Google Trends Scraper

Scrape Google Trends for as many keywords as you like and get every number on one comparable scale. Google only compares five keywords at a time and rescales each comparison, so numbers from different comparisons can't be put side by side. This Actor links comparisons through shared keywords, so 500 keywords come back as if Google had compared them all at once. It also returns daily data over any range (even 2004 to today), interest by region, related and rising queries, and Trending Now with search volumes. No login, no API key, no five-keyword limit.

It is a practical Google Trends API alternative: Google's official Trends API is still an invite-only alpha, and the popular open-source libraries break on rate limits.

Sample output (real run: 10 drinks, US, past 12 months, one shared scale)

Keyword	Average interest	Peak	Peak week
coffee	76.5	100	2026-04-12
tea	36.6	45	2026-04-05
espresso	9.6	14.8	2026-04-12
matcha	6.2	8.6	2026-04-12
energy drink	3.7	7.9	2026-04-12
chai	3.0	4.2	2026-08-23
cold brew	2.0	3.4	2026-06-21
kombucha	1.1	1.6	2026-06-21

Google Trends only compares 5 terms at a time and rescales every request to 0-100. This Actor chains overlapping batches so every keyword sits on the same scale (validated within 0-1.7% of Google's own numbers), and the full time series comes with each row.

Why this one

	This Actor	pytrends / DIY	Typical Store alternative
Keywords compared on one scale	Hundreds	5	5
Daily data for multi-year ranges	Yes, stitched and calibrated	Weekly or monthly only	Rarely
Rate limits (429)	Handled with session rotation	Breaks	Some fail 1 run in 4
Price	$3 / 1,000 keyword series	Free, your time	$2 to $25 / 1,000, often plus start fees

What it costs: 100 keywords with their time series = about $0.50.

What this Actor does

• Returns interest over time for any number of keywords or topics, for one or many locations.
• Normalizes across batches. Keywords beyond five are chained through shared "pivot" keywords, chosen so no keyword is lost to Google's rounding. Every value is on one scale where 100 is the single highest point in the run.
• Builds daily history over long ranges. Google switches to weekly data after about nine months and to monthly after five years. This Actor fetches daily windows and calibrates each one against the full-range series, so levels stay consistent across years.
• Adds interest by region at country, state, metro (US) or city level, and related queries, both top and rising, including Breakouts.
• Scrapes Trending Now: everything on trends.google.com/trending, with approximate search volume, growth, start and end time, category and related searches.
• Supports web, image, news, YouTube and Google Shopping search, category filters, topic IDs such as /m/07bsy, and hourly ranges (past hour to past 7 days).
• Rotates to a fresh IP and session the moment Google throttles, and always finishes before the run timeout with everything collected so far.

How accurate is the normalization?

We checked it against Google itself. Eighteen keywords were normalized together. Then pairs that had landed in different Google requests were compared directly on Google Trends. The ratio between the two keywords matched within:

Pair	Google, compared directly	This Actor	Difference
chatgpt / yoga	5.27	5.27	0.0%
netflix / pilates	7.82	7.81	0.1%
iphone / netflix	2.52	2.52	0.1%
coffee / sourdough	9.23	9.26	0.4%
airbnb / padel	21.67	21.76	0.4%
spotify / ozempic	5.64	5.66	0.4%
bitcoin / kombucha	14.70	14.52	1.2%
tesla / notion	11.66	11.86	1.7%

For daily stitching, the change in level across every window seam matched Google's own daily data within 0 to 3%. (Tested 25 September 2026, US, past 12 months and past 5 years.)

The usual shortcut is one fixed "anchor" keyword in every request. It loses small keywords to rounding: in simulation it was off by up to 74%, or returned zero, for keywords a few hundred times smaller than the anchor. This Actor stayed under 1% on the same data.

What data can you extract from Google Trends?

Group	Fields
Keyword	keyword, keywordTitle (the name of a topic ID), isTopic, geo, timeframe, startDate, endDate, resolution, searchProperty, category
Summary	average, peak, peakDate, latest, changePct (last quarter of the range vs the first), comparedWith
Time series	timeline: date and value per point, isPartial for the still-filling last point
Regions	interestByRegion: geoName, geoCode, value (0-100)
Related	relatedQueries.top and relatedQueries.rising (value, formattedValue, isBreakout)
Scale	scale.method, scale.stitchedDaily, scale.precision (high, medium or low), exploreUrl
Trending Now	rank, query, geo, searchVolume, increasePct, startedAt, endedAt, isActive, relatedSearches, categories, newsArticleCount

How to scrape Google Trends

1. Choose Interest over time or Trending now.
2. Add your keywords, one per line. There's no limit of five.
3. Add locations such as US, GB, US-CA or US-NY-501, or leave empty for worldwide.
4. Pick a time range. Turn on Daily data for long ranges if you want one point per day over years.
5. Optionally add interest by region and related queries, then run and export to JSON, CSV or Excel.

