Google Trends Scraper — Interest, Regions & Related

Export Google Trends data for any search term or pasted Google Trends URL: interest over time, interest by region, related queries and related topics (top and rising), for any country, time range, category and Google property (Web, Images, News, Shopping, YouTube). You get flat rows ready for CSV, Excel, Google Sheets or your own code.

Why this Actor

• Compare up to 5 terms per query, all 4 panels in one charge. One query returns the timeline, the regional breakdown and the top/rising related queries and topics for up to 5 compared terms. It costs $0.015 on the Free plan and $0.006 on Gold and above.
• Priced per query, not per row. When terms are compared in groups of 5, each term costs $0.003 (Free) down to $0.0012 (Gold+). 1,000 terms in groups of 5 cost $3.00 on Free and $1.20 on Gold+, however many rows come back. A 12-month weekly timeline alone is about 53 rows per term.
• No proxies, no start fee, no monthly rental. No proxy setup and no blocked requests on your side. Apify platform usage is billed separately by Apify (at the default 256 MB a small run typically uses about $0.001–$0.003).
• Paste Google Trends URLs. Copy the address of any Google Trends explore page and the Actor reads its terms (q), country (geo), period (date), category (cat), property (gprop) and language (hl).
• 34 output fields, including a stable row id for de-duplication, breakout flags, partial-bucket flags, period averages and "leading term" per region. Queries that return no data cost only a reduced "query checked" fee (see Pricing).

Compared with alternatives

Typical run: 100 search terms, all 4 panels (terms compared in groups of 5 here = 20 queries).

Alternative on Apify Store	Listed price per result (row)	100 terms on Free	100 terms on Gold	Platform usage extra?
Most-used Google Trends Actor (~14,100 users)	$0.003 Free, $0.0003 Gold, $0.00015 Platinum, $0.0001 Diamond	at least $0.30 + platform usage	at least $0.03 + platform usage ($0.015 Platinum, $0.01 Diamond)	Yes
Fast Google Trends Actor with "Trending now" (~2,300 users)	$0.002 Free / $0.0005 Gold + $0.02 per run	at least $0.22	at least $0.07	No
Flat-price Google Trends Actor (~1,130 users)	$0.025 on all plans + $0.025 per run	at least $2.53	at least $2.53	No
Interest, regions & queries Actor (~360 users)	$0.005 on all plans + $0.00005 per run	at least $0.50 + platform usage	at least $0.50 + platform usage	Yes
This Actor	$0.015 Free / $0.006 Gold+ per query (up to 5 terms, all 4 panels, any number of rows)	$0.30	$0.12 (Platinum and Diamond pay the Gold price)	Yes (small: ~$0.001–$0.003 per run)

Prices and user counts from public Apify Store listings, checked September 2026. "At least" assumes only one result per term. The other Actors charge per result (row), and one term with all 4 panels usually returns far more than one row: a 12-month weekly timeline alone is about 53 rows per term, plus regions and related lists. With just 10 rows per term, the four alternatives would cost at least $3.00 + platform usage, $2.02, $25.03 and $5.00 + platform usage on Free.

Where we are not cheaper. The most-used Actor is cheaper whenever a term returns only a few rows: on Gold below 4 rows per term ($0.0003 × 4 = $0.0012, our Gold price per term in groups of 5), on Platinum below 8 rows and on Diamond below 12 rows. That happens if you only need, say, a short regional list or a few related queries; with a full timeline it does not. On Free, one row per term costs the same as our per-term price. The fast alternative can also be cheaper for very small runs and offers "Trending now", which this Actor does not. If you turn off Compare terms against each other, each term is its own query and this Actor costs 5× more ($1.50 on Free, $0.60 on Gold for 100 terms). This Actor's price never depends on the number of rows.

Not included

• "Trending now" / daily trending searches. This Actor covers the Google Trends Explore view (interest over time, by region, related queries and topics).
• Absolute search volumes. Google Trends only publishes relative 0–100 values. For monthly search volumes, CPC and keyword ideas use Keyword Research Tool.
• Google search results and rankings. Use Google SERP Scraper.
• YouTube video search results. The youtube property gives YouTube search interest; for the videos themselves use YouTube Search Scraper.
• Topic IDs as input (e.g. /m/02vqfm). Type the topic name as a search term instead.

