Google Trends Scraper – Interest & Related Queries

What does Google Trends Scraper – Interest & Related Queries do?

Google Trends Scraper collects Google Trends data for your search terms: how interest in each term moved over time, where it is searched most (by country or by region), and which related queries are top and rising, as clean rows you can export or call by API.

Unofficial, not affiliated with Google Trends.

Main features: search by keyword, up to 10,000 term results per run, no login needed, export to JSON, CSV or Excel, and full API and scheduling support on Apify.

What data can you get from Google Trends?

Each term result is one row with these fields:

Field	Description
id	Unique id of the result: terms, term, geo, time range, category and data type (always filled)
term	The search term (always filled)
dataType	Which data this result holds: interestOverTime, interestByRegion or relatedQueries (always filled)
url	The Google Trends page for the same search (always filled)
comparedWith	The other terms compared on the same scale (empty when the term was looked up alone)
geo	Country or region code (empty = worldwide)
timeRange	Time range of the data (e.g. today 12-m)
category	Google Trends category id (0 = all categories)
rowCount	Number of points, regions or queries in this result
interestOverTime	For interestOverTime: [{ date, value, isPartial }], value 0–100 relative to the peak (UTC dates)
interestByRegion	For interestByRegion: [{ geoCode, geoName, value }], value 0–100 relative to the top region
relatedQueries	For relatedQueries: { top: [...], rising: [...] }, each entry { query, value, formattedValue, link }
scrapedAt	When the result was collected (ISO 8601)

How to scrape Google Trends with Google Trends Scraper – Interest & Related Queries

1. Open Google Trends Scraper – Interest & Related Queries on Apify and go to the Input tab.
2. In Search terms, enter coffee.
3. In Time range, enter today 12-m.
4. Click Start and wait for the run to finish. A small run like this one takes under a minute.
5. Open the Output tab to see the results, or download them as JSON, CSV, Excel or HTML.

Input

See the Input tab for every option. The fields:

Field	Name	What it does	Default
searchTerms	Search terms	Search terms to look up (e.g. coffee). Each entry is looked up on its own. To compare up to 5 terms on one scale, put them in one entry separated by commas (e.g. coffee, tea).
geo	Country or region	Two-letter country code (e.g. US, GB, DE) or a region code (e.g. US-CA). Leave empty for worldwide.	""
timeRange	Time range	The period the data covers. Options: now 1-H (Past hour), now 4-H (Past 4 hours), now 1-d (Past day), now 7-d (Past 7 days), today 1-m (Past 30 days), today 3-m (Past 90 days), today 12-m (Past 12 months), today 5-y (Past 5 years), all (Since 2004).	"today 12-m"
category	Category	Google Trends category id, to narrow an ambiguous term (e.g. 71 = Food & Drink, 5 = Computers & Electronics). 0 = all categories.	0
includeRegions	Include interest by region	Adds one result per term with interest by country (worldwide) or by region (when a country is set).	true
includeRelated	Include related queries	Adds one result per term with its top and rising related queries.	true
maxItems	Max items	Maximum number of results in total. Each term gives up to 3 results: interest over time, interest by region and related queries. You pay only for results saved.	100

Example input:

{
  "searchTerms": [
    "coffee"
  ],
  "timeRange": "today 12-m"
}

Output

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel. One real result:

