Website Tech-Stack Detector — Frameworks, CMS, Analytics, CDN, Hosting for Any URL

Detect the full technology stack of any website in one request — JS frameworks, CMS, analytics pixels, CDN, hosting provider, payment processors, chat widgets, A/B-testing tools, marketing-automation platforms — structured per-category JSON. Wappalyzer-class detection without the browser-extension juggling.

Built for teams that need tech-stack data as a dataset: B2B sales enrichment, competitive-intel audits, partner-ecosystem mapping, or security-attack-surface audits of a vendor list.

Who uses this

• Sales teams filtering B2B lead lists — "show me all prospects using Shopify and Segment" — to pitch compatible tools.
• Competitive-intel teams auditing competitor tech choices across their target market.
• Partner / BD teams at SaaS companies finding every site using a specific integration they support.
• Security teams inventorying vendor/3rd-party scripts loaded on partner sites.
• Market researchers quantifying adoption of a technology category across a sector.

What you get per URL

Field	Type	Example
url	string	https://stripe.com
final_url	string	(after redirects)
status_code	number	200
server_header	string	cloudflare
cms	array	[{name:"WordPress", version:"6.4", confidence:1.0}]
js_frameworks	array	[{name:"React", version:"18.2", confidence:1.0}]
css_frameworks	array	[{name:"Tailwind CSS", version:"3.4"}]
analytics	array	[{name:"Google Analytics", tag:"GA4", id:"G-XXXX"}]
tag_managers	array	[{name:"Google Tag Manager", id:"GTM-XXXX"}]
cdn	array	[{name:"Cloudflare"}]
hosting	array	[{name:"Vercel"}] or [{name:"AWS EC2"}]
payment_processors	array	[{name:"Stripe"}]
ecommerce_platform	array	[{name:"Shopify"}]
marketing_automation	array	[{name:"HubSpot"}, {name:"Klaviyo"}]
chat_widgets	array	[{name:"Intercom"}]
ab_testing	array	[{name:"Optimizely"}]
cookie_consent	array	[{name:"OneTrust"}]
ssl_issuer	string	"Amazon RSA 2048 M03"
tech_total_detected	number	18
page_size_kb	number	187
third_party_scripts	array	list of external script domains
scanned_at	string	ISO timestamp

Input

{
  "urls": [
    "https://stripe.com",
    "https://shopify.com",
    "https://notion.so"
  ],
  "includeCategories": ["cms","js_frameworks","analytics","payment_processors","marketing_automation"],
  "followRedirects": true,
  "renderJs": false,
  "timeoutMs": 15000
}

• urls — list to scan. Bulk tested up to 500 URLs.
• includeCategories — subset to return. Omit for all.
• renderJs — set true for SPA-heavy sites where detection requires post-JS rendering (slower, ~3x cost).
• followRedirects — resolve through to final URL.

Python usage example

from apify_client import ApifyClient
from collections import Counter

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("knotless_cadence/website-tech-stack-detector").call(
    run_input={
        "urls": [
            "https://stripe.com",
            "https://shopify.com",
            "https://notion.so",
            # ... 500 B2B prospect URLs
        ],
        "includeCategories": ["cms","js_frameworks","analytics","payment_processors"],
    }
)
records = list(client.dataset(run["defaultDatasetId"]).iterate_items())

# Which analytics platforms dominate in this prospect list?
analytics = Counter()
for r in records:
    for a in r.get("analytics", []):
        analytics[a["name"]] += 1

print("Analytics platforms across prospects:")
for name, count in analytics.most_common():
    pct = count / len(records) * 100
    print(f"  {count:4d}  ({pct:5.1f}%)  {name}")

# Find prospects using competitor's tech
target_prospects = [
    r for r in records
    if any(cms["name"] == "Shopify" for cms in r.get("ecommerce_platform", []))
]
print(f"\n{len(target_prospects)} prospects on Shopify — pitch relevant tools to them")

B2B-sales-enrichment pattern

Typical workflow: (1) pull prospect list from CRM → (2) scan via this actor → (3) filter by tech they use → (4) personalize outbound by tech context.

Example: If you sell a Shopify-integrated email tool, filter the dataset for ecommerce_platform: Shopify AND absence of competing marketing_automation: Klaviyo.

Common questions

Q: How does this compare to Wappalyzer / BuiltWith? Wappalyzer is browser-extension (one-site-at-a-time). BuiltWith has a paid API — powerful, but $295+/month for meaningful volume. This actor uses Wappalyzer-class fingerprints at dataset scale, priced per-run via Apify compute.

Q: Does it detect things behind JS rendering? By default, no (fast static analysis). Set renderJs: true to execute JS — catches SPAs, React/Vue/Angular apps more reliably. ~3x cost, ~5x slower per URL.

Q: How current are the fingerprint rules? Fingerprint database updated weekly. New tools added regularly (AI-tool adoption is tracked aggressively — LLM chat widgets, RAG-provider scripts, AI SDKs).

Q: False-positive rate? Detection comes with confidence scores (0-1). Confidence ≥ 0.8 typically indicates reliable match. Scores below 0.5 indicate a weak pattern match (e.g. a generic CSS class name) — filter them out client-side if precision matters.

Q: Can I detect competitors' own internal tools? Sometimes — internal tools that embed public SDK snippets (Segment, Amplitude, Datadog RUM) are detectable. Fully custom in-house stacks aren't.

Q: Cost per run? Apify compute-unit pricing. A 100-URL scan (static) typically costs $0.02. With renderJs: true, $0.05-$0.10.

Export integrations

• CSV / JSON / Excel / HTML (native Apify dataset download)
• Google Sheets (via Apify integration)
• Webhooks on each run
• S3 / GCS direct sync
• Zapier / Make.com / n8n

Related scrapers

Source	Actor	Data
Tech-Stack Detector (this)	Tech fingerprints	Site tech stack
PageSpeed Insights	Core Web Vitals	Performance
Website Uptime	HTTP status, latency	Availability
SSL Certificate Checker	TLS posture	Security
IP Geolocation	IP → location	Geo context

All free on Apify Store.

About the maintainer

Maintained by an active Apify developer (78 public actors covering scraping, MCP servers, and B2B-sales enrichment tooling). Articles on Hashnode, Telegraph, Write.as, and Mataroa covering tech-stack fingerprinting, B2B enrichment pipelines, and competitive-intelligence data architecture.

Need a custom B2B-enrichment / competitive-intel pipeline (tech-stack-based prospect segmentation, competitor rollout tracking, ecosystem-partner discovery)? Email spinov001@gmail.com or message t.me/scraping_ai.

Disclaimer

Detects technologies via publicly visible page signals (HTML, HTTP headers, JS). Respects standard rate limits. Not affiliated with Wappalyzer or BuiltWith.