Google Trends Scraper

Scrape Google Trends at scale — starting with the live Trending Now feed: everything a country is searching for right now, with search volume, growth %, start time, and related queries attached. Plus interest over time, interest by region, and top + rising related queries for any search term, in any country, over any window back to 2004. Pure HTTP against the Trends JSON API: no browser, no Playwright, fast and cheap to run.

✨ Why use this scraper?

• Live Trending Now feed — the full "trending now" board for any country (typically 250+ topics per 24h), each with absolute search volume, growth %, when it started, and its related queries. Most Trends scrapers don't return this at all.
• All four core Trends datasets — interest-over-time timelines, region breakdowns, top + rising related queries, and related topics — in the same actor and the same run.
• Many terms and many countries per run — searchTerms and geos are both lists, so one run covers bitcoin/ethereum across US/GB/DE. No one-run-per-country juggling.
• Compare mode — query up to 5 terms in a single request so their 0–100 interest values are relative to each other, exactly like the compare view on trends.google.com.
• No browser overhead — the actor speaks directly to the same JSON API the Trends web app uses. Runs finish in seconds, not minutes.
• Any geo, any window — worldwide, country (US, GB), or region (US-CA); from the past hour back to 2004.
• Web, News, YouTube, Images, Shopping — measure interest on any Google property.
• Built-in residential proxies — no proxy setup needed; sessions rotate automatically on rate limits.
• Clean, predictable output — one dataset row per term × location × data type; one row per trending topic. No padding rows.

🎯 Use cases

• SEO & content planning — find rising queries around your topic before the competition writes about them.
• Market & brand research — track how interest in a brand, product, or ticker moves across time and geography.
• Newsrooms & social teams — poll the Trending Now feed on a schedule and catch breaking topics with search-volume and growth numbers attached.
• E-commerce & merchandising — spot seasonal demand curves and regional hot-spots for product categories.
• Investment & alt-data signals — search interest is a leading indicator; pipe timelines into your models as CSV/JSON.
• Academic & social research — reproducible interest data across dozens of geos in one run.

📥 Supported inputs

Input	What it does
searchTerms	Keywords or Google topic ids (/m/05p0rrx) — each queried independently
compareKeywords	Compare all terms in one request (groups of ≤5, relative 0–100 scale)
geos	US, GB, DE, US-CA, … or empty string for worldwide — every term is queried per geo
timeRange	now 1-H → all (2004–present), same windows as the Trends UI
property	Web Search (default), Images, News, YouTube, Shopping
category	Trends category id to narrow results (0 = all)
includeInterestOverTime	Interest timeline — on by default
includeInterestByRegion	Geographic breakdown by country/subregion — on by default
includeRelatedQueries	Top + rising related search queries — on by default
includeRelatedTopics	Top + rising related topics (entities) — off by default, see FAQ
trendingNow + trendingNowGeo + trendingNowHours	Live Trending Now feed per country, 4h–7d window
maxItems	Hard cap on rows produced, and therefore on run cost

🔄 How it works

1. The actor opens a Trends session and performs the token handshake the web app uses.
2. For every term × location, one explore call issues per-widget API tokens.
3. The requested datasets (timeline, regions, related queries) are fetched in parallel with those tokens.
4. Trending Now is fetched from the live RPC feed the new Trends UI uses — search volume, growth %, start time, and related queries included.
5. Each dataset is normalized and pushed as one structured row. Failed queries retry on a fresh proxy session.

⚙️ Input parameters

{
    "searchTerms": ["bitcoin", "chatgpt"],
    "geos": ["US"],
    "timeRange": "today 12-m",
    "includeInterestOverTime": true,
    "includeInterestByRegion": true,
    "includeRelatedQueries": true,
    "includeRelatedTopics": false,
    "trendingNow": true,
    "trendingNowGeo": "US",
    "trendingNowHours": "24",
    "maxItems": 100
}

📊 Output overview

One row per term × location × data type, plus one row per trending topic when the Trending Now feed is enabled. Every row carries the query context (keyword, geo, timeRange, category, property, exploreUrl) so it's self-describing in CSV exports.

💵 What a run actually costs

You're billed one result per dataset, not one result per data point. A 5-year weekly timeline arrives as a single row with 260 points inside it — not 260 billed rows. The same holds for a region breakdown (every region in one row) and a related-queries set (top + rising in one row). Empty relatedTopics responses are skipped rather than billed.

You only pay for the datasets you switch on. Want just the timeline? Turn the other three off and a keyword costs one row instead of four — nothing is forced into a mandatory all-in-one result you pay for whether you use it or not. maxItems then puts a hard ceiling on top of that.

At $0.003 per result plus a $0.005 run-start fee:

Run	Rows	Cost
1 term × 1 country, 3 core datasets	3	~$0.014
3 terms × 2 countries, 3 core datasets	18	~$0.06
5 terms × 5 countries, 3 core datasets	75	~$0.23
Full Trending Now board, US, 24h	~260	~$0.79 — set maxItems to take just the top N

The Trending Now feed is deep (250+ topics for a large country in a 24h window), so maxItems doubles as your spend cap — rows come back rank-ordered, so maxItems: 25 gives you the top 25 trending topics.

