Google Trends Scraper & API - Reliable, Bulk, Trending Now

Get Google Trends data at scale without 429 errors. Scrape interest over time, interest by region (country, state, metro, city), related queries (top + rising, including Breakout), optional related topics, and Trending Now / daily trending searches for any country. Compare up to 5 terms, batch hundreds of keywords in one run, and pick any geo, time range, category or search property (Web, Images, News, YouTube, Google Shopping).

A reliable Google Trends API alternative (Google has no official public Trends API) and a maintained replacement for pytrends. Built for SEO and content research, market research, e-commerce product research, investors and data teams.

Already using apify/google-trends-scraper? Same input, all 14 fields: searchTerms, isMultiple, timeRange, geo, viewedFrom, skipDebugScreen, startUrls (including {url} / requestsFromUrl objects), spreadsheetId (public Google Sheet), category, maxItems, customTimeRange, maxConcurrency, maxRequestRetries, pageLoadTimeoutSecs. Change only the Actor ID. Success rate on our Actor was 0.97 (32/33) on Oct 19, 2026 vs Apify's 0.56 (Store stats that day). Set outputFormat to apify-compatible for one nested row per term.

• ✅ Reliability first - every request gets a warmed session (NID cookie), automatic session + IP rotation on HTTP 429, exponential backoff, a residential-proxy fallback and a second pass for anything that failed. 0 failed terms out of 413 in our cloud stress tests (see below).
• ✅ Optional related topics - available as an opt-in setting (off by default, see the FAQ for how they are retrieved).
• ✅ Everything Google Trends shows: timeline, regions, related queries, optional related topics, Trending Now with search volume, growth %, trend breakdown and news articles.
• ✅ Two output formats: flat rows (one row per data point - perfect for Excel, Google Sheets, BigQuery, Looker Studio) or one row per term with nested JSON (perfect for APIs and AI agents).
• ✅ Pay per term, not per row - a 5-year weekly timeline (260 points) costs the same as a 7-day one. Failed and no-data terms are free.
• ✅ Fast and cheap to run: ~1 second per term at the default concurrency on 256 MB memory.

Wrenfield Search Data: keyword and AI-search visibility data, priced per result

• Keyword data: Keyword Search Volume · Keyword Difficulty · Keyword Research · Autocomplete Expander · Google Trends · People Also Ask · Competitor Keywords · Keyword Gap
• AI search visibility: AI Search Visibility Tracker · Google AI Overview Scraper · AI Answer Scraper · Agency AI Visibility Reports
• Site and domain SEO: Domain Authority & Traffic · Bulk PageSpeed · SEO Toolkit · SEO Agency Report

What data can you get?

Data	Description
Interest over time	0-100 search interest per minute / hour / day / week / month, with isPartial for the last, incomplete point
Interest by region	Countries, subregions (states), US metros (DMA) or cities with lat/lng. Comparisons return each term's share (%) per region, like the Google Trends website
Related queries	Top and rising searches with value, +250% / Breakout, and a link
Related topics (opt-in)	Top and rising Knowledge Graph topics (ID, title, type). Off by default - enable includeRelatedTopics
Trending now	Daily trending searches per country for the past 4 h / 24 h / 48 h / 7 days: approximate search volume, growth %, start/end time, active flag, categories, the queries grouped into the trend and up to 3 news articles

How to use

1. Add search terms - one per line. To compare terms on one scale, put up to 5 on one line separated by commas: coffee, tea, matcha. You can also use topic IDs like /m/02vqfm.
2. Choose one or more geos (US, GB, US-CA, US-NY-501 or empty for worldwide), a time range (presets or custom dates) and optionally a category and search property.
3. Optionally add countries to Trending now.
4. Click Start and download the results as JSON, CSV, Excel or via the API.

Every line of search terms is scraped for every geo. For full control, use Advanced queries to give each query its own geo, time range, category and property.

Input example

{
    "searchTerms": ["coffee", "tea, matcha", "/m/02vqfm"],
    "geos": ["US", "GB", "WORLDWIDE"],
    "timeRange": "today 12-m",
    "category": 0,
    "property": "web",
    "includeInterestOverTime": true,
    "includeInterestByRegion": true,
    "regionResolution": "auto",
    "includeRelatedQueries": true,
    "includeRelatedTopics": false,
    "trendingGeos": ["US", "IN"],
    "trendingHours": "24",
    "maxTrendingPerGeo": 25,
    "outputFormat": "flat"
}

Time ranges: 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 (2004 - present), or custom 2024-01-01 2024-12-31, or hourly 2026-10-01T00 2026-10-07T23 (last 7 days, UTC).