What data you get

Each row has a recordType that tells you which Google Trends panel it comes from.

Data	One row per	Key fields
Interest over time	date bucket × search term	date, dateTo, value (0–100), periodAverage, isPartial
Interest by region	region × search term	regionCode, regionName, value (0–100), isLeadingTerm, regionRank
Related queries	query in the top / rising list	listType, rank, query, value, isBreakout
Related topics	topic in the top / rising list	listType, rank, topicId, topicTitle, topicType, value, isBreakout

All output fields:

Field	Type	Example
id	string	c8eff4ce4b9fa3fa (stable across runs for the same data point)
recordType	string	interestOverTime, interestByRegion, relatedQuery, relatedTopic
searchTerm	string	coffee
comparedTerms	string	coffee, tea
country	string | null	US (or Worldwide)
location	string	United States
language	string	en
searchType	string	web, images, news, shopping, youtube
categoryId	integer	0 (all categories)
timeRange	string	past12Months or custom
periodStart / periodEnd	string | null	2024-01-01 / 2024-12-31 (custom dates only)
queryIndex	integer	1
sourceUrl	string | null	Google Trends URL you pasted, null for search terms
scrapedAt	string	2026-09-28T09:00:47.505Z
dataUpdatedAt	string	2026-09-26T15:24:02.000Z
date / dateTo	string	2025-09-21 / 2025-09-27
timestamp	string	2025-09-21T00:00:00.000Z
value	number | null	100 (0–100; percent increase in rising lists)
periodAverage	number | null	92
isPartial	boolean	false
regionCode / regionName	string	US-MS / Mississippi
isLeadingTerm	boolean | null	true
regionRank	integer	2
listType	string	top or rising
rank	integer	1
query	string	matcha tea latte
topicId / topicTitle / topicType	string	/g/11x / Brown sugar oat latte / Drink
valueMeaning	string	relativeInterest or percentIncrease
isBreakout	boolean	true when growth is above 5000%

How to use

In Apify Console

1. Open the Actor and go to the Input tab.
2. Enter your Search terms (e.g. coffee, tea), or paste one or more Google Trends URLs copied from your browser. If you only want the URLs, clear the prefilled terms first.
3. Pick a Country (e.g. US, or leave empty for worldwide) and a Time range. These apply to search terms; URLs keep their own settings.
4. Optional: open the Advanced sections to change grouping, panels, Google property, category, language, a state/city, custom dates or processing speed.
5. Optional: set Maximum cost per run in the run options to cap your spend.
6. Click Start. When the run finishes, open the Output tab and export as JSON, CSV, Excel, XML or HTML, or pick one of the table views (All rows, Interest over time, Interest by region, Related queries, Related topics).

Input JSON

{
  "searchTerms": ["coffee", "tea", "matcha"],
  "trendsUrls": ["https://trends.google.com/trends/explore?date=today%205-y&geo=GB&q=oat%20milk,almond%20milk"],
  "country": "US",
  "timeRange": "past12Months"
}

This run makes 2 queries: coffee, tea, matcha compared in the US over the past 12 months, and oat milk, almond milk in the UK over the past 5 years (settings taken from the URL).

API: curl

curl -X POST "https://api.apify.com/v2/acts/cheapapi~google-trends-scraper-pro/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["coffee", "tea"], "country": "US", "timeRange": "past12Months"}'

API: JavaScript (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_TOKEN>' });
const run = await client.actor('cheapapi/google-trends-scraper-pro').call({
    searchTerms: ['coffee', 'tea'],
    country: 'US',
    timeRange: 'past12Months',
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.filter((r) => r.recordType === 'interestOverTime'));

API: Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("<YOUR_TOKEN>")
run = client.actor("cheapapi/google-trends-scraper-pro").call(run_input={
    "trendsUrls": ["https://trends.google.com/trends/explore?geo=US&q=coffee,tea"],
})
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    if row["recordType"] == "relatedQuery":
        print(row["searchTerm"], row["listType"], row["rank"], row["query"], row["value"])

Use cases

• Content and SEO planning: find rising related queries and "Breakout" topics before they peak.
• Product and market research: compare brands, products or features across countries and Google Shopping.
• Seasonality analysis: pull 5-year or since-2004 timelines to plan campaigns, stock and budgets.
• Regional targeting: see which states or countries show the most interest to focus ads and sales.
• Dashboards and monitoring: schedule weekly runs and feed Google Sheets, BI tools or a data warehouse.
• Research and journalism: reproduce a Google Trends chart you found by pasting its URL, and get the numbers behind it.

