Google Trends Scraper — Interest, Regions & Queries

What is Google Trends Scraper?

Google Trends Scraper returns Google Trends data as clean dataset rows: interest over time, interest by region, related queries, related topics and today's trending searches for a country. Compare up to 5 keywords in one run, for any country or worldwide, across web, news, image, YouTube or shopping search. Content strategists, SEO teams, market researchers, analysts and AI agents use it to put trend data into spreadsheets, dashboards and models without copying charts by hand.

Why use it instead of the Google Trends website

• All five data types in one run, instead of downloading one chart at a time.
• Up to 5 keywords on one scale, so comparisons stay valid.
• Spreadsheet-ready rows with a source_url back to the exact Trends view.
• Repeatable. Schedule the same input to build your own trend history, with no API key.

Five kinds of Trends data in one Actor

dataTypes value	One row is	Good for
interest_over_time	One week (or day or hour) of interest for one keyword	Seasonality and momentum
interest_by_region	One state, region or country for one keyword	Where to target content or ads
related_queries	One related search, top or rising	New content angles
related_topics	One related topic, top or rising	Adjacent themes
trending_searches	One search trending today in a country, with its news headlines	Newsjacking and daily briefs

Related queries and topics come back when Google publishes them for the keyword, market and time window; broad, popular keywords over longer windows return them most often.

How to read Google Trends numbers

Google Trends does not show search counts. Every value is relative, from 0 to 100, where 100 is the peak interest for that request. When you compare several keywords in one run, all of them share the same scale, so ChatGPT at 80 and Claude at 20 means four times the interest in the same place and period. Values from different runs are on different scales, so compare keywords inside one run. For absolute monthly search volumes, pair it with Keyword Search Volume Finder (linked below).

How to use it

Compare keywords over the last year in the US:

{
  "keywords": ["ChatGPT", "Gemini", "Copilot"],
  "geo": "US",
  "timeframe": "today 12-m",
  "dataTypes": ["interest_over_time", "related_queries"]
}

Find where a keyword is most popular:

{
  "keywords": ["pickleball"],
  "geo": "US",
  "timeframe": "today 3-m",
  "dataTypes": ["interest_by_region"]
}

Get today's trending searches in the UK:

{
  "dataTypes": ["trending_searches"],
  "trendingSearchesGeo": "GB"
}

Input	What it controls
keywords	1–5 terms to compare; leave empty when you only want trending searches
geo	Two-letter country code such as US, GB or DE, or empty for worldwide
timeframe	now 1-H, now 4-H, now 1-d, now 7-d, today 1-m, today 3-m, today 12-m (default), today 5-y or all (since 2004)
customTimeRange	An exact range, such as 2024-01-01 2025-01-01; overrides timeframe
property	web (default), news, images, youtube or froogle (Google Shopping)
category	Google Trends category ID; 0 means all categories
dataTypes	Any mix of the five types above
trendingSearchesGeo	Country for trending searches; defaults to geo
maxResults	Cap on total rows across all types, up to 50,000 (default 500)

Output: Google Trends data you receive

Every row has a type field, the keyword, the market and time window, and a source_url that opens the same view on Google Trends. These rows are copied from real runs.

Interest over time for ChatGPT in the US:

{
  "type": "interest_over_time",
  "keyword": "ChatGPT",
  "geo": "US",
  "timeframe": "today 12-m",
  "date": "Nov 2 – 8, 2025",
  "period": "Nov 2, 2025",
  "value": 100,
  "is_partial": false,
  "scraped_at": "2026-09-29T21:32:12.168Z",
  "source_url": "https://trends.google.com/trends/explore?q=ChatGPT&geo=US&date=today%2012-m"
}

Interest by region, from a comparison of ChatGPT and Claude:

{
  "type": "interest_by_region",
  "keyword": "ChatGPT",
  "geo_code": "US-CA",
  "geo_name": "California",
  "parent_geo": "US",
  "timeframe": "today 3-m",
  "value": 74,
  "scraped_at": "2026-09-29T21:51:51.667Z",
  "source_url": "https://trends.google.com/trends/explore?q=ChatGPT%2CClaude&geo=US&date=today%203-m"
}

A trending search in the US, with the headlines behind it:

{
  "type": "trending_search",
  "title": "sox",
  "traffic": "1000+",
  "geo": "US",
  "date": "Tue, 29 Sep 2026 14:20:00 -0700",
  "related_queries": [
    "Red Sox finalize AL Wild Card Series roster",
    "How to watch Red Sox vs. Yankees: Game 1 TV channel and streaming options for September 29",
    "Yankees-Red Sox Preview + David Stearns Press Conference Takeaways | The Show Ep. 220"
  ],
  "articles": [],
  "scraped_at": "2026-09-29T21:51:44.870Z",
  "source_url": "https://trends.google.com/trends/trending?geo=US"
}

Related query rows carry related_query, formatted_value and is_rising; related topic rows carry topic_title, topic_type and topic_mid. is_partial: true marks the current, still-filling period.

