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To find a site’s tech stack in bulk, choose between a hosted lookup service, a local Python fingerprinting workflow, or a hybrid. Hosted services handle much of the crawling and fingerprint maintenance; Python gives you control over fetching and processing. Neither can reveal every component: detections are inferences from observable signals such as headers, cookies, HTML, and scripts, not a complete inventory of hidden server-side infrastructure.
Choose a bulk lookup approach
The right method depends on the size of your list, how current the results need to be, and how much operational work you want to own. There is no cited head-to-head accuracy benchmark, so compare documented workflow features rather than assuming one detector is more accurate.
| Approach | Volume and throughput | Cost and freshness | Control and output |
|---|---|---|---|
| Wappalyzer hosted lookup | Bulk web upload accepts CSV or TXT lists of up to 100,000 URLs. The API accepts up to 10 URLs per request and documents a limit of 10 requests per second. | Ordinary API lookups use 1 credit per URL; live recursive scans use 5 credits per URL. Cached results are described as verified within the last 30 days. | Bulk upload exports CSV or JSON. The API returns JSON. The service manages fingerprint data and scanning. |
| BuiltWith API and bulk jobs | High-throughput lookup accepts up to 64 root domains or subdomains. Larger bulk jobs can return a job ID for background processing. | The cited API documentation describes endpoints and limits, but does not establish current pricing or a no-subscription pay-per-use option. | Documented output formats include XML, JSON, CSV, and XLSX. API keys should be kept secret. |
| Local Python fingerprinting | Volume depends on the fetching, concurrency, and retry behavior you implement. | You control requests and processing; you also own fingerprint-data maintenance and operating costs. Freshness depends on what you fetch and when. | You control persistence and output shape. A third-party Python project can analyze fetched responses or fetch URLs itself. |
| Hybrid workflow | Run a local first pass over the list, then route selected sites to a hosted scan. | Can reserve higher-credit live scans for ambiguous, important, or JavaScript-heavy sites; this is a workflow design choice, not a measured savings claim. | Combines local control with hosted crawling, but requires consistent recordkeeping across both stages. |
Use Wappalyzer from Python or upload a list
Understand the two bulk workflows
Wappalyzer offers a file-upload workflow separately from its API. Its technology lookup page accepts a CSV or TXT file containing up to 100,000 URLs and lets users export results as CSV or JSON. The page describes cached results as verified within the last 30 days and says live-only lookups count as five lookups each. It recommends cached results when speed and completeness are preferred.
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The API documentation describes a REST endpoint at GET https://api.wappalyzer.com/v2/lookup/. Send the API key in the x-api-key header. The documented maximum is 10 URLs per request, with a rate limit of 10 requests per second. Do not confuse that per-request limit with the separate upload workflow’s 100,000-URL capacity.
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Choose cached or live scanning deliberately
An ordinary API lookup costs 1 credit per URL. A live recursive lookup, requested with live=true and recursive=true, costs 5 credits per URL and may run asynchronously. Wappalyzer says a crawl can take up to 15 minutes; a callback or a later repeat request can be used to receive results. For an immediate, shallower scan, recursive=false analyzes one page in the request and is described as less complete.
The credit meter does not mean the API is available as a no-subscription pay-as-you-go service. Wappalyzer’s current public pricing page says API access requires a plan. When accessed in 2026, it listed the following USD monthly prices and API credits; confirm current terms before budgeting:
| Plan | Monthly price | API credits |
|---|---|---|
| Pro | $250/month | 5,000 |
| Business | $450/month | 20,000 |
| Enterprise | $850+/month | 200,000+ |
The same pricing page listed 50 monthly technology lookups in the free account, but that figure is not the same as API access eligibility. Check the current plan details for the workflow you intend to use.
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For an API-driven run, keep the input list and result records separate from the request loop so a failed batch does not erase completed work. A practical pipeline should:
- Normalize each entry into a URL, while retaining the original input for audit and troubleshooting.