Advanced options

Option	Input field	Default	What it does
Google Trends URLs	trendsUrls	empty	Explore URLs to fetch. Each URL = 1 query with its own q (up to 5 terms), geo, date, cat, gprop and hl. Missing parameters use Google's defaults (worldwide, past 12 months, web, all categories).
Compare terms against each other	compareTerms	true	On: search terms are compared in groups of up to 5 (1 query per group). Off: each term is its own query on its own 0–100 scale.
Interest over time	includeInterestOverTime	true	Timeline of relative interest (0–100).
Interest by region	includeInterestByRegion	true	Interest per state/province (country searches) or per country (worldwide).
Related queries	includeRelatedQueries	true	Top and rising related search queries.
Related topics	includeRelatedTopics	true	Top and rising related topics (brands, people, places…).
Max related items per list	maxRelatedItems	25 (1–100)	Keep the top N items of each top/rising list.
Google property	searchType	web	web, images, news, shopping or youtube. All 4 panels are available for web. News searches return interest over time only (see Limitations).
Category ID	categoryId	0 (all)	Google Trends category, e.g. 71 Food & Drink, 7 Finance, 18 Shopping. See Category IDs below. With a category and no terms you get the whole category's trend.
Language	language	en	ISO code for topic and category names. It does not change the numbers.
State / region / city (Google location ID)	geoTargetId	empty	Numeric Google location ID, e.g. 21167 = New York state. See Locations below. Overrides Country.
Custom start / end date	dateFrom, dateTo	empty	Any period since 2004-01-01 (web) or 2008-01-01 (other properties). Overrides Time range.
Processing speed	processingSpeed	auto	Bronze and higher plans always use bulk mode (a few minutes, up to ~45 min at peak times). On Free, Auto fetches up to 20 queries instantly and uses bulk mode for larger runs. Same price in every mode.
Max queries	maxQueries	no limit	Hard cap on the number of queries in a run.

Supported URL date values include 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, all_2008, other today N-m / today N-y / now N-d values, and custom ranges like 2024-01-01 2024-06-30.

Example: Google Shopping interest in Germany, Food & Drink category, calendar year 2024, each term on its own scale, only the timeline and related queries.

{
  "searchTerms": ["matcha", "chai latte", "cold brew"],
  "compareTerms": false,
  "country": "DE",
  "language": "de",
  "searchType": "shopping",
  "categoryId": 71,
  "dateFrom": "2024-01-01",
  "dateTo": "2024-12-31",
  "includeInterestByRegion": false,
  "includeRelatedTopics": false
}

Category IDs

Google Trends has 25 top-level categories and about 1,400 subcategories. The ID is the cat= number in a Google Trends URL. The easiest way to find a subcategory ID is to pick it on trends.google.com and copy cat from the address bar (or just paste the whole URL into Google Trends URLs).

ID	Category	ID	Category
0	All categories (default)	22	Books & Literature
3	Arts & Entertainment	29	Real Estate
5	Computers & Electronics	44	Beauty & Fitness
7	Finance	45	Health
8	Games	47	Autos & Vehicles
11	Home & Garden	65	Hobbies & Leisure
12	Business & Industrial	66	Pets & Animals
13	Internet & Telecom	67	Travel
14	People & Society	71	Food & Drink
16	News	174	Science
18	Shopping	299	Online Communities
19	Law & Government	533	Reference
20	Sports	958	Jobs & Education

Popular subcategories: 32 Software, 31 Programming, 78 Consumer Electronics, 107 Investing, 37 Banking, 276 Restaurants, 68 Apparel, 34 Movies, 294 Soccer, 1080 Real Estate Listings.

