Disclosure: I publish the Apify actor used below (pennydata/website-tech-stack-detector), and it was built with AI assistance (Claude). This is a how-to for one specific tool; it is pay-per-event, so check the price on the actor page before a big run. It is not affiliated with Wappalyzer or BuiltWith.

Say you have a spreadsheet of 500 company websites and want to know which ones run Shopify, WordPress or Next.js, which sit behind Cloudflare, and which load Google Tag Manager. Opening each in a browser with a devtools extension does not scale. This actor does it with one plain HTTP request per site, no browser.

1. Input

urls is the only required field. Bare domains work too.

{ "urls": [ { "url": "https://getbootstrap.com" }, { "url": "example.com" } ], "respectRobotsTxt": true, "maxConcurrency": 5, "timeoutSecs": 20 }

maxConcurrency is 1-20 and timeoutSecs is 5-60. With respectRobotsTxt on (the default), URLs disallowed for all bots are skipped and not charged.

2. Run it from Python 

from apify_client import ApifyClient client = ApifyClient("<YOUR_APIFY_TOKEN>") domains = ["getbootstrap.com", "nextjs.org", "wordpress.org"] # your list, as long as you like run = client.actor("pennydata/website-tech-stack-detector").call( run_input={ "urls": [{"url": d} for d in domains], "respectRobotsTxt": True, "maxConcurrency": 10, } ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["input"], item.get("httpStatus"), item.get("technologyNames"))

3. What comes back

One item per URL. This is a real item from a test run on getbootstrap.com:

{ "input": "https://getbootstrap.com", "finalUrl": "https://getbootstrap.com", "httpStatus": 200, "technologyNames": ["Cloudflare", "Fastly", "GitHub (hosting)", "Astro", "Bootstrap"], "technologies": [ { "name": "Cloudflare", "category": "CDN", "evidence": "header cf-ray" }, { "name": "Astro", "category": "JS framework", "evidence": "meta generator \"Astro v7.3.2\"" }, { "name": "Bootstrap", "category": "UI framework", "evidence": "html bootstrap css" } ], "capturedAt": "2026-10-16T02:00:53.501Z" }

(I trimmed technologies to three of its five entries.) The useful part is evidence: every detection says which header, meta tag, script URL, HTML marker or cookie name triggered it, so you can spot-check a result instead of trusting it blindly. Cookie names are read, cookie values never are.

4. Filter to the sites you care about 

items = list(client.dataset(run["defaultDatasetId"]).iterate_items()) shopify = [i["finalUrl"] for i in items if "Shopify" in (i.get("technologyNames") or [])] by_category = {} for i in items: for t in i.get("technologies") or []: by_category.setdefault(t["category"], set()).add(t["name"]) print(len(shopify), "Shopify sites") print({k: sorted(v) for k, v in by_category.items()})

Typical uses: qualifying leads by platform (agencies selling Shopify or WordPress work), spotting migration targets, or a quick competitor survey.

Limits worth knowing

  • One HTTP request to the URL you give, no JavaScript execution. Things that only appear after client-side rendering, or on other pages, can be missed. An empty list does not prove nothing is used.
  • Signature-based, about 60 common technologies, no version numbers. A CDN header may only reflect a proxy in front of the site. Treat results as strong hints, not an audit.
  • Only the first 1 MB of each page is inspected. Sites behind bot walls or logins may return a block page; there is no proxy rotation or captcha solving.
  • Only public http(s) URLs. You are responsible for having the right to analyze what you submit.
  • Failed URLs are not billed, and you can set a maximum cost per run in Apify's run options.

Actor page: https://apify.com/pennydata/website-tech-stack-detector

Written with AI (Claude).