Search properties: web, images, news, youtube, shopping.

Top-level categories: 0 All, 3 Arts & Entertainment, 47 Autos & Vehicles, 44 Beauty & Fitness, 22 Books & Literature, 12 Business & Industrial, 5 Computers & Electronics, 7 Finance, 71 Food & Drink, 8 Games, 45 Health, 65 Hobbies & Leisure, 11 Home & Garden, 13 Internet & Telecom, 958 Jobs & Education, 19 Law & Government, 16 News, 299 Online Communities, 14 People & Society, 66 Pets & Animals, 29 Real Estate, 533 Reference, 174 Science, 18 Shopping, 20 Sports, 67 Travel. Any Google Trends sub-category ID works too (it's the cat= value in a Google Trends URL).

Advanced queries example:

[
    { "terms": "nike, adidas", "geo": "DE", "timeRange": "today 5-y", "property": "youtube" },
    { "terms": "air fryer", "geo": "US-TX", "timeRange": "2024-11-01 2024-12-31", "category": 18 }
]

Output examples

Flat format (default) - every row has a type. Use the dataset views Interest over time, Interest by region, Related queries, Related topics (when enabled) and Trending now, or filter on type.

{"type": "interest_over_time", "searchTerm": "coffee", "comparedWith": [], "geo": "US", "timeRange": "today 12-m", "period": "2025-10-08 2026-10-08", "category": 0, "property": "web", "date": "2025-10-05", "timestamp": 1759622400, "formattedTime": "Oct 5 – 11, 2025", "value": 62, "hasData": true, "isPartial": false, "resolution": "WEEK", "exploreUrl": "https://trends.google.com/trends/explore?q=coffee&date=today%2012-m&geo=US"}
{"type": "interest_by_region", "searchTerm": "coffee", "geo": "US", "rank": 1, "geoCode": "US-WY", "geoName": "Wyoming", "value": 100, "valueType": "interest", "resolution": "REGION"}
{"type": "related_query", "searchTerm": "coffee", "geo": "US", "rankType": "top", "rank": 1, "query": "coffee near me", "value": 100, "formattedValue": "100", "isBreakout": false, "link": "https://trends.google.com/trends/explore?q=coffee+near+me&date=today+12-m&geo=US"}
{"type": "related_topic", "searchTerm": "coffee", "geo": "US", "rankType": "top", "rank": 1, "topicId": "/m/02vqfm", "topicTitle": "Coffee", "topicType": "Beverage", "value": 100, "formattedValue": "100", "isBreakout": false}

Trending now row:

{
    "type": "trending_now", "geo": "US", "rank": 1, "searchTerm": "susan dell",
    "searchVolume": 200000, "searchVolumeFormatted": "200K+", "increasePercentage": 1000,
    "startedAt": "2026-10-07T17:10:00Z", "endedAt": null, "isActive": true,
    "categories": ["Law and Government"],
    "trendBreakdown": ["susan dell", "trump accounts", "michael dell", "trump account for kids"],
    "newsArticles": [{"title": "Michael Dell defends Trump Accounts' shift to individual company stocks", "url": "https://finance.yahoo.com/...", "source": "Yahoo Finance", "publishedAt": "2026-10-07T21:34:51Z", "picture": "https://encrypted-tbn2.gstatic.com/..."}],
    "timeWindowHours": 24
}

Per-term format (outputFormat: "perTerm") - one row per term and geo (lists shortened; relatedTopics* are null unless includeRelatedTopics is enabled):

{
    "type": "term", "searchTerm": "tea", "comparedWith": ["matcha"], "geo": "US", "timeRange": "today 12-m",
    "period": "2025-10-08 2026-10-08", "category": 0, "property": "web", "status": "ok",
    "averageInterest": 80.42, "peakInterest": 100, "peakDate": "2026-04-12", "latestInterest": 70, "resolution": "WEEK",
    "interestOverTime": [{"date": "2025-10-05", "timestamp": 1759622400, "value": 70, "isPartial": false}],
    "interestByRegion": [{"geoCode": "US-SD", "geoName": "South Dakota", "value": 95, "formattedValue": "95%", "hasData": true}],
    "regionValueType": "share", "regionResolution": "REGION",
    "relatedQueriesTop": [{"rank": 1, "query": "green tea", "value": 100, "formattedValue": "100", "isBreakout": false}],
    "relatedQueriesRising": [{"rank": 1, "query": "how to make cold brew tea", "value": 2050, "formattedValue": "+2,050%", "isBreakout": false}],
    "relatedTopicsTop": [{"rank": 1, "topicId": "/m/07clx", "topicTitle": "Tea", "topicType": "Beverage", "value": 100, "formattedValue": "100"}],
    "relatedTopicsRising": [{"rank": 1, "topicId": "/g/11y0w7h_p9", "topicTitle": "The Loaded Teashop", "topicType": "Topic", "value": 32100, "formattedValue": "Breakout", "isBreakout": true}],
    "error": null,
    "exploreUrl": "https://trends.google.com/trends/explore?q=tea%2Cmatcha&date=today%2012-m&geo=US"
}