Locations: countries, states and cities

• Country: a 2-letter ISO code (US, GB, DE, BR…) or an English name (Germany). Leave it empty for worldwide data.
• State, region or city: set State / region / city (geoTargetId) to a numeric Google location ID. These are the public Google Ads geo target IDs, listed in Google's geo targets file at developers.google.com/google-ads/api/data/geotargets. Examples: 21167 New York state, 21137 California, 21176 Texas, 20339 England.
• In the output: country is the ISO code (or Worldwide), location the name. Interest-by-region rows use ISO 3166-2 codes in regionCode (US-NY, GB-ENG), or country codes for worldwide searches.
• Pasted URLs: the URL's geo is used. A country (geo=DE) works as is; a sub-region (geo=US-NY) falls back to the country, so use geoTargetId for regional data.

Example output

[
  {
    "id": "c8eff4ce4b9fa3fa",
    "recordType": "interestOverTime",
    "searchTerm": "coffee",
    "comparedTerms": "coffee, tea",
    "country": "US",
    "location": "United States",
    "language": "en",
    "searchType": "web",
    "categoryId": 0,
    "timeRange": "past12Months",
    "periodStart": null,
    "periodEnd": null,
    "queryIndex": 1,
    "sourceUrl": null,
    "scrapedAt": "2026-09-28T09:00:47.505Z",
    "dataUpdatedAt": "2026-09-26T15:24:02.000Z",
    "date": "2025-09-21",
    "dateTo": "2025-09-27",
    "timestamp": "2025-09-21T00:00:00.000Z",
    "value": 100,
    "periodAverage": 92,
    "isPartial": false
  },
  {
    "id": "8c5eda87cec1a4eb",
    "recordType": "interestByRegion",
    "searchTerm": "tea",
    "comparedTerms": "coffee, tea",
    "country": "US",
    "location": "United States",
    "regionCode": "US-MS",
    "regionName": "Mississippi",
    "value": 62,
    "isLeadingTerm": true,
    "regionRank": 2
  },
  {
    "id": "9405e3c1c9a78c0b",
    "recordType": "relatedQuery",
    "searchTerm": "tea",
    "comparedTerms": "coffee, tea",
    "listType": "rising",
    "rank": 1,
    "query": "matcha tea latte",
    "value": 1250,
    "valueMeaning": "percentIncrease",
    "isBreakout": false
  },
  {
    "id": "d9beb4240e792703",
    "recordType": "relatedTopic",
    "searchTerm": "coffee",
    "comparedTerms": "coffee, tea",
    "listType": "rising",
    "rank": 1,
    "topicId": "/g/11x",
    "topicTitle": "Brown sugar oat latte",
    "topicType": "Drink",
    "value": null,
    "valueMeaning": "percentIncrease",
    "isBreakout": true
  }
]

The context fields are left out of the last three rows to keep the example short. Every row has all of them. A RUN_SUMMARY record in the key-value store shows how many queries were delivered, empty, failed or not run, how many rows of each type were produced, and any problems. The dataset has 5 views: All rows, Interest over time, Interest by region, Related queries and Related topics. Each view shows the columns of one record type; all rows stay in the same dataset, so sort or filter by the Type column (or by recordType in your code) to see one type only.

Pricing

Typical cost: 100 search terms with all 4 panels = $0.30 on Free, $0.12 on Gold and above (20 queries of 5 compared terms), plus Apify platform usage billed separately (about $0.001–$0.003 for a small run).

Speed depends on your Apify plan. On Bronze, Silver, Gold and higher plans every run uses slower bulk processing: results take a few minutes, and up to ~45 minutes at peak times. The lower prices are only possible that way. On the Free plan, runs of up to 20 queries are fetched instantly (larger runs also use bulk processing).

Apify Free plan: Apify does not pay developers for usage on its Free plan, so on the Free plan this Actor can be used for up to $0.25 of results per calendar month — enough to try it on a small input. When the allowance is used up, the run ends with a clear message (not an error). Any paid Apify plan removes the limit; prices are the same.

Pay per event: you pay per query that delivered data. Apify platform usage is billed separately by Apify (at the default 256 MB a small run typically uses about $0.001–$0.003). Prices drop automatically on higher Apify plans (see the speed note above).