Example workflow: planning a quarter of content

A content lead at a software company compares ChatGPT, Gemini and Copilot for the US over 12 months. They chart the interest_over_time rows to see which assistant is gaining, pull interest_by_region to find the states with the strongest interest, and read the rising related_queries for new article angles. Then they schedule the same input monthly and export each dataset to a sheet, so the trend line grows over time.

Outcome states for API and agent users

Every run writes OUTPUT and RUN_SUMMARY to the default key-value store. OUTPUT.message lists the row count for each requested type.

Outcome	Meaning
COMPLETE	Every requested type returned rows and all were saved.
PARTIAL	Rows were saved, and at least one type returned none or maxResults was reached.
VALID_EMPTY	The request was valid and Google returned no data for it.
INVALID_INPUT	The input was rejected; OUTPUT.message explains what to change.
UPSTREAM_FAILED	Google Trends was unavailable for every requested type.

Pricing

This Actor uses Pay per event plus Apify platform usage. You pay one event per saved row, plus a start event and the platform usage of the run. The live Pricing tab on this page shows the current prices.

• 3 keywords × 52 weeks of interest over time = 156 row events + platform usage.
• One keyword by US state = about 50 row events + platform usage.
• Today's trending searches for one country = one event per trending search returned.
• maxResults stops the run at your cap, so it doubles as a cost ceiling.

Google Trends with AI agents (MCP)

Connect this Actor to an AI agent through Apify MCP and ask in plain language:

Compare Google Trends interest for ChatGPT, Gemini and Copilot in the US over the last 12 months. Tell me which is growing fastest, the top 5 states for each, and any rising related queries, and include the source URLs.

Agent checklist:

1. Put up to 5 terms in keywords; set geo, timeframe and the dataTypes the question needs.
2. Read rows with get-dataset-items, group by type, and compare value only within one run.
3. Read OUTPUT.outcome and OUTPUT.message to see which types returned rows.
4. Cite source_url so a person can open the same Trends view.

API example

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~google-trends-scraper/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["ChatGPT", "Gemini"], "geo": "US", "timeframe": "today 3-m", "dataTypes": ["interest_over_time"]}'

Related tools from the same developer

Your next step	Use	Why
When you want absolute monthly search volumes and CPC	Keyword Search Volume Finder	Real search counts to size a trend
When you want to read the news behind a trending search	Google News Scraper	Articles and sources for a topic
When you want to monitor trends on social platforms	TikTok Trending Hashtags Scraper	Ranked TikTok hashtags by country
When you want to discover what Reddit is talking about	Reddit Trends Scraper	Trending posts from subreddit feeds
When you then want to check a trend on YouTube	YouTube Search Scraper	Videos and channels for a keyword

Best results

Starting situation	Helpful action	Expected outcome
Comparing brands or products	Put all terms in one run	One shared 0–100 scale
Looking for new angles	Use today 12-m or longer with popular terms	More related queries and topics
Local targeting	Set geo and request interest_by_region	State or regional ranking
Daily news monitoring	Schedule trending_searches each morning	A running log of what people search
Precise periods	Use customTimeRange	Exactly the dates you need

Builder's note

I found that most people copying Trends by hand want to compare terms, so the Actor keeps every keyword of a comparison in one request and one shared scale. I learned that each row needs its own source_url, because Trends numbers are relative and people want to open the exact view a value came from.

FAQ

Can I compare more than 5 keywords?

Google Trends compares at most 5 terms at once. For more, split them into runs and include one shared anchor keyword in each, so you can rescale the results against it.

Why do values differ between two runs?

Each request is scaled to its own peak of 100. Compare keywords inside one run, or keep an anchor keyword across runs.

Can I get Google Trends data for YouTube or Google Shopping?

Yes. Set property to youtube or froogle.

How far back can I go?

Set timeframe to all for data since 2004, or use customTimeRange for an exact span.

Does it return search volume numbers?

Trends shows relative interest only. Use Keyword Search Volume Finder for monthly search counts.

Responsible use

Google Trends data is aggregated and anonymous. Use it in line with Google's terms. Read Apify's guide on whether web scraping is legal for more context. Google and Google Trends are trademarks of Google LLC. This independent Actor is not affiliated with, associated with, or endorsed by Google. For questions or bugs, open a ticket in the Issues tab.