Google Trends Scraper — Interest, Regions, Related & Trending

Google Trends Scraper is an Apify actor that extracts data from Google Trends — search-interest over time, interest by region, top and rising related queries, and today's country-level trending searches — without an official API key.

Use it to spot rising keywords before you write content, benchmark a brand or product's search interest across countries, feed a trend-monitoring dashboard, or catch a topic the moment it starts trending in a country — exported to JSON, CSV, or Excel.

Table of contents

• What the Google Trends Scraper does
• How to scrape Google Trends data
• Google Trends scraper input
• What data you get
• Pricing
• Why residential proxy for Google Trends
• Why this Google Trends scraper
• FAQ
• Rate this actor
• Related actors

What the Google Trends Scraper does

• 📈 Interest over time — the 0–100 search-interest index per date for one or more keywords, so you can chart momentum and seasonality.
• 🌍 Interest by region — the same 0–100 index broken down by country/region, so you can see where a keyword is hottest.
• 🔎 Related queries — the top and rising related searches Google Trends surfaces for a keyword, great for keyword-research and content ideation.
• 🔥 Trending now — today's trending searches for a country, no keyword required — catch what's blowing up right now.
• ⚡ HTTP-only, no anti-bot browser tier — no login, no CAPTCHA solving; runs are fast and cheap when a residential proxy keeps requests unblocked.

How to scrape Google Trends data

1. Click Try for free and open the actor.
2. Choose data_type — one per run: interest_over_time (default), interest_by_region, related_queries, or trending_now. Each produces a clean, single-schema dataset.
3. For the three keyword-based types, enter 1–5 keywords. For trending_now, enter a geo country code instead (no keywords needed).
4. Optionally narrow with geo (country code), timeframe (e.g. today 12-m), and category.
5. Click Start and watch results stream into the dataset.
6. Export as JSON, CSV, or Excel, or pull from the Apify API.

Interest over time for two keywords, last 12 months, worldwide:

{
  "data_type": "interest_over_time",
  "keywords": ["bitcoin", "ethereum"],
  "timeframe": "today 12-m"
}

Interest by region for one keyword, US only:

{
  "data_type": "interest_by_region",
  "keywords": ["bitcoin"],
  "geo": "US"
}

Top and rising related queries for a keyword:

{
  "data_type": "related_queries",
  "keywords": ["bitcoin"],
  "timeframe": "today 12-m"
}

Today's trending searches in the US (no keyword needed):

{
  "data_type": "trending_now",
  "geo": "US"
}

Google Trends scraper input

Field	Type	Default	Description
data_type	enum	interest_over_time	One per run: interest_over_time, interest_by_region, related_queries, or trending_now.
keywords	string[]	—	1–5 search terms. Required for all data types except trending_now.
geo	string	"" (worldwide)	Two-letter country code, e.g. US, GB, VN. Required for trending_now.
timeframe	select	Past 12 months	Time range to measure: Past hour / 4 hours / day / 7 days / 30 days / 90 days / 12 months / 5 years / 2004–present. Used by interest & related types only.
category	select	All categories	Narrow to a Google Trends topic (Finance, Sports, Health, …) — disambiguates terms like apple (Finance = stock, Food & Drink = fruit). Default: all categories.
hl	string	en-US	UI language for the underlying Trends request.
tz	int	-420	Timezone offset in minutes used by Trends (e.g. -420 for PDT).
max_records	int	—	Cap total rows per run — useful for interest_by_region / trending_now.
proxy	object	—	Apify Proxy configuration. Residential strongly recommended — see Why residential proxy.

What data you get

Interest over time (data_type: interest_over_time)

One row per date per keyword. Sample from a live run:

{
  "data_type": "interest_over_time",
  "keyword": "bitcoin",
  "date": "2026-06-14",
  "value": 62,
  "is_partial": false,
  "geo": "",
  "timeframe": "today 12-m",
  "category": 0,
  "scraped_at": "2026-07-17T09:12:44.118203+00:00"
}

Interest by region (data_type: interest_by_region)

One row per region per keyword. Sample from a live run:

{
  "data_type": "interest_by_region",
  "keyword": "bitcoin",
  "geo_name": "California",
  "geo_code": "US-CA",
  "value": 100,
  "geo": "US",
  "timeframe": "today 12-m",
  "category": 0,
  "scraped_at": "2026-07-17T09:12:44.118203+00:00"
}

Related queries (data_type: related_queries)

One row per top/rising related query per keyword. Sample from a live run:

{
  "data_type": "related_queries",
  "keyword": "bitcoin",
  "rank_type": "rising",
  "query": "bitcoin etf",
  "value": 850,
  "geo": "",
  "timeframe": "today 12-m",
  "scraped_at": "2026-07-17T09:12:44.118203+00:00"
}