- Send batches of no more than 10 URLs, and pace requests to remain within the documented 10-requests-per-second limit.
- Persist each response as it arrives, including the requested URL, final URL if returned, timestamp, scan mode, and raw response.
- Retry transient HTTP failures with bounded backoff; write permanent failures to a separate error record rather than dropping the entire job.
- For recursive scans, persist callback or job state and make result processing idempotent so a repeated notification does not duplicate data.
These are implementation recommendations, not a claim that a particular script was tested. Protect the API key in server-side secret storage; do not publish it in source code.
Consider BuiltWith for multi-domain jobs
BuiltWith documents a Domain API that supports XML, JSON, CSV, and XLSX, with examples for multiple domains. Its high-throughput lookup accepts up to 64 root domains or subdomains per lookup, with exclusions for text, metadata, attributes, contacts, and live lookup of results absent from its database.
For larger jobs, the documented bulk Domain Jobs API can return small batches synchronously and provide a job ID for larger batches processed in the background. This establishes that a bulk workflow exists, but the cited documentation does not establish current pricing or whether one-off usage without a plan is available. Check current vendor terms before comparing costs with a credit-based plan.
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A local implementation lets you choose which pages to request, how to limit concurrency, what to retry, and how to store findings. It also makes you responsible for those operational decisions and for the fingerprint data itself.
The Wappalyzer repository describes a cross-platform technology-identification utility and categories including content management systems, web frameworks, ecommerce platforms, JavaScript libraries, and analytics. A separate third-party project, wappalyzerpy, describes a pure-Python package that can analyze fetched responses or fetch URLs itself. Its listed signals include headers, cookies, HTML, metadata, and script references; it also documents an optional browser mode for JavaScript-heavy sites. It is not an official Wappalyzer SDK. Before adopting it, check its current Python requirement, fingerprint source, release activity, and license.
Whatever you run locally, set request timeouts and concurrency limits, record fetch failures, and honor applicable site access rules. A detector can only analyze signals it can reach; a failed request or a script-rendered page can leave gaps.
Use a hybrid workflow for selective depth
A useful design is to run local fingerprinting across the whole list, then submit ambiguous, important, or JavaScript-heavy sites to a hosted live scan. This keeps the first pass under your control and directs deeper scans to cases where they may add value. It is a practical workflow recommendation, not a benchmarked claim about cost or accuracy.
Keep stage and evidence in the output: record whether each finding came from a local fetch, a cached lookup, or a live recursive scan, along with the timestamp and any matched signal details the tool exposes. That distinction helps downstream users understand what was observed and how current it may be.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret detections as indicators, not proof
A technology detector infers products from exposed response and page signals. A match can indicate that a technology appears in the page or assets examined, but it does not prove that the site uses it everywhere or that it is part of the current backend. Hidden services may leave no detectable public signal, and stale or partial pages can produce incomplete results.
Wappalyzer says its dataset is continuously updated and that it aims to re-verify identified technologies on every website at least once a month. It also says company details are refreshed quarterly. These are Wappalyzer’s statements about its dataset, not independent validation of coverage or accuracy; see its API FAQ. Preserve scan dates and distinguish observed detections from your own inferences.
What to compare before processing a large list
- Volume: Check the batch limit, rate limit, and whether larger jobs become asynchronous.
- Cost model: Determine whether usage is billed per URL, credit, subscription, or negotiated volume, and whether live scans use more credits.
- Freshness: Identify whether the result is cached, live, or mixed, and what verification interval the provider describes.
- Scan depth: Separate a one-page lookup from a recursive crawl or a local analysis of pages and assets you choose.
- Operations: Decide who will own retries, timeouts, parallelism, persistence, and failure reporting.
- Integration: Match the available JSON, CSV, XML, or XLSX output to the next stage of your pipeline.
- Evidence: Check whether you can retain timestamps and inspect the matched signals, rather than storing only a technology name.
No cited source supplies an independent comparative precision or recall benchmark for these options, so do not treat product limits or dataset claims as proof that one detects more accurately than another.
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