Find Companies Using Magento: Technographic List

Magento powers a particular kind of online store: bigger than a typical Shopify shop, custom enough to need developers, and complex enough to feel every bit of database load, security patching, and hosting cost. That profile is exactly why a list of companies using Magento is such a clean prospecting target. If you sell ecommerce hosting, performance tooling, security and PCI services, payment integrations, replatforming consulting, or extensions, “runs Magento” is the first qualifying question—and you can answer it from the outside, at scale, without a single sales call.

This guide covers how Magento is actually detected (it leaves real fingerprints, unlike a server-side database), how Adobe Commerce fits into the picture, and how to turn a raw domain list into a Magento-confirmed prospect list with the DetectZeStack API: single checks, batch scans, and side-by-side comparison included.

Why Build a List of Companies Using Magento

Magento technographics serve a wider set of buyers than most ecommerce filters, because a Magento store is a self-hosted, developer-maintained application rather than a hosted SaaS storefront:

• Ecommerce hosting and infrastructure vendors. Magento is heavy—PHP, a MySQL-compatible database, often Redis and Elasticsearch behind it. Managed Magento hosting, CDN, and caching products all start from “is this a Magento store?”
• Performance and reliability tooling. Catalog pages, checkout, and search under real load are where Magento stores feel pain in revenue. A confirmed detection puts a store on your list before its next Black Friday.
• Security, PCI, and patching services. Magento detections come with a CPE identifier (cpe:2.3:a:magento:magento), which plugs straight into vulnerability workflows. Magento has a long history of widely-exploited CVEs, so “runs Magento” is also a security-sales signal. Our CPE guide for security teams covers that pipeline end to end.
• Replatforming and agency services. Teams selling migrations off legacy Magento 1, or onto Adobe Commerce, need to find the Magento estate before they can pitch modernizing it.
• Extension and integration vendors. If your product is a Magento module or an integration, the stores already running the platform are your entire addressable market.

The workflow below is the same one we documented for finding companies using Stripe and finding companies using MySQL—only the filter changes. What makes the Magento version satisfying is that, unlike a database, Magento is detected directly.

How Magento Is Detected on a Website

A server-side database like MySQL never speaks HTTP, so it can only be inferred. Magento is the opposite: it is an application that renders pages, sets cookies, and serves static assets, and every one of those touchpoints leaves a fingerprint a scanner can match directly. That makes Magento positives both high-precision and independent—you are not relying on an implication chain.

HTTP, DNS, and TLS Fingerprints That Reveal Magento

DetectZeStack runs a full detection pass—HTTP response and HTML, DNS records, and the TLS certificate—in a single request. For Magento, the decisive evidence sits in the HTTP layer:

Because the match is direct, Magento is reported under the Ecommerce category at full confidence whenever these signals are present—no inference required. Two technologies do come along by implication, though: Magento is written in PHP and requires a MySQL-compatible database, so PHP and MySQL are added to the result automatically. That is useful context, but the Magento detection itself stands on its own evidence.

Magento vs Adobe Commerce vs Other Ecommerce Platforms

A common point of confusion: Adobe Commerce and Magento are the same platform for detection purposes. Adobe Commerce is the commercial, enterprise edition Adobe sells; Magento Open Source is the free edition. They share a code base and therefore the same external fingerprints, so a scan reports both as Magento. A detection tells you a store runs the Magento platform—not which edition or license the company bought. If edition matters for your segmentation, treat the rest of the detected stack (enterprise-grade CDN, search, and personalization tools tend to cluster around Adobe Commerce deployments) as your tiebreaker.

Magento also sits in a different bracket than the platforms it competes with, and the contrast is what makes it a sharp filter:

• Magento — self-hosted, PHP, developer-maintained, mid-market to enterprise. The store owns its infrastructure, which means it can buy and install what you sell.
• Shopify — fully hosted SaaS. Detected the same way (by its own fingerprints), but a Shopify merchant cannot change hosting, database, or server-side tooling. We cover that filter in how to detect Shopify.
• WooCommerce — a WordPress plugin, so it implies WordPress, PHP, and MySQL. Lighter-weight stores, usually SMB. The Shopify vs WooCommerce detection guide breaks down those signals.

Filtering for Magento specifically—rather than “any ecommerce”—is how you isolate the self-hosted, infrastructure-owning buyer from the hosted-SaaS crowd that cannot act on most B2B offers.