📦 Output sample

Interest over time:

{
    "dataType": "interestOverTime",
    "keyword": "bitcoin",
    "geo": "US",
    "timeRange": "today 12-m",
    "property": "web",
    "exploreUrl": "https://trends.google.com/trends/explore?q=bitcoin&geo=US",
    "averages": [43],
    "timeline": [
        { "time": "1752364800", "formattedTime": "Jul 13 – 19, 2025", "values": [53], "isPartial": false },
        { "time": "1784332800", "formattedTime": "Jul 19 – 25, 2026", "values": [21], "isPartial": true }
    ]
}

Related queries:

{
    "dataType": "relatedQueries",
    "keyword": "chatgpt",
    "geo": "US",
    "top": [{ "query": "what is chatgpt", "value": 100, "formattedValue": "100" }],
    "rising": [{ "query": "chatgpt agent", "value": 850, "formattedValue": "+850%" }]
}

Trending Now:

{
    "dataType": "trendingNow",
    "geo": "US",
    "rank": 1,
    "query": "world cup final",
    "searchVolume": 1000000,
    "formattedSearchVolume": "1M+",
    "percentGrowth": 300,
    "startTimestamp": 1784406000,
    "startedAgoSeconds": 81754,
    "relatedQueries": ["world cup final", "what time is the world cup game today"]
}

🗂 Key output fields

Field	Meaning
dataType	interestOverTime | interestByRegion | relatedQueries | relatedTopics | trendingNow
timeline[].values	Relative search interest 0–100 (100 = peak within the window/compare set)
timeline[].isPartial	Last point is usually partial — the current period isn't finished yet
regions[].geoCode	ISO region code (US-WY), with the same 0–100 scaling
top / rising	Related queries: top = most popular, rising = fastest-growing (value is % growth, formattedValue may be "Breakout")
searchVolume / formattedSearchVolume	Trending Now: absolute volume bucket (e.g. 1000000 / "1M+")
percentGrowth	Trending Now: growth vs the previous period, in %
keywords	Compare mode: the full compare set; per-point values align with this array

❓ FAQ

Are the interest values absolute search counts? No — Google Trends only publishes relative interest, scaled 0–100 against the peak in your chosen window (or across the compare set in compare mode). Only Trending Now rows carry absolute volume buckets.

Why is relatedTopics sometimes missing? Google currently serves an empty related-topics list to anonymous (not signed-in) sessions, while related queries are unaffected. That's why includeRelatedTopics is off by default. When the list does come back empty the actor skips the row entirely, so you're never billed for an empty result — switch it on if you want the actor to try anyway.

Can I compare more than 5 terms? Google's compare view caps at 5 terms per request. With compareKeywords on, the actor splits longer lists into groups of 5 (each group is internally consistent). For cross-group comparability, include a shared anchor term in each group.

How fresh is Trending Now? It's the same live feed the trends.google.com "Trending now" page shows, queryable for windows of 4, 24, 48, or 168 hours per country.

Do I need my own proxies? No. The actor ships with built-in residential proxy routing and rotates sessions automatically. The proxy input exists only as an explicit override.

What happens when Google rate-limits? Each failed query retries up to maxRequestRetries times on a fresh proxy session before being dropped; everything that succeeded is kept.

💬 Support

• Found a bug or need a field added? Open an issue on the actor's Issues tab in the Apify Console — issues are answered within a day.
• Custom requirements (bigger volumes, extra endpoints, private deployment)? Message via the Apify contact form.

🛠 Additional services

• Scheduling & monitoring — run the actor on a cron schedule from the Apify Console and export new rows to a webhook, database, or Google Sheets.
• Custom scrapers — need another data source scraped to the same standard? Get in touch via the profile below.

🔎 Explore more scrapers

• Keyword Metrics — the absolute numbers Trends can't give you: Google + Bing search volume, CPC, competition, top-of-page bids, difficulty, search intent, and ~93 months of history. 1000 keywords per run at one flat price, or expand 20 seeds into up to 20,000 keyword ideas across 111 countries and 46 languages.
• Ahrefs Scraper — Domain Rating, backlinks, keyword ideas, and AI visibility for any domain.
• Semrush Scraper — traffic estimates, authority score, backlinks, and top websites per niche.
• Ubersuggest Scraper — keyword volume, CPC, difficulty, and domain backlink data.

If this Google Trends Scraper was useful, see other scrapers and actors at memo23's Apify profile — covering job boards, real estate, e-commerce, social media, and more.

🤖 For AI Agents & LLM Apps

This actor is agent-friendly: deterministic JSON output, one self-describing row per query, and a compact input schema of plain booleans. Call it from LLM tool-use / MCP setups to give your agent live search-interest data — e.g. "how has interest in X moved this year, and what related queries are rising?" maps to one run with includeInterestOverTime + includeRelatedQueries and everything else switched off. Use maxItems to bound cost per call, and prefer trendingNow for "what's happening right now" questions.

⚠️ Disclaimer

This actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Google. It retrieves only publicly available data that Google Trends serves to any visitor without signing in. "Google", "Google Trends", and related marks are trademarks of Google LLC. You are responsible for how you use the data, including compliance with applicable laws and Google's terms of service. Interest values are Google's own relative, sampled estimates and may change between requests.

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