Terms without enough search volume return a single status row (no_data, free). Terms that fail after all retries return status: error (free). A run summary with success rate and proxy statistics is saved to the key-value store as SUMMARY.

Reliability - how it works and how we test it

Google Trends rate-limits aggressively (HTTP 429), redirects suspicious traffic to a captcha page and requires a session cookie plus per-widget tokens. This Actor:

1. Warms every session with a tiny request that sets Google's NID cookie, then calls the explore endpoint for widget tokens and the widget data endpoints (timeline, regions, related searches).
2. Rotates on any block - 429, 403, captcha redirect, timeout or HTML instead of JSON drops the session and retries on a new IP with a new cookie, with exponential backoff and jitter.
3. Datacenter first, residential fallback - starts on cheap datacenter IPs and switches a request (or the whole run, via a circuit breaker) to residential IPs when datacenter IPs are blocked.
4. Isolates errors per term and per section - one failed term or section never breaks the rest; failed queries get a second pass with fresh sessions at the end of the run.

Measured on the Apify platform (October 2026), default proxy settings (datacenter first, residential fallback), concurrency 5-10: 5 stress runs, 413 terms across 15+ countries, worldwide, US states and metros, 10 time ranges (past 4 hours to 2004-present, custom dates), all 5 search properties and comparisons of up to 5 terms: 391 terms returned data, 22 genuinely low-volume terms returned no data, 0 failed (100% completion), plus 225 Trending Now rows from 6 countries. Typical speed at the default concurrency of 5: 116 terms in 104 seconds; 55 terms + 150 trending searches in 76 seconds.

Pricing

This Actor uses pay-per-event pricing – you pay per search term analysed and per trending search returned.

Event	Price
Search term analysed (term-result)	$3.00 per 1,000 terms ($0.003 per term)
Trending search (trending-search)	$0.0005 per row

Terms with no data and failed terms are free. Platform usage (compute) is included. Set Maximum cost per run in the run options to cap your spend – the Actor stops cleanly at the limit and keeps everything scraped so far.

Related tools

Trends shows relative interest; Keyword Search Volume gives absolute monthly searches for the same terms.

• Keyword Search Volume (Google Keyword Planner): search volume, CPC and keyword ideas in bulk
• AI Search Visibility Tracker: check if ChatGPT, Perplexity, Gemini and AI Overviews mention your brand
• YouTube Transcript Scraper: transcripts and subtitles from videos, channels and searches
• Keyword Research Tool: keyword ideas with volume, KD, intent and SERP features
• Google Autocomplete Keyword Expander: long-tail and question keywords from Google suggestions
• People Also Ask Scraper: Google PAA questions, related searches and AI Overviews

All of these are maintained by the same developer and export flat JSON, CSV or Excel, so you can feed one tool's dataset into the next.

Google Trends API: REST, Python, JavaScript, n8n and MCP

Actor ID for every client: wrenfield-data/google-trends-scraper (in URLs: wrenfield-data~google-trends-scraper). You need an Apify API token; send it in an Authorization: Bearer header. Every example below sets maxTotalChargeUsd, a hard spend cap for the run.