Build a Magento Technographic List with the DetectZeStack API

The API resolves all of the above—HTTP fingerprints, DNS, TLS, and the PHP/MySQL implications—in one call and returns structured JSON. You can try it right now against the public demo endpoint, no API key required (it is IP-rate-limited, so use it for spot checks, not bulk scans):

Spot-Check a Single Domain with /demo and /analyze

$ curl -s "https://detectzestack.com/demo?url=magento.com" \
  | jq '.technologies[] | select(.name == "Magento")'
{
  "name": "Magento",
  "categories": ["Ecommerce", "CMS"],
  "confidence": 100,
  "description": "Magento is an open-source ecommerce platform written in PHP.",
  "website": "https://magento.com",
  "icon": "Magento.svg",
  "source": "http",
  "version": "",
  "cpe": "cpe:2.3:a:magento:magento:*:*:*:*:*:*:*:*"
}

That is a real response shape. Magento appears under the Ecommerce category (it carries a CMS tag too) at confidence 100, matched at the HTTP layer (source: "http"). The cpe field is ready for vulnerability tooling. The version field is empty here because the store did not expose a version string in its assets—when one is present, it lands in that field, which is handy for splitting Magento 1 from Magento 2 stores.

The top-level categories map is a shortcut—you can check for the platform and read its PHP/MySQL implications without iterating the technologies array:

$ curl -s "https://detectzestack.com/demo?url=magento.com" \
  | jq '{ecommerce: .categories["Ecommerce"], implied: [.categories["Databases"], .categories["Programming languages"]]}'
{
  "ecommerce": ["Magento"],
  "implied": [["MySQL"], ["PHP"]]
}

With an API key, /analyze is the authenticated equivalent—same response shape, your own rate limits, and results cached for repeat lookups:

$ curl -s "https://detectzestack.p.rapidapi.com/analyze?url=example-store.com" \
  -H "X-RapidAPI-Key: YOUR_KEY" \
  -H "X-RapidAPI-Host: detectzestack.p.rapidapi.com" \
  | jq '{domain, magento: (.categories["Ecommerce"] // [] | contains(["Magento"])), tech_count: .meta.tech_count, cached, response_ms}'
{
  "domain": "example-store.com",
  "magento": true,
  "tech_count": 21,
  "cached": false,
  "response_ms": 1973
}

When all you need is the boolean, the /check endpoint is the cheapest call—pass the domain and tech=magento (the parameter is case-insensitive):

$ curl -s "https://detectzestack.p.rapidapi.com/check?url=magento.com&tech=magento" \
  -H "X-RapidAPI-Key: YOUR_KEY" \
  -H "X-RapidAPI-Host: detectzestack.p.rapidapi.com"
{
  "domain": "magento.com",
  "technology": "Magento",
  "detected": true,
  "confidence": 100,
  "version": "",
  "categories": ["Ecommerce", "CMS"],
  "response_ms": 1144,
  "cached": false
}

Scale to a Full List with POST /analyze/batch

For list building, POST /analyze/batch accepts up to 10 URLs per request and analyzes them concurrently. Here is a complete pipeline using bash, curl, and jq that reads domains.txt (one domain per line) and writes every Magento-confirmed domain to magento_leads.csv, annotated with its total tech count and CDN for qualification:

#!/usr/bin/env bash
# find-magento.sh — filter a domain list down to Magento-confirmed leads
KEY="YOUR_KEY"
HOST="detectzestack.p.rapidapi.com"

echo "domain,tech_count,cdn" > magento_leads.csv

# Process domains.txt in batches of 10 (the /analyze/batch maximum)
xargs -n 10 < domains.txt | while read -r batch; do
  urls=$(printf '%s\n' $batch | jq -R . | jq -s '{urls: .}')
  curl -s -X POST "https://$HOST/analyze/batch" \
    -H "X-RapidAPI-Key: $KEY" \
    -H "X-RapidAPI-Host: $HOST" \
    -H "Content-Type: application/json" \
    -d "$urls" |
  jq -r '.results[]
    | select(.result != null)
    | select(.result.categories["Ecommerce"] // [] | contains(["Magento"]))
    | [.result.domain,
       (.result.meta.tech_count | tostring),
       ((.result.categories["CDN"] // ["none"])[0])]
    | @csv' >> magento_leads.csv
done

wc -l magento_leads.csv

A 1,000-domain list becomes 100 batch calls. Domains that fail to resolve or time out appear in results[] with an error field instead of a result, and the select(.result != null) guard skips them cleanly. One filter worth adding for data quality: check meta.scan_depth. A value of "partial" means the HTTP fetch failed and only DNS and TLS layers ran—and since the Magento signal lives entirely in the HTTP layer, a partial scan can never confirm Magento. Those domains belong in a retry queue, not your rejects file. For throughput, retries, and a production Python version of this scanner, see how to batch scan 1,000 websites.

