit: false,
  batchMode: false,
  batchData: [],
};

const run = await client.actor('perryay/website-tech-stack-detector').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

items.forEach(item => {
  console.log(`URL: ${item.url}`);
  console.log(`Technologies detected: ${item.tech_count}`);
  item.technologies.forEach(tech => {
    console.log(`  - ${tech.name} (${tech.category}) [${tech.confidence}]`);
  });
});

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Use Cases

1. Competitive Intelligence

Analyse competitor websites to understand their technology choices. Identify which CMS they use, whether they've adopted a particular JavaScript framework, or which CDN powers their performance strategy. Share insights across product, marketing, and engineering teams.

2. Sales Prospecting & Lead Qualification

Sales teams targeting Shopify merchants, WordPress sites, or React-based web apps can batch-scan prospect URLs and segment leads by technology fit. Focus outreach on high-intent accounts already using complementary tools.

3. Security & Vulnerability Assessment

Security teams can maintain an accurate technology inventory across owned domains. Detect outdated or deprecated technologies, identify unexpected CDN or hosting changes, and prioritise patching based on the actual software in use.

4. Market Research & Trend Analysis

Scan a representative sample of websites in a vertical (e.g., top 100 e-commerce sites) and aggregate the results to measure technology adoption rates, framework migration trends, and CDN market share.

5. Merger & Acquisition (M&A) Due Diligence

During M&A tech due diligence, quickly profile the target company's web technology stack to identify integration complexity, technical debt, and dependency risks before the deal closes.

6. AI Agent Context Enrichment

Equip AI agents with real-time knowledge of a website's technology stack before they generate code, write documentation, or make architectural recommendations. The MCP integration makes this seamless.

7. Recruitment & Talent Sourcing

Identify companies using specific technologies (e.g., "Next.js + Tailwind + Vercel") to target relevant job openings and candidates with matching skills.

8. SEO & Performance Auditing

Understanding the underlying framework and CDN helps contextualise page speed scores, Core Web Vitals, and SEO recommendations. Correlate technology choices with performance metrics.

9. API & Integration Planning

Before building an integration with a third-party site or service, determine which APIs and technologies they use to plan compatibility and authentication requirements.

10. Continuous Monitoring

Run the actor on a schedule (daily/weekly via Apify Cron or CI pipeline) to track technology stack changes on important domains — detect migrations away from a platform, new CDN deployments, or analytics additions.

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FAQ

Q: How does the detection work? A: The actor combines two analysis vectors: (1) HTML content analysis — scanning script tags, meta elements, link tags, CSS classes, and DOM patterns for known signatures; and (2) HTTP response header analysis — inspecting server headers, X-Powered-By, Set-Cookie patterns, and other response metadata for technology fingerprints.

Q: How accurate is the detection? A: Each detection is tagged with a confidence level (high, medium, or low). High confidence means multiple distinct signatures matched the same technology. Medium confidence means a single strong signature matched. Low confidence indicates partial or heuristic matches. We do not report speculative results — every detection has at least one positive signature.

Q: How many URLs can I scan in batch mode? A: Batch mode supports up to 20 URLs per run. Each URL is analysed independently with its own result row. If you need to scan more than 20 URLs, split them across multiple runs.

Q: What happens when a website uses custom-built technology? A: Custom-built or highly bespoke sites will have fewer detections. The actor reports only positive signature matches and will never fabricate a detection. You'll still get results for any standard technologies (hosting, CDN, analytics) that are present.

Q: Does this actor produce false positives? A: The actor is designed to avoid false positives by requiring positive signature matches. It does not use probabilistic or heuristic models that could guess incorrectly. If a technology is not positively detected, it is simply not included in the output.

Q: What categories of technology can you detect? A: Ten categories are currently covered: CMS, E-commerce, JavaScript Frameworks, CSS Frameworks, Analytics, CDN/Security, Cloud/Hosting, Payment Processors, and Miscellaneous (icons, fonts, CAPTCHA, maps, embedded media). New categories and signatures are added regularly.

Q: Can I integrate this into my CI/CD pipeline? A: Yes. The actor has a full REST API and supports the ApifyClient SDK for Python and JavaScript. You can trigger analysis from any CI/CD system (GitHub Actions, GitLab CI, Jenkins) using cURL or the SDK. See the API Usage section above for examples.

Q: Is there rate limiting? A: Apify platform platform-level rate limits apply. Within a single actor run, URLs are processed sequentially. For high-throughput batch requirements, use multiple parallel actor runs within your Apify account's concurrency limits.

Q: Does the actor support MCP (Model Context Protocol)? A: Yes. The actor is fully compatible with the Apify MCP Server. See the MCP Integration section below for the configuration block.

Q: Is there a batch processing limit? A: Batch mode supports up to 20 URLs per run. Each URL is analysed independently with its own result row. If you need to scan more than 20 URLs, split them across multiple runs.

Q: Can I detect technologies on a page that requires authentication? A: The actor performs public-facing analysis only — it does not handle login sessions or authenticated pages. For behind-login analysis, you would need to provide pre-authenticated cookies or use a browser-based actor.

Q: How often are the detection signatures updated? A: The signature database is updated regularly as new technologies emerge and existing platforms update their fingerprints. The actor always uses the latest signatures available at the time of the run — no manual update step required.

Q: What should I do if a technology I need is not in the supported list? A: The actor's technology signatures are continuously expanded. If you have a specific technology you need detected, please contact the actor maintainer with the website URL and the technology name for consideration in a future update.

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Usage & Billing

This actor uses Apify's PAY_PER_EVENT pricing model. You are charged only for successful runs based on the events your usage triggers.

Charge Events

Event	Trigger
apify-actor-start	Every run (base)
tech-audit	fullAudit: true — detailed technology audit
cve-crossref	fullAudit: true — CVE cross-reference (included in audit)

Platform costs (Apify's infrastructure fee) are passed through to the customer.

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MCP Integration

This actor can be used through the Apify MCP server. Once connected, your MCP client (Claude Desktop, Cursor, etc.) can discover and run this actor from the Apify Store.

Quick Start

1. Install the Apify connector in your MCP client:

   • Claude Desktop: Search for "Apify" in the connector directory, or use the remote server at https://mcp.apify.com
   • Other clients: See the Apify MCP server docs for setup instructions

2. Ask your AI assistant to detect technology stacks. For example:

   "Analyse the technology stack of https://example.com and summarise what CMS, framework, and CDN it uses."

Claude Desktop Configuration

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com"
    }
  }
}

On first connection, your browser opens to sign in to Apify and authorize access.

Bearer token alternative: For headless environments, CI/CD pipelines, or clients without browser-based OAuth, you can authenticate directly with your Apify API token:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Get your API token from Apify Console → API & Integrations section.

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Related Tools

• SSL Certificate Checker — Validate SSL/TLS certificate chains, expiry dates, and issuer details for domains detected in your tech stack.
• Domain Intel — WHOIS lookups, DNS enumeration, and SSL certificate validation — complete domain reconnaissance to complement stack detection.
• URL Health Checker — Monitor URL availability, HTTP status codes, and SSL health across all URLs in your infrastructure.
• CVE Vulnerability Lookup — Cross-reference detected technology versions against known CVEs for proactive vulnerability management.
• Link Quality Analyzer — Deep link health and quality scoring for SEO and content auditing across detected web properties.