Event	Free	Bronze	Silver	Gold, Platinum, Diamond
Trend query (up to 5 compared terms or 1 URL, all selected panels)	$0.015	$0.008	$0.007	$0.006
Cost per term when compared in groups of 5	$0.003	$0.0016	$0.0014	$0.0012
1,000 terms in groups of 5 (200 queries)	$3.00	$1.60	$1.40	$1.20
Query checked, no data (only when a query was processed but Google Trends had no data for it, or the answer was lost)	$0.014	$0.0037	$0.0037	$0.0037

A query is one comparison group of up to 5 search terms, one term when comparison is off, one category-only lookup, or one pasted Google Trends URL. All 4 panels are included at no extra cost.

Query checked fee. Every query we send is processed and paid for, even when Google Trends has no data for it (tiny terms, narrow locations, very short ranges) or the answer never reaches us (a dropped connection or a bulk task that did not finish in time). Such a query delivers no rows and is charged only the reduced query checked fee instead of the full query price. Queries rejected before processing (invalid settings) are free. Why is the Free-plan fee ($0.014) almost as high as a full query ($0.015)? Free-plan runs of up to 20 queries are fetched instantly, and an instant query costs us almost the same to process whether or not Google Trends has data for it. The fee only covers that processing cost, so it stays close to the query price. Paid plans (Bronze and up) use bulk processing, which costs us much less, so their fee is only $0.0037. Tip for Free-plan users: check doubtful terms in your browser first, or compare them with a popular term, so the query returns data.

Each term on its own scale costs 5× more. With Compare terms against each other turned off, every term is a separate query ($0.015 on Free, $0.006 on Gold+ per term) instead of sharing one query with 4 other terms. Keep comparison on (the default) unless you need independent 0–100 scales.

Worked examples (Free plan):

• 5 brands compared, all panels, 12 months: 1 query = $0.015.
• 100 keywords for a seasonality study, compared in groups of 5 (default): 20 queries = $0.30 ($0.12 on Gold+).
• The same 100 keywords, each on its own scale (comparison off): 100 queries = $1.50 ($0.60 on Gold+), 5× the grouped price.
• 10 niche keywords, comparison off, 3 have no data: 7 × $0.015 + 3 × $0.014 = $0.147 on Free (7 × $0.006 + 3 × $0.0037 = $0.0531 on Gold+), plus Apify platform usage.

Set a Maximum cost per run in the run options. The Actor never starts a query that would go over it and stops cleanly when the limit is reached.

De-duplicate or upsert with id

Every row has a stable id: the same week, region or related query for the same terms and settings gets the same id in every run. Use it as the primary key when you store results from scheduled runs.

from apify_client import ApifyClient

client = ApifyClient("<YOUR_TOKEN>")
run = client.actor("cheapapi/google-trends-scraper-pro").call(run_input={"searchTerms": ["coffee", "tea"], "country": "US"})
store = {}  # replace with your database table keyed by id
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    is_new = row["id"] not in store
    store[row["id"]] = row  # upsert: insert new rows, refresh values of existing ones
    if is_new and row["recordType"] == "relatedQuery" and row["listType"] == "rising":
        print("New rising query:", row["query"])

In SQL: INSERT ... ON CONFLICT (id) DO UPDATE SET value = EXCLUDED.value, "isPartial" = EXCLUDED."isPartial". The last timeline bucket is often partial (isPartial: true), so update existing rows instead of skipping them.

Integrations

• Google Sheets: export the dataset directly, or use the Apify Google Sheets integration to append rows after each run.
• Make and Zapier: use the Apify app to start the Actor and send new rows to any of thousands of apps.
• Webhooks: get a POST request to your endpoint when a run succeeds or fails, then fetch the dataset.
• Schedules: run the same input daily or weekly (e.g. every Monday at 8:00) to monitor trends over time.
• API and MCP: call the Actor from the Apify REST API, the JavaScript or Python clients above, or AI agents through the Apify MCP server.

FAQ

Is there a limit on the Apify Free plan? Yes: up to $0.25 of this Actor's results per calendar month, enough to try it. Apify pays developers nothing for Free-plan usage while our data costs are real, so this keeps the Actor sustainable. Runs that reach the allowance stop cleanly and keep everything collected so far; the allowance resets on the 1st of the month. Any paid Apify plan has no limit.

Is it legal to scrape Google Trends? Google Trends shows public, aggregated and anonymised statistics. It contains no personal data. Collecting publicly available data is generally legal, but you are responsible for how you use it. Check Google's terms and your local law, and ask a lawyer if you are unsure.

