Tech Stack Detector — Website Qualification

Qualify a website list with evidence you can inspect

Tech Stack Detector checks public website responses for CMS, ecommerce, analytics, frameworks and other technology signals. Agencies and researchers can turn a list of known domains into a technology shortlist, then inspect which signals were observed and which technologies were inferred.

Try a small example

Open the starter workflow or paste this input into the Actor:

{
  "startUrls": [
    "https://wordpress.org",
    "https://www.shopify.com",
    "https://example.com"
  ],
  "filterTechnologies": [
    "WordPress"
  ],
  "minConfidence": 80,
  "matchMode": "any",
  "onlyMatching": false,
  "maxConcurrency": 2
}

One row per supplied URL, including matches, non-matches and errors, with evidence details in full JSON. Start the run, inspect the result and summary, then use the export links.

Preview the output

Illustrative field example; this is not a live result or a guarantee about a named website or listing:

{
  "url": "https://example.com",
  "matchesFilter": true,
  "matchedTechnologies": [
    "WordPress"
  ],
  "technologyCount": 1,
  "technologies": [
    {
      "name": "WordPress",
      "confidence": 100,
      "detectionMethod": "observed",
      "evidence": [
        "meta:generator"
      ]
    }
  ],
  "htmlTruncated": false,
  "error": null
}

The Workflow view highlights url, finalUrl, cms, ecommerce, analytics, matchesFilter, matchedTechnologies, technologyCount, htmlTruncated, error. Use full JSON for all fields and nested arrays; CSV is suitable for the selected overview columns.

Use it in your workflow

Build a technology-qualified company segment

Provide the domains you already want to research. Set filterTechnologies to the exact technology names you need, such as ["WordPress", "WooCommerce"]. Matching ignores letter case. Use matchMode: "any" when either is useful, or "all" when every named technology must be detected.

minConfidence filters the returned technologies before the match decision. A value of 80 removes weaker pattern scores and the engine’s lower-scored inferences. This score is not a measured probability of accuracy. Review technologies[].evidence and detectionMethod in full JSON: header, metadata, HTML and script signals are observed; inferredFrom:... records a relationship inferred from another technology. Evidence describes the signal type without exposing cookie values.

Start with onlyMatching: false. After reviewing errors and coverage, enable it to save matching rows only. This Actor checks supplied websites; it does not discover every company using a technology.

Coverage and limitations

Understand detection limits

The Actor fetches HTML over HTTP and does not run browser JavaScript. It can miss tools loaded after page load, hidden backends and technologies without recognizable public signals. A non-match is not proof that a site does not use the technology.

Only the first 2,000,000 response bytes are analyzed. htmlBytes shows retained bytes and htmlTruncated identifies a larger response. A limited response can miss later markup. HTTP errors, unusable content and access challenges produce error rows when onlyMatching is off. With it on, those rows are omitted and the SUMMARY still counts failed targets.

Equivalent URLs are deduplicated within a run. HTTP and HTTPS, www and non-www, different paths and query strings remain distinct because they can return different applications. Source pages and signatures can change; treat a segment as a research aid and verify important decisions.

What does it cost?

The current event price is $0.003 per saved website row: 3 rows cost $0.009, 100 cost $0.30, and 1,000 cost $3 in Actor event fees. Saved error rows count too. With onlyMatching: true, non-matching and failed rows are omitted, although the Actor still checks those targets. SUMMARY is not an additional dataset row. Inspect the live Pricing tab before a larger run.

Repeat runs and integrations

For an agency intake workflow, run this check when new company domains arrive. Store the full JSON for review, use matchesFilter to route the shortlist, and keep source URL plus fetchedAt with the record. Recheck only at a frequency that fits your need; every run can save and charge for the same URL again. Prefer updating a record keyed by your canonical company domain rather than appending duplicates blindly.

In Make or n8n, start the Actor, wait for its terminal status, then retrieve the dataset and SUMMARY. A started run is not a completed export. Keep the Apify token in the integration’s credentials. Use its error path for failed runs and review partial summaries before publishing downstream results.

Input and export reference

The Input tab lists every accepted control, default and limit. Use the JSON example above for a small first run. The Output tab links to Workflow CSV, Workflow JSON, full JSON and Run summary. When calling the dataset API for larger results, paginate until you have retrieved the full intended dataset; a single limited response may contain only its first page.

Frequently asked questions

Is this a Wappalyzer or BuiltWith alternative?

It supports bulk HTTP-based technology checks through Apify. It does not reproduce a commercial product’s historical database, browser extension, coverage or accuracy guarantees. Use a small known-site sample to compare the fields you actually need.

What does confidence mean?

It is a score from matching technology signatures, not a calibrated probability. A high score can still be wrong. An inferred technology is explicitly labeled.

Why are all-filter matches empty?

Every requested technology must survive minConfidence and be detected when matchMode is all. Check spelling, the threshold, failed targets and HTML truncation; do not treat an empty export as proof of absence.

Related tools

For a narrower table, see Shopify Store Detector or WordPress Site Detector. Their fields and filters differ; each has its own price.

Report a reproducible issue

Use this Actor’s Issues tab with a small public input, run ID, expected result and the relevant error or summary. Avoid posting credentials or private data. Source changes can require updates; no response-time or accuracy guarantee is implied.