Google Trends Scraper

Google Trends as clean JSON: the 0–100 interest over time timeline, interest by region, top + rising related queries, multi-term compare on one shared scale, and the live Trending Now board for any country with search volume, growth % and start time attached. Any term, any country or region, any window back to 2004, on any Google property. Pure HTTP against the same JSON API the Trends site uses, so a run finishes in seconds.

You pay for delivered results. A fetch that fails, is blocked, or comes back empty never becomes a dataset row and is never charged as one. It is named in the run's ERRORS record with the reason and what to change, and the run's status line reconciles the arithmetic: results asked for = results delivered + results reported. The run-stats box on this page is public; check the live success rate yourself.

✨ Why use this scraper?

• Live Trending Now feed — the full board for any country (600+ topics per 24 h for the US), each with absolute search volume, growth %, when it started, and its related queries. Most Trends scrapers do not return this at all.
• All four Trends datasets in one run — timelines, region breakdowns, top + rising related queries, and related topics (see the note on topics below).
• Many terms × many countries per run — searchTerms and geos are both lists, so one run covers bitcoin / ethereum across US / GB / DE.
• Compare mode — up to 5 terms in one request, interest values relative to each other, exactly like the compare view on trends.google.com.
• Every miss is explained, none is billed — a term below Google's volume floor, a rate-limited call, a withheld topics feed: each is a named line in the ERRORS record, never an empty row you paid for.
• No browser, no API key, no proxy setup — residential routing is built in; runs finish in seconds.
• Any geo, any window, any property — worldwide, country (US) or region (US-CA); from the past hour back to 2004; Web, News, YouTube, Images, Shopping.
• Self-describing rows — one row per term × location × data type, each carrying its full query context, so CSV exports and downstream joins need no lookup.

🧾 What you pay for, and what you never pay for

One result = one delivered row. A 12-month timeline is one row with every week inside it, a region table is one row, a related-queries set (top + rising) is one row, and the Trending Now board is one row per trending topic. You are billed per row written to the dataset, and only then.

A result that is not delivered is not charged, and it is never silently dropped either. Every one lands in the run's ERRORS record (key-value store) with a reason, a plain-language detail saying what happened and what to change, and a retryable flag:

reason	What happened	Re-run as-is?
no_data	Google answered with nothing measurable: an empty or all-zero timeline or region table, or empty related lists. The term sits below Google's volume floor for that window.	No — widen the time range, drop the region filter, or use a topic id
topics_gated	Google serves the related-topics feed only to sessions it classifies as regular users; it came back empty. Related queries are unaffected.	No
rate_limited	Google answered HTTP 429 and the retry budget is spent.	Yes, in a few minutes
blocked	The token step (/trends/api/explore) never issued widget tokens for this query.	Yes, in a few minutes
proxy_failed	Our proxy refused the request (HTTP 407). Nothing reached Google; this is on our side.	Yes
fetch_failed	A transport failure that survived one retry.	Yes
no_widget	Google's explore response carried no widget for this data type.	Yes
budget_cap	maxItems was reached before this result's turn.	Raise maxItems

The run's status line carries the same arithmetic, so a partial result is visible in your run list rather than something you discover in the dataset later:

Delivered 4 of 6 results. 2 not delivered and not charged: 1 no data from Google (below its volume floor), 1 rate-limited by Google — see the ERRORS record; 1 worth re-running in a few minutes.

A timeline with even one measurable week is a delivered result, because Google charted the term and found interest only in those weeks. Every timeline row carries measurablePeriods and totalPeriods, so a sparse timeline explains itself on the row and is never mistaken for a failed fetch.

A run that delivers nothing because of a block or a fault on our side is marked FAILED, with the reason in its status message, so it stands out in your run list and in our alerts. A run that delivers nothing because Google has no data for your terms is a SUCCEEDED run with an empty dataset and a full ERRORS record: nothing went wrong, and the record says exactly which term had nothing in that window.

🎯 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, with every miss on the record.

📥 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, 4 h–7 d window
maxItems	Hard cap on rows produced, and therefore on run cost; what it stops is reported as budget_cap

🔄 How it works

1. The actor opens a Trends session through built-in residential routing and performs the token handshake the web app uses.
2. For every term × location, one explore call issues per-widget API tokens. If that call fails, it is retried on a fresh session up to maxRequestRetries times.
3. The requested datasets are fetched with those tokens, one at a time with a short gap, in the order timeline → regions → related queries → related topics. A widget Google rate-limits is retried once; a whole run that keeps hitting 429s stops retrying rather than spending more of a quota that is already gone. Paying users also get a paid fallback network for rate-limited widget calls, capped per run.
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 delivered dataset is normalized and pushed as one row. Everything else is written to the ERRORS record, and the run's status line reconciles the two.