Trending now (data_type: trending_now)

One row per trending topic for the requested country. Sample from a live run:

{
  "data_type": "trending_now",
  "geo": "US",
  "rank": 1,
  "title": "NBA Draft",
  "search_volume": "200K+",
  "started": "2026-07-17T08:00:00Z",
  "related_terms": "NBA Draft 2026, draft order, mock draft",
  "scraped_at": "2026-07-17T09:12:44.118203+00:00"
}

Field	Description
data_type	Which of the four data types this row belongs to (matches the run's data_type input).
keyword	The search term this row is about (interest/related types only).
date / value / is_partial	Date, 0–100 search-interest index, and whether that data point is a still-incomplete final period (interest over time).
geo_name / geo_code / value	Region name, region code, and 0–100 interest index (interest by region).
rank_type / query / value	top or rising, the related search term, and its relative value (related queries).
rank / title / search_volume / started / related_terms	Trending topic's rank, title, estimated search volume, start time, and related terms (trending now).
geo, timeframe, category, scraped_at	Request parameters echoed back plus the capture timestamp.

Pricing

This actor uses pay-per-event pricing — you pay for what you scrape, not for time. Pricing below is provisional until Console monetization is finalized (see the Monetization tab for current live pricing).

Event	USD	Per 1,000
Actor start (per run)	$0.001	—
Trend result scraped (trend-result)	$0.005	$5

Typical run	Cost
1 keyword, interest over time	~$0.006
5 keywords, interest over time	~$0.026
1 country, trending now	~$0.006

A "result" is one delivered keyword result set (interest-over-time series, interest-by-region breakdown, or related-queries set for one keyword) or one country's trending feed — not per individual row, so a 12-month daily timeline still bills as a single event.

Why residential proxy for Google Trends

Google Trends aggressively rate-limits and blocks datacenter IPs (typically HTTP 429) after a handful of requests. A residential proxy is strongly recommended for anything beyond a one-off run — set it under Proxy configuration in the input. Apify Proxy (residential) works out of the box.

If you run your own scrapers (inside or outside Apify) and need reliable residential proxies for scale, we use DataImpulse — pay-as-you-go IPs with per-country targeting and no monthly minimum:

👉 Get DataImpulse proxies (referral link)

Why this Google Trends scraper

• Four data types, one actor — interest over time, interest by region, related queries, and trending now, each a clean single-schema dataset; pick one per run.
• No anti-bot browser tax — HTTP-only, no browser, no CAPTCHA solving, so runs are fast and cheap as long as requests stay unblocked.
• No official API key needed — Google Trends has no public API; this actor replicates the same requests the trends.google.com website makes.
• Validated output — every row is Pydantic-validated before it's pushed; malformed entries are dropped, not shipped with garbage fields.
• Open source — the underlying google-trends-scraper Python package ships a Typer CLI and a FastAPI server; the Apify wrapper is a thin layer.

FAQ

Is this the official Google Trends API? No — Google Trends has no official public API. This actor scrapes the same public data the trends.google.com website displays, via the same requests the site's own frontend makes.

Why are values 0–100 instead of raw search counts? That's how Google Trends itself reports interest — a relative index scaled to the highest point in the selected timeframe/region (100 = peak popularity), not an absolute search-volume count. Google does not publish raw numbers.

How many keywords can I compare at once? Up to 5 per run, matching Google Trends' own comparison limit for interest_over_time, interest_by_region, and related_queries.

Do I need a proxy? Not strictly, but Google Trends blocks datacenter IPs quickly (HTTP 429). A residential proxy (Apify Proxy or your own) is strongly recommended beyond light, occasional use — see Why residential proxy.

Can I get multiple data types in one run? One data_type per run by design, so every run's dataset has a single clean schema. Run the actor again with a different data_type to get another.

Is scraping Google Trends legal? This actor collects only publicly available, aggregated search-interest data — no personal data. You are responsible for complying with Google's terms and applicable laws.

Rate this actor ⭐

If the Google Trends Scraper saved you time, please leave a review on its Apify Store page — ratings help other people find it and tell us what to build next. Hit a bug or missing field? Open an issue or contact us through the actor's Issues tab and we'll fix it fast — recency and reliability are what keep this actor ranking.

Related actors

Building a trend-research or content-intelligence pipeline? Pair this actor with our other scrapers — same proxy config format, same Pydantic-validated output, all open source.

• levels.fyi Salary Scraper — another HTTP-only, no-anti-bot actor if you're assembling a lightweight-scrape toolkit.
• Indeed Job Scraper — cross-reference job-market demand against the keywords trending here.
• Google Play App Search & Reviews Scraper — pair app search-interest trends with real app reviews.