Google Trends Scraper: Interest, Regions, Related & Trending

Google Trends Scraper exports Google Trends data as clean JSON, CSV or Excel. There's no official Google Trends API for most people (the alpha has a waitlist), so this actor reads the same data the trends.google.com website shows:

• ✅ Interest over time for any search term: minute, hourly, daily, weekly or monthly points from 2004 to now.
• ✅ Interest by region: countries, states/provinces, US metro areas (DMA) or cities.
• ✅ Related queries, both Top and Rising (including "Breakout"). Related topics too, when Google returns them (see FAQ).
• ✅ Trending now: the searches trending in a country over the past 4 hours to 7 days, with search volume, % increase, start/end time, category and related queries.
• ✅ Compare more than 5 keywords on one scale. Google compares at most 5 terms per chart. The actor batches larger lists and repeats your first term in every batch as an anchor, then rescales all terms onto one 0–100 scale (normalizedValue).
• ✅ Filter by location, timeframe (presets or custom dates), category and search type: Web, News, Images, YouTube or Google Shopping.
• ✅ Handles Google's rate limits and flaky proxies: switches to a new proxy IP on every failed request (429, captcha, timeout, 5xx), falls back to direct requests when the proxy is down, and caps retry time.
• ✅ Pay only for results that contain data. Empty results are free.

Who uses Google Trends data?

• SEO and content teams: find rising queries and topics to write about, and check a keyword's seasonality before investing in it.
• Marketers and brand managers: compare brand vs competitors over time and by region, and pick the regions or metros where to spend ad budget.
• E-commerce and product teams: spot rising product demand early with Google Shopping and YouTube search interest.
• Investors, analysts and researchers: use search interest as an alternative-data signal alongside sales, stock or survey data.
• Newsrooms and social media teams: monitor what's trending right now in any country.
• Data scientists and AI agents: feed Trends data into models, dashboards or LLM workflows via the Apify API or MCP server.

How does the Google Trends scraper work?

1. Add search terms (Explore mode) or pick Trending now.
2. Choose location, timeframe, category, search type and which datasets you want.
3. The actor calls Google Trends' own web endpoints (explore → widget data), the same way the website does, and returns one dataset item per search term × dataset.
4. If a term has too little search volume for a dataset, you still get the item (with hasData: false, rows: []), and you're not charged for it.

How do I choose Google Trends terms, regions and timeframes?

Field	Description	Example
mode	explore (your search terms) or trendingNow	"explore"
searchTerms	Keywords. Any number; more than 5 are batched automatically, anchored on the first term	["coffee", "tea", "matcha"]
datasets	Any of interestOverTime, interestByRegion, relatedQueries, relatedTopics	["interestOverTime"]
geo	Empty = worldwide, or country US, region US-CA, metro US-NY-501	"GB"
timeframe	now 1-H, now 4-H, now 1-d, now 7-d, today 1-m, today 3-m, today 12-m, today 5-y, all, custom	"today 5-y"
startDate / endDate	Custom range, YYYY-MM-DD (or YYYY-MM-DDTHH for hourly data within 7 days)	"2024-01-01"
category	Google Trends category ID (0 = all; e.g. 7 Finance, 71 Food & Drink, 20 Sports)	71
property	web, news, images, youtube, froogle (Google Shopping)	"youtube"
regionResolution	auto, COUNTRY, REGION, DMA (US metros), CITY	"DMA"
includeLowVolumeRegions	Also return regions without enough data	false
language	Interface language for names and labels	"en-US"
trendingHours	Trending now window: 4, 24, 48 or 168 hours	"24"
maxTrendingSearches	Max trending searches to return	50
proxyConfiguration	Apify Proxy (on by default; Google rate-limits single IPs quickly)	{ "useApifyProxy": true }

Example: five years of YouTube search interest for three brands in the US, by metro area.

{
  "searchTerms": ["nike", "adidas", "puma"],
  "geo": "US",
  "timeframe": "today 5-y",
  "property": "youtube",
  "datasets": ["interestOverTime", "interestByRegion"],
  "regionResolution": "DMA"
}

What Google Trends data do you get?

Every Explore item has the same fields; rows depends on dataset.