{
  "id": "coffee|coffee|worldwide|today 12-m|0|interestOverTime",
  "term": "coffee",
  "dataType": "interestOverTime",
  "url": "https://trends.google.com/trends/explore?q=coffee&date=today+12-m",
  "comparedWith": [],
  "geo": "",
  "timeRange": "today 12-m",
  "category": 0,
  "rowCount": 53,
  "interestOverTime": [
    {
      "date": "2025-10-05T00:00:00.000Z",
      "value": 62,
      "isPartial": false
    },
    {
      "date": "2025-10-12T00:00:00.000Z",
      "value": 63,
      "isPartial": false
    },
    {
      "date": "2025-10-19T00:00:00.000Z",
      "value": 62,
      "isPartial": false
    },
    {
      "date": "2025-10-26T00:00:00.000Z",
      "value": 55,
      "isPartial": false
    },
    {
      "date": "2025-11-02T00:00:00.000Z",
      "value": 62,
      "isPartial": false
    },
    {
      "date": "2025-11-09T00:00:00.000Z",
      "value": 70,
      "isPartial": false
    },
    {
      "date": "2025-11-16T00:00:00.000Z",
      "value": 73,
      "isPartial": false
    },
    {
      "date": "2025-11-23T00:00:00.000Z",
      "value": 73,
      "isPartial": false
    },
    {
      "date": "2025-11-30T00:00:00.000Z",
      "value": 70,
      "isPartial": false
    },
    {
      "date": "2025-12-07T00:00:00.000Z",
      "value": 67,
      "isPartial": false
    },
    {
      "date": "2025-12-14T00:00:00.000Z",
      "value": 68,
      "isPartial": false
    },
    {
      "date": "2025-12-21T00:00:00.000Z",
      "value": 67,
      "isPartial": false
    },
    {
      "date": "2025-12-28T00:00:00.000Z",
      "value": 66,
      "isPartial": false
    },
    {
      "date": "2026-01-04T00:00:00.000Z",
      "value": 62,
      "isPartial": false
    },
    {
      "date": "2026-01-11T00:00:00.000Z",
      "value": 63,
      "isPartial": false
    },
    {
      "date": "2026-01-18T00:00:00.000Z",
      "value": 66,
      "isPartial": false
    },
    {
      "date": "2026-01-25T00:00:00.000Z",
      "value": 67,
      "isPartial": false
    },
    {
      "date": "2026-02-01T00:00:00.000Z",
      "value": 71,
      "isPartial": false
    },
    {
      "date": "2026-02-08T00:00:00.000Z",
      "value": 90,
      "isPartial": false
    },
    {
      "date": "2026-02-15T00:00:00.000Z",
      "value": 97,
      "isPartial": false
    },
    {
      "date": "2026-02-22T00:00:00.000Z",
      "value": 76,
      "isPartial": false
    },
    {
      "date": "2026-03-01T00:00:00.000Z",
      "value": 66,
      "isPartial": false
    },
    {
      "date": "2026-03-08T00:00:00.000Z",
      "value": 66,
      "isPartial": false
    },
    {
      "date": "2026-03-15T00:00:00.000Z",
      "value": 68,
      "isPartial": false
    },
    {
      "date": "2026-03-22T00:00:00.000Z",
      "value": 78,
      "isPartial": false
    },
    {
      "date": "2026-03-29T00:00:00.000Z",
      "value": 90,
      "isPartial": false
    },
    {
      "date": "2026-04-05T00:00:00.000Z",
      "value": 94,
      "isPartial": false
    },
    {
      "date": "2026-04-12T00:00:00.000Z",
      "value": 92,
      "isPartial": false
    },
    {
      "date": "2026-04-19T00:00:00.000Z",
      "value": 81,
      "isPartial": false
    },
    {
      "date": "2026-04-26T00:00:00.000Z",
      "value": 90,
      "isPartial": false
    },
    {
      "date": "2026-05-03T00:00:00.000Z",
      "value": 100,
      "isPartial": false
    },
    {
      "date": "2026-05-10T00:00:00.000Z",
      "value": 98,
      "isPartial": false
    },
    {
      "date": "2026-05-17T00:00:00.000Z",
      "value": 83,
      "isPartial": false
    },
    {
      "date": "2026-05-24T00:00:00.000Z",
      "value": 84,
      "isPartial": false
    },
    {
      "date": "2026-05-31T00:00:00.000Z",
      "value": 81,
      "isPartial": false
    },
    {
      "date": "2026-06-07T00:00:00.000Z",
      "value": 86,
      "isPartial": false
    },
    {
      "date": "2026-06-14T00:00:00.000Z",
      "value": 86,
      "isPartial": false
    },
    {
      "date": "2026-06-21T00:00:00.000Z",
      "value": 83,
      "isPartial": false
    },
    {
      "date": "2026-06-28T00:00:00.000Z",
      "value": 70,
      "isPartial": false
    },
    {
      "date": "2026-07-05T00:00:00.000Z",
      "value": 73,
      "isPartial": false
    },
    {
      "date": "2026-07-12T00:00:00.000Z",
      "value": 74,
      "isPartial": false
    },
    {
      "date": "2026-07-19T00:00:00.000Z",
      "value": 78,
      "isPartial": false
    },
    {
      "date": "2026-07-26T00:00:00.000Z",
      "value": 74,
      "isPartial": false
    },
    {
      "date": "2026-08-02T00:00:00.000Z",
      "value": 76,
      "isPartial": false
    },
    {
      "date": "2026-08-09T00:00:00.000Z",
      "value": 76,
      "isPartial": false
    },
    {
      "date": "2026-08-16T00:00:00.000Z",
      "value": 74,
      "isPartial": false
    },
    {
      "date": "2026-08-23T00:00:00.000Z",
      "value": 73,
      "isPartial": false
    },
    {
      "date": "2026-08-30T00:00:00.000Z",
      "value": 82,
      "isPartial": false
    },
    {
      "date": "2026-09-06T00:00:00.000Z",
      "value": 78,
      "isPartial": false
    },
    {
      "date": "2026-09-13T00:00:00.000Z",
      "value": 81,
      "isPartial": false
    },
    {
      "date": "2026-09-20T00:00:00.000Z",
      "value": 86,
      "isPartial": false
    },
    {
      "date": "2026-09-27T00:00:00.000Z",
      "value": 64,
      "isPartial": false
    },
    {
      "date": "2026-10-04T00:00:00.000Z",
      "value": 59,
      "isPartial": true
    }
  ],
  "interestByRegion": null,
  "relatedQueries": null,
  "scrapedAt": "2026-10-08T04:16:16.066Z"
}