⚙️ 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 is self-describing in CSV exports.

Alongside the dataset, every run writes an ERRORS record to its default key-value store — an empty items list on a clean run, so your integration can read it unconditionally.

💵 What a run actually costs

You are billed one result per dataset, not one result per data point. A 5-year weekly timeline is 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).

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 bundled into a mandatory all-in-one result. maxItems puts a hard ceiling on top of that, and Apify's Maximum cost per run caps the spend in dollars.

At $0.003 per delivered result plus a $0.01 run start (the only fixed cost; charged once per run, including a run that delivers nothing):

Run	Rows	Cost
1 term × 1 country, 3 core datasets	3	~$0.019
3 terms × 2 countries, 3 core datasets	18	~$0.064
5 terms × 5 countries, 3 core datasets	75	~$0.235
Trending Now board, US, 24 h, top 100	100	~$0.31 — set maxItems to take just the top N

These are ceilings: a result Google has no data for, a rate-limited call, or a withheld topics feed costs $0. The Trending Now feed is deep (600+ topics for a large country in a 24 h window), so maxItems doubles as your spend cap — rows come back rank-ordered, so maxItems: 25 gives you the top 25.

📦 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],
    "measurablePeriods": 53,
    "totalPeriods": 53,
    "timeline": [
        { "time": "1757808000", "formattedTime": "Sep 14 – 20, 2025", "values": [53], "isPartial": false },
        { "time": "1789171200", "formattedTime": "Sep 13 – 19, 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"]
}

The ERRORS record, from a run that asked for a real term and a term Google has never charted:

{
    "schemaVersion": 1,
    "asked": 8,
    "delivered": 3,
    "notDelivered": 5,
    "byReason": { "no_data": 3, "topics_gated": 2 },
    "items": [
        {
            "keywords": ["xqzv9plmwq8kqq"],
            "geo": "US",
            "dataType": "interestOverTime",
            "reason": "no_data",
            "detail": "Google returned an empty timeline for this term in this window — it sits below Google's volume floor. Try a broader time range, drop the region filter, or use a topic id instead of free text.",
            "retryable": false,
            "charged": false
        }
    ]
}

🗂 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	The last point is usually partial — the current period is not finished yet
measurablePeriods / totalPeriods	How many periods carried interest, out of how many Google returned; a sparse term reads e.g. 4 / 53
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
ERRORS.items[].reason	Why a result was not delivered — see the table above; retryable says whether re-running as-is can help

⏰ Run it on a schedule

Save the input as a Task, put the Task on an Apify Schedule, and add an integration on run succeeded (a Google Sheet with one row per keyword per run is the usual shape). Google refreshes a 12-month timeline about weekly and the now windows about daily, so match the cadence to the window: weekly for today 12-m and longer, daily for today 1-m and now 7-d, hourly only for now 1-H / now 4-H and Trending Now. Have the integration read the ERRORS record too — the retryable entries are the ones worth queuing for a second pass.

❓ FAQ

Are the interest values absolute search counts? No. Google Trends 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.

Does a failed or empty run cost anything? No result charges. A run that delivers nothing bills only the $0.01 run start. Every result that was asked for and not delivered is in the ERRORS record, and charged is false on each one.

Why did my run end as FAILED? Because nothing was delivered and the cause was a block, a rate limit, or a fault on our side, and a green run with an empty dataset would have hidden that. The status message names the cause; the ERRORS record names every result. Re-run in a few minutes, and keep several terms in one run rather than launching one run per term — the latter is what trips Google's quota.

My term came back with no_data. Is the actor broken? No: Google charts nothing for a term below its volume floor, and it says so with a complete, empty answer. Try a broader time range, drop the region filter, or use the topic id (/m/...) instead of the free-text term — the same term often charts.

Why is relatedTopics off by default? Google serves the related-topics feed only to sessions it classifies as regular users and hands anonymous sessions an empty list; related queries are unaffected. Switch it on if you want the actor to try. When the list comes back empty, no row is written, nothing is charged, and the miss is recorded as topics_gated.

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 is 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 routing. The proxy input exists only as an explicit override.

What happens when Google rate-limits? A rate-limited widget call is retried once after a short wait; paying users also get a second network for those calls, capped per run. If Google keeps answering 429, the run stops retrying (a fresh IP is not fresh quota) and reports the affected results as rate_limited — not charged, marked retryable. 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.
• Google Autocomplete & Keyword Suggest Scraper — every suggestion Google completes for a seed, by country and language.
• 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, a compact input schema of plain booleans, and a machine-readable ERRORS record that tells the agent which results to retry and which to give up on. Call it from LLM tool-use / MCP setups to give your agent live search-interest data — "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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