{
  "dataset": "interestOverTime",
  "term": "coffee",
  "geo": "US",
  "timeframe": "today 12-m",
  "category": 0,
  "property": "web",
  "resolution": null,
  "comparedWith": ["tea"],
  "anchorTerm": null,
  "hasData": true,
  "rowCount": 53,
  "rows": [
    {
      "date": "2025-09-28T00:00:00.000Z",
      "formattedTime": "Sep 28 – Oct 4, 2025",
      "value": 74,
      "normalizedValue": 74,
      "hasData": true,
      "isPartial": false
    }
  ],
  "exploreUrl": "https://trends.google.com/trends/explore?q=coffee%2Ctea&date=today+12-m&geo=US&hl=en-US"
}

Row shapes per dataset:

• interestOverTime: date (ISO, UTC), formattedTime, value (0–100, relative to the highest point among the terms in the same request), normalizedValue (0–100 across all your terms; see below), hasData, isPartial (the latest, incomplete period).
• interestByRegion: geoCode (e.g. US-CA, DMA code 807), geoName, value (0–100), hasData, coordinates (cities only).
• relatedQueries: ranking (top / rising), rank, query, value, formattedValue (e.g. "100", "+250%", "Breakout"), isBreakout, link.
• relatedTopics: same as related queries, with topicId (Knowledge Graph ID like /m/02vqfm), title and topicType instead of query.

Trending now items:

{
  "dataset": "trendingNow",
  "term": "national coffee day",
  "geo": "US",
  "timeframe": "past 24 hours",
  "searchVolume": 100000,
  "increasePercent": 1000,
  "startedAt": "2026-09-28T23:00:00.000Z",
  "endedAt": null,
  "isActive": true,
  "categories": ["Food and Drink"],
  "relatedQueries": ["national coffee day deals", "free coffee today"],
  "newsArticleCount": 9,
  "exploreUrl": "https://trends.google.com/trends/explore?q=national+coffee+day&date=now+1-d&geo=US&hl=en-US"
}

searchVolume is Google's rounded lower bound (e.g. 100000 = "100K+").

Comparing more than 5 terms

Google Trends scales every chart so that its highest point is 100, and it compares at most 5 terms at a time. With 6+ terms the actor sends batches of 5 that all include your first term (the anchor). It rescales each batch by the anchor's total interest in it, so normalizedValue puts every term on one 0–100 scale. value stays exactly what Google returned for that batch.

The anchor works best when it's reasonably popular throughout the timeframe. If the anchor has zero interest in a batch, that batch's normalizedValue is null, and the run log says so. Rescaling rounds Google's integer values, so very small terms lose precision, just as they do on the website.

How much does it cost to scrape Google Trends?

Pay-per-event, only for results with data:

Event	Price
Trend result (one term × dataset, or one trending-now search)	$0.002

That's $2 per 1,000 results. One result holds a whole time series, region table or related list (up to 50 rows), not one row. The default input (2 terms × 3 datasets) costs $0.012. Results without data are free. If you set a maximum cost per run, the actor stops cleanly when it reaches it.

Tips for scraping Google Trends

• Keyword research: set datasets: ["relatedQueries"] and look at rows with ranking: "rising". isBreakout marks queries that grew more than 5000%.
• Seasonality: use timeframe: "today 5-y" for weekly data, or all for monthly data since 2004.
• Daily data for a long period: Google returns daily points for ranges up to ~9 months. Use custom startDate/endDate windows.
• Monitoring: schedule Trending now every few hours and send new items to Slack, Google Sheets or a webhook with Apify integrations.
• Topics vs search terms: Google Trends compares exact search terms here. Related topics give you Knowledge Graph IDs (topicId) if you need entity-level data.

Google Trends scraper FAQ

Is this the official Google Trends API? No. It reads the public data that the trends.google.com website loads. Values are identical to what you see in the browser for the same settings.

Why do values differ between runs? Google Trends computes values from a sample of searches, so numbers can shift slightly between requests, especially for low-volume terms and short timeframes. Values are always relative (0–100), never absolute search counts.

Do I need a proxy? Google rate-limits Trends heavily per IP (often after a dozen or so requests). Apify Proxy is enabled by default, and the actor switches IP automatically when it's throttled. Large runs take longer when Google throttles, but they keep going.

Why are some rising queries unrelated to my term? Rising queries are what Google returns: the terms whose share grew the most among searches containing your term. For broad terms and long timeframes these can be noisy.

Why are related topics empty? As of September 2026, Google Trends' endpoint returns an empty related-topics list for the requests we tested, so relatedTopics is off by default. If you enable it, empty results come back with hasData: false and are not charged. They fill in automatically if Google serves the data again.

Can I use it through the API or with AI agents? Yes. Call it through the Apify API, the Apify MCP server or any Apify integration. Pricing is per event only, so agent payments work too.

Disclaimer: This actor is not affiliated with, endorsed by or sponsored by Google. Google Trends is a trademark of Google LLC. You are responsible for complying with Google's terms when using the data.

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