Rank and Compare Prospects with POST /compare

When you are deciding which of two Magento accounts to work first, POST /compare analyzes 2–10 domains in one call and computes the shared and unique technologies across them:

$ curl -s -X POST "https://detectzestack.p.rapidapi.com/compare" \
  -H "X-RapidAPI-Key: YOUR_KEY" \
  -H "X-RapidAPI-Host: detectzestack.p.rapidapi.com" \
  -H "Content-Type: application/json" \
  -d '{"urls": ["store-a.com", "store-b.com"]}' \
  | jq '{shared, unique_a: .domains[0].unique, unique_b: .domains[1].unique}'
{
  "shared": ["Magento", "PHP", "MySQL", "Cloudflare"],
  "unique_a": ["Elasticsearch", "New Relic", "PayPal"],
  "unique_b": ["Google Analytics"]
}

Both stores run Magento, but store A has a heavier, more invested stack—dedicated search (Elasticsearch), APM (New Relic), and a payment integration. That is a larger, better-resourced operation and the stronger first call for most B2B offers. The full response also carries each domain’s complete technologies array, so one compare call doubles as two enrichment calls. Feeding these signals into a scoring model is covered in our lead enrichment pipeline guide, and the broader pattern of reading an ecommerce stack end to end is in ecommerce tech stack analysis.

Turning the List Into Qualified Sales Leads

The CSV above is a lead list; the surrounding stack is what makes it routable. A bare Magento detection and one wrapped in enterprise infrastructure describe very different companies:

• Magento + heavy stack (Elasticsearch, Redis, APM, enterprise CDN, multiple payment processors) — a serious, revenue-critical store, very likely Adobe Commerce. Prime for performance, reliability, and security offers.
• Magento + thin stack (shared hosting, no CDN, no APM) — often a smaller or legacy store, possibly on unsupported Magento 1. A different pitch: managed hosting, replatforming, or security remediation.
• Magento + a stale version string — when the version field surfaces an old release, that is a direct opening for patching and migration services, and a flag worth confirming against its CPE in a vulnerability workflow.

One discipline carries over from database detection: keep positives and negatives in separate piles. A Magento positive is strong evidence—the fingerprints are direct. But a missing detection is not proof the store avoids Magento; it can mean the scan was blocked at the HTTP layer, the storefront sits behind an aggressive bot wall, or only a partial scan completed. Score those as “unknown,” not “disqualified,” and send them to a retry queue.

Get Your API Key

Every external technographic tool detects platforms by matching fingerprints—the differences that matter for a Magento pipeline are freshness, structure, and price. Directory-style tools answer from a crawl that may be months old, so a store that replatformed off Magento last quarter still shows as a lead; DetectZeStack scans the domain at request time, and the cached field in every response tells you whether you got a fresh scan or a brief cache hit. The output is built for scripts—a boolean from /check, a filterable categories map from /analyze, and a CPE on every detection that has one. And if all you need is “run my own domain list through a detector,” API pricing is an order of magnitude cheaper than enterprise export platforms; we did the math in the technographic data pricing breakdown.

Conclusion

Finding companies using Magento is one of the cleaner technographic plays available, because Magento is detected directly—its session cookies, static asset paths, and RequireJS module loader are unambiguous HTTP-layer fingerprints, and PHP plus MySQL come along by implication. Adobe Commerce and Magento Open Source look identical from the outside, so a scan tells you the platform, not the edition; the surrounding stack is your tiebreaker for sizing and edition. The pipeline itself is three calls: /demo or /check to validate the approach on stores you know, /analyze/batch to turn a raw domain list into a qualified Magento lead list, and /compare to rank the prospects you are about to work. The free tier’s 100 requests per month are enough to validate the pipeline on a sample of your list before scaling up.