How fresh is the data? Data is collected from Google Trends when you run the Actor. dataUpdatedAt shows when the numbers were retrieved and scrapedAt shows when the row was written. Short ranges (past hour/day) are near real time. The last bucket of a timeline may be incomplete (isPartial: true).

Why are the values between 0 and 100? That is how Google Trends reports interest. 100 is the peak popularity within the chosen comparison, time range and location. Values are relative, not absolute search volumes. In rising lists value is the percent increase instead; when Google shows "Breakout" (growth above 5000%) we set isBreakout: true and value: null.

Why do my numbers change when I compare different terms? Values are scaled within each comparison group. To keep every term on its own scale, turn off Compare terms against each other. Each term then counts as one query, so the run costs 5× more than grouping terms in fives.

Can I paste a Google Trends link instead of typing terms? Yes. Paste it into Google Trends URLs. The terms, country, period, category, property and language come from the URL. Sub-regions in the URL (e.g. geo=US-NY) fall back to the whole country; use the State / region / city option for regional data instead. Topic IDs such as /m/02vqfm are skipped. Type the topic name as a search term instead.

Why did I get fewer results than expected, or a "query checked" fee? Low-volume terms, very short time ranges and narrow locations often have no data, or have empty related lists, on Google Trends too. Also check Max related items per list, Max queries, your maximum cost per run and the RUN_SUMMARY record. A query checked fee means one of your queries was processed but returned no data (Google Trends has nothing for that term, place or period), or its answer was lost on the way. Processing it still costs us, so it is charged the reduced fee ($0.014 on Free, $0.0037 on paid plans) instead of the full query price. RUN_SUMMARY.queriesCheckedCharged shows how many. To avoid it, compare low-volume terms with popular ones, widen the location or time range, and check a Google Trends URL in your browser first.

How do I control my budget, and why is there a separate platform usage charge? Set Maximum cost per run in the run options and/or Max queries in the input. Group terms in fives (the default) for the lowest cost per term. Apify bills the compute a run uses separately from the query price; at the default 256 MB a small run typically uses about $0.001–$0.003.

Can I monitor trends automatically? Yes. Save your input as a task, add a schedule (daily or weekly) and connect a webhook, Google Sheets, Make or Zapier to get every new run's rows. Every row has a stable id: the same week, region or related query gets the same id in every run, so you can upsert rows into a sheet or database and keep one row per data point. To see only new rising queries, keep the ids of relatedQuery rows from earlier runs and filter out the ones you have already seen. Each run is charged per query, so a weekly check of 5 terms costs $0.015 (Free) per week.

Which export formats are available? JSON, CSV, Excel, XML, HTML table and RSS from the Output tab or the dataset API. Rows are flat, so spreadsheets need no extra processing.

Something is wrong. How do I get help? Open an issue on the Actor's Issues tab with your run ID and the input you used. We read every issue and fix confirmed problems quickly.

Limitations

• Google Trends data is relative (0–100) and sampled. Small terms often return no data, and repeated runs can differ slightly. That is how Google Trends works.
• Related queries and topics are often empty for short time ranges (past hour/day) or low-volume terms.
• Regional, related-query and related-topic data is available for Web searches. For News searches it is not available (confirmed in live tests): those queries return interest over time only (still one query charge), the log says so and RUN_SUMMARY.panelsUnavailableFor lists the property. For Image, Shopping and YouTube searches all 4 panels are requested, but this has not been verified in live tests; if Google Trends rejects the extra panels for a property, the Actor detects it during the run and falls back to interest over time for that property in the same way. Web queries in the same run are not affected.
• Pasted URLs: a sub-region geo (e.g. US-NY) falls back to the country, topic IDs are skipped, and only the first 5 terms are used.
• Bulk mode (all runs on Bronze and higher plans, large runs on Free) can take several minutes, up to about 45 minutes at peak times.
• "Trending now" / daily trending searches are out of scope (see Not included). This Actor covers the Google Trends Explore view.

Privacy

This Actor collects only aggregated, anonymised search-interest statistics published by Google Trends. It does not collect or output personal data. Your inputs and results are stored in your own Apify account and are not shared with anyone.