Example input

{
  "keywords": ["coffee", "tea", "matcha", "kombucha", "yerba mate", "cold brew", "chai"],
  "geos": ["US", "GB"],
  "timeframe": "today 5-y",
  "dailyResolution": true,
  "includeRelatedQueries": true
}

Example output (one keyword, trimmed)

{
  "type": "interestOverTime",
  "keyword": "coffee",
  "geo": "US",
  "timeframe": "today 12-m",
  "startDate": "2025-09-25",
  "endDate": "2026-09-25",
  "resolution": "WEEK",
  "average": 76.23,
  "peak": 100.0,
  "peakDate": "2026-04-12",
  "latest": 71.0,
  "changePct": 13.4,
  "comparedWith": ["tea", "matcha"],
  "scale": {"method": "single-request", "stitchedDaily": false, "precision": "high"},
  "timeline": [{"date": "2025-09-21", "value": 65.0}, {"date": "2025-09-28", "value": 70.0}],
  "relatedQueries": {
    "top": [{"query": "coffee near me", "value": 100}],
    "rising": [{"query": "how to remove coffee stain from carpet", "value": 5050, "isBreakout": true, "formattedValue": "Breakout"}]
  },
  "exploreUrl": "https://trends.google.com/trends/explore?q=coffee&date=today+12-m&geo=US"
}

How much does it cost to scrape Google Trends?

Pay per result. You only pay for what you turn on.

Event	What it is
Keyword series	One keyword in one location: summary and time series
Normalized keyword	Added when a run has more than five keywords and they are put on one scale
Daily history, per year	Added per keyword per year of stitched daily data
Region breakdown	Interest by region for one keyword
Related queries	Top and rising queries for one keyword
Trending search	One trending search in Trending Now mode

Current prices are shown on the Actor's pricing tab. A typical run of 100 keywords over the past 12 months takes under a minute.

Tips

• Compare like with like for the finest detail. Google rounds every value to a whole number, so a keyword more than about 100 times smaller than the biggest in the same run carries less detail. The scale.precision field flags it.
• Topics vs search terms. A topic ID (from the Google Trends URL, for example /m/0dr90d for Tesla the company) counts every language and spelling. A plain keyword counts that exact search.
• The last point is often partial. It's marked isPartial: true and left out of latest and changePct.
• Locations: country codes (DE), regions (US-TX, GB-SCT) and US metro areas (US-CA-807).

FAQ

Is there an official Google Trends API? Google announced one in July 2025. It is still an application-only alpha, returns only consistently scaled interest over time, and has no regions, related queries or Trending Now.

Why are my numbers different from the Google Trends website? Values are relative to the highest point among all keywords in the run, not just the five on screen. Ratios between keywords are the same as on Google; the absolute 0 to 100 level moves with the set you compare.

Can I track keywords every day? Yes. Schedule the Actor and store runs, or pick the past 7 days for hourly data.

Is scraping Google Trends legal? Google Trends shows aggregated, anonymized search interest. It contains no personal data. As with any data source, check that your use fits your own legal requirements.

Support

Found a bug or need a field? Open an issue on the Actor's Issues tab and it will be looked at quickly.

Related Actors

• Google Trends Bulk Keyword Comparison: the same engine, focused on comparing up to 500 keywords on one scale.
• Google Trends Trending Now Scraper: what people are searching right now, with search volume.

Use it as an API

Run it from your own code and get the results back in one call. Replace YOUR_TOKEN with your Apify API token.

curl -X POST "https://api.apify.com/v2/acts/headply~google-trends-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["coffee", "tea", "matcha"], "geos": ["US"], "timeframe": "today 12-m"}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")
run_input = {'keywords': ['coffee', 'tea', 'matcha'], 'geos': ['US'], 'timeframe': 'today 12-m'}
run = client.actor("headply/google-trends-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

It also works from Make, Zapier, n8n, Google Sheets and as a tool for AI agents through the Apify MCP server.

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