How much does it cost to scrape Google Trends?

Google Trends Scraper – Interest & Related Queries uses pay-per-event pricing: $1.25 per 1,000 term results (Term result). You pay only for term results saved to the dataset; platform usage is included in the price. Each run also has Apify's standard start fee of a fraction of a cent.

Term results	Price
100	$0.13
1,000	$1.25
10,000	$12.50

Set Max items (and the run's maximum cost in the run options) to cap what a run can spend. The run stops when either limit is reached.

Use cases

• Keyword and SEO research: see whether a keyword is growing or fading before you write for it, and find rising related queries to target next.
• Market and product research: compare interest in brands, products or categories (up to 5 on one scale) across countries and time.
• Content planning: spot seasonal peaks (e.g. weekly data over 12 months) and plan posts, campaigns and stock around them.
• Regional targeting: find the countries or regions where a term is searched most, for ads and launch planning.
• Dashboards and monitoring: schedule a daily or weekly run and feed the data into Google Sheets, BI tools or your own app.

Tips

• Put terms you want to compare in one entry separated by commas (e.g. coffee, tea, matcha): their values share one 0–100 scale. Entries on separate lines are each scaled on their own.
• Values are relative: 100 is the peak for the term (or for the group) in the chosen period, not a search count.
• Set Country or region to get interest by region inside that country (e.g. US gives US states).
• Use a Category to narrow an ambiguous term (e.g. "apple" in Food & Drink, id 71).
• Turn off Include interest by region or Include related queries if you only need the timeline: you pay only for the results you get.
• Very rare terms may have no data; such terms give no results and cost nothing.

FAQ

Is it legal to scrape Google Trends?

Google Trends Scraper – Interest & Related Queries collects only data that Google Trends shows publicly to every visitor, and it leaves out personal data such as private profiles and contact details. Our Actors are ethical and do not extract private user data. Results could still contain personal data in titles or descriptions written by uploaders. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers. You are responsible for how you use the data.

Can I use it through an API?

Yes. Every Apify Actor has an API: start runs, pass input and read results over HTTP, or use the JavaScript and Python clients. See the API tab on the Actor page for ready-made examples.

Can I schedule runs and connect other apps?

Yes. Use Apify Schedules to run Google Trends Scraper – Interest & Related Queries every hour, day or week, and integrations (Make, Zapier, Google Sheets, webhooks and more) to send the results where you need them.

Why did I get fewer results than expected?

The run stops at Max items or at the run's maximum cost, whichever comes first. Google Trends may also return fewer results for rare search terms. Duplicates across search terms are saved once. Check the run's RUN_SUMMARY record in the key-value store for the inputs that returned nothing.

What if it stops working?

Sites change. We run a daily check of our own and fix breakages quickly. If something looks wrong, open an issue in the Issues tab with the run link and we'll look at it.

What do the values mean?

Google Trends scales each search from 0 to 100: 100 is the highest point for the term (or the compared group) in the chosen place and period, 50 is half as popular, 0 means not enough data. They are not absolute search volumes.

What is a rising query and what does "Breakout" mean?

Rising queries grew the most in the chosen period compared with the period before it; the value is the growth in percent. "Breakout" means growth above 5,000 %, usually a new query.

Why are there several results per term?

Each term gives one result per data type (interest over time, interest by region, related queries), so you can pick and pay for only the data you need.

Are related topics included?

Not at the moment: Google currently returns no related topics for these requests, so they are left out rather than sent empty.

Not included

No personal data: Google Trends data is aggregated and anonymous. Related topics, daily trending searches and absolute search volumes are not included.

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