Keyword Research Scraper — Google, YouTube, Amazon & more

One seed in. Hundreds of real keywords out — from six engines at once, grouped into questions and comparisons, with a note of which engines agreed.

seed: "protein powder"     1,000 keywords in 5.6 seconds

6x  whey protein powder                [google youtube amazon bing ebay duckduckgo]  BUY
6x  protein powder chocolate           [google youtube amazon bing ebay duckduckgo]  BUY
5x  protein powder for weight loss     [google youtube amazon bing duckduckgo]       BUY

questions    267   what protein powder is best · how protein powder is made
comparisons   78   protein powder vs creatine · protein powder vs whey protein
long tail    805

No API key, no login, no browser.

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The number that matters is how many engines agreed

Every keyword tool gives you a list. This one tells you which engines suggested each phrase.

One engine offering a phrase means people type it. Two independent engines offering it means the demand is real rather than an artefact of one ranker. And when Amazon is one of them, the people typing it arrived somewhere they could buy — that is a different and more valuable fact than a search volume estimate.

Sort by sourceCount, filter on hasBuyerIntent, and the top of the list is where search demand and purchase intent overlap.

Source	What a suggestion from it tells you
Google · Bing · DuckDuckGo	what people search for
YouTube	what people want to watch — different phrasing entirely
Amazon · eBay	what people are ready to buy

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What you get

Per keyword — keyword, sourceCount, sources, hasBuyerIntent, group, questionWord, bestPosition, positionsBySource, wordCount, country, language

Per seed — keywords found, counts by question / comparison / preposition, multiSourceKeywords, buyerIntentKeywords, per-source coverage, median word count, long-tail count, and any sources that could not serve the country.

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Who this is for

• SEO and content teams — question keywords are article briefs that already have demand behind them.
• Amazon and e-commerce sellers — Amazon autocomplete is the only free window into what shoppers type into a buying box.
• YouTube creators — YouTube phrases its suggestions differently from Google. Titling from Google data leaves views on the table.
• PPC — comparisons name the competitors people weigh you against, which is where the cheap conquest terms live.

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Input

{
  "keywords": ["protein powder", "creatine"],
  "sources": ["google", "youtube", "amazon"],
  "country": "US",
  "includeQuestions": true,
  "includeComparisons": true
}

Option	Default	Notes
sources	all six	google, youtube, amazon, bing, ebay, duckduckgo
country	US	Sets the proxy exit too — see below
language	follows country	DE→de, JP→ja, BR→pt
includeQuestions	true	how, what, why, when, where, who, which, is, can, do…
includeComparisons	true	vs, versus, or, alternative
includePrepositions	true	for, with, without, near, like, best, cheap
includeAlphabet	true	a–z, the widest net
includeDigits	false	0–9; useful for model numbers, noise otherwise
maxKeywordsPerSeed	1000	Seeds that hit it are flagged truncated

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Five things worth knowing

Each was found by running against live data.

Autocomplete is personalised on your IP, and the country parameter does not override it. Asking Google for United States suggestions from a Lithuanian address returned car insurance lithuania and protein powder kaina — plausible-looking keywords for a market nobody asked about. This Actor therefore points the Apify Proxy at the country you are researching, automatically. If you pin a proxy country yourself, yours wins and the mismatch is called out in the log. Run without a proxy and your keywords describe wherever the run happened to execute.

Amazon is addressed by marketplace hostname, not by marketplace id. The id alone looks sufficient and is not: completion.amazon.com with a German id returns HTTP 200 and an empty list, which reads exactly like a term nobody searches for. completion.amazon.de with the same id returns protein pulver and esn protein pulver. Fifteen marketplaces are wired up; ask for one that Amazon does not operate in and the source is reported as unavailable rather than quietly answered with American keywords.

The order of expansion is the truncation policy. Questions, comparisons and prepositions run before the alphabet. One live seed reached its keyword cap after 187 of 336 lookups — with the alphabet leading it returned 65 questions, and with questions leading it returned 267 from the same budget. If a seed truncates you lose long-tail letters, not the phrases you came for.

An endpoint with nothing to offer hands your query straight back. Ask Google for how protein powder and one of the suggestions is how protein powder — a phrase nobody types, arriving as a billable row. Ten of 55 expansion prefixes did this on one seed. Deleting every echo would be too blunt, because protein powder alternatives is both an echo of its own prefix and a real query. So a suggestion is kept when some other prefix also surfaced it, and dropped when the only way it ever appeared was by asking for it verbatim. Dropped rows are counted as echoesDropped and never charged. (protein powder on survives this test, and should: ON is Optimum Nutrition, and the protein powder o prefix found it independently.)

Groups are read from the keyword, not from the prefix that found it. running shoes vs walking shoes arrives from the letter "v" and is still a comparison. Matching is on whole words only, so conversion rate is not filed as a vs comparison.

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Output

{
  "recordType": "keyword",
  "seed": "protein powder",
  "keyword": "whey protein powder",
  "sources": ["google", "youtube", "amazon", "bing", "ebay", "duckduckgo"],
  "sourceCount": 6,
  "hasBuyerIntent": true,
  "group": "alphabet",
  "bestPosition": 1,
  "positionsBySource": { "google": 2, "amazon": 1, "bing": 3 },
  "wordCount": 3,
  "country": "US", "language": "en"
}

Rows arrive sorted: most engines first, then best rank. Three ready-made views — Keywords, Questions & comparisons, Seed summary. Set flattenOutput: true for CSV.

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Honest limits

• No search volume, CPC or difficulty. Autocomplete says what people type, not how many. sourceCount is a demand signal, not a volume estimate, and this Actor will not invent one.
• Suggestions are live and personalised. Two runs a week apart will differ, and that is the data being current rather than the Actor being unstable.
• Ten suggestions per lookup is the engines' limit. Breadth comes from asking many prefixes, which is exactly what the expansions do.
• Amazon covers fifteen marketplaces. Elsewhere it is reported in sourcesUnavailable.
• Seeds must be short phrases. A pasted URL is rejected rather than expanded into a hundred pointless lookups.
• Seeds no source can serve are never charged, nor are query echoes, nor duplicates across sources.

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Pricing

Event	Price	When
Actor start	$0.005	Once per run
Seed researched	$0.01	Per seed, covering every lookup across every source
Keyword found	$0.0002	Per unique keyword

$0.20 per 1,000 keywords. A seed returning 1,000 keywords costs about $0.21. AnswerThePublic and Keyword Tool start at $89–99 a month.

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Tips

• Sort by sourceCount, then filter hasBuyerIntent. That is your commercial shortlist, and it takes one sort in the dataset view.
• Feed the output back in as seeds. Point sourceDatasetId at a finished run to go a second level deep on the phrases that scored highest.
• Run the same seed for several countries to size a market before translating anything. The German and US lists for one seed rarely map onto each other.
• Use YouTube-only for titles and Google-only for articles. They phrase the same intent differently, and the gap between the two lists is content nobody has written yet.
• Compare the question list against your site. Every question with no matching page is a brief.