One request, rows back (runs up to 5 minutes):

curl -X POST "https://api.apify.com/v2/acts/wrenfield-data~google-trends-scraper/run-sync-get-dataset-items?maxTotalChargeUsd=5" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["crm software", "email marketing"], "geos": ["US"], "timeRange": "today 12-m", "outputFormat": "perTerm"}'

Python (pip install apify-client):

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("wrenfield-data/google-trends-scraper").call(
    run_input={'searchTerms': ['crm software', 'email marketing'], 'geos': ['US'], 'timeRange': 'today 12-m', 'outputFormat': 'perTerm'},
    max_total_charge_usd=5,
)
for row in client.dataset(run.default_dataset_id).iterate_items():  # apify-client 1.x/2.x: run["defaultDatasetId"]
    print(row.get("searchTerm"), row.get("geo"), row.get("averageInterest"))

JavaScript / TypeScript (npm i apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('wrenfield-data/google-trends-scraper').call(
    {"searchTerms": ["crm software", "email marketing"], "geos": ["US"], "timeRange": "today 12-m", "outputFormat": "perTerm"},
    { maxTotalChargeUsd: 5 },
);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

Long or scheduled jobs (async + webhook): start with POST https://api.apify.com/v2/acts/wrenfield-data~google-trends-scraper/runs?maxTotalChargeUsd=5 (returns at once with data.id and data.defaultDatasetId), wait with GET /v2/actor-runs/{id}?waitForFinish=60, then read GET /v2/datasets/{defaultDatasetId}/items?clean=true&format=json (or csv, xlsx). To be called back instead of polling, add &webhooks= with the base64 of [{"eventTypes":["ACTOR.RUN.SUCCEEDED"],"requestUrl":"https://your-app.example/hook"}]. For recurring pulls, save the input as a task and put it on an Apify schedule.

n8n: Apify node → Resource Actors → Run an Actor and Get Dataset → Actor wrenfield-data/google-trends-scraper → custom input {"searchTerms": ["crm software"], "geos": ["US"], "timeRange": "today 12-m", "outputFormat": "perTerm"}. Make and Zapier have the same "run Actor" + "get dataset items" steps. Or call the run-sync-get-dataset-items URL above from any HTTP node.

MCP (Claude, Cursor, VS Code, any MCP client): https://mcp.apify.com?tools=wrenfield-data/google-trends-scraper. The tool exposes the input schema and output fields to the agent.

Output fields

Main fields (the Output tab and the dataset schema list every field with its type):

Field	Type	Meaning
type	string	Row type: interest_over_time, interest_by_region, related_query, related_topic, trending_now, term (per-term format) or status (no data / error, free)
searchTerm	string or null	Search term (or trending search)
geo	string or null	Geo code (WORLDWIDE for worldwide)
timeRange	string or null	Requested time range
date	string or null	Timeline point (YYYY-MM-DD, or ISO datetime for hourly data, UTC) (flat format)
value	integer or null	Interest 0-100 (region rows of comparisons: share %); related rows: relative value or % growth (flat format)
isPartial	boolean or null	Last timeline point is still being collected (flat format)
geoCode	string or null	Region code (e.g. US-CA, DMA 807) (flat format)
geoName	string or null	Region name (flat format)
rankType	string or null	top or rising (flat format)
query	string or null	Related query (flat format)
isBreakout	boolean or null	Rising more than 5000% (flat format)
averageInterest	number or null	Per-term format: mean interest
peakInterest	integer or null	Per-term: max interest
latestInterest	integer or null	Per-term: last value
interestOverTime	array or null	Per-term: timeline
status	string or null	ok, partial, no_data or error
exploreUrl	string or null	Open the same query on trends.google.com
scrapedAt	string or null	UTC timestamp

Field names and types stay stable within version 1.x: we add fields but don't rename or remove them. Anything that changes a field will be announced in this README first. Optional fields can be null or absent depending on the options you set, so read them with a default.

Cost per 1,000 results

Volume	Cost (free plan prices)	How it adds up
1,000 search terms (any number of rows each)	$3.00	$0.003 per term × geo; timeline, regions and related queries included
1,000 Trending Now rows	$0.50	$0.0005 per trending search

Terms with no data and failed terms are free. Platform usage is included. Paid Apify plans pay less (down to $1.95 per 1,000 terms).

Pipeline tips

• "outputFormat": "flat" (default) gives one row per timeline point, region or related query with a type column: best for SQL and spreadsheets. "perTerm" gives one row per term + geo with arrays inside: best for code.
• Up to 5 terms on one line ("coffee, tea") share one 0-100 scale; separate lines are scraped independently.
• The last timeline point is often still being collected: check isPartial before alerting on a drop.

FAQ

Is there an official Google Trends API? Google offers a limited alpha API only to selected partners. This Actor gives you the same data the Google Trends website shows, as JSON/CSV, via the Apify API, schedules and integrations.

Why does my Google Trends scraper / pytrends keep getting 429 errors? Google limits requests per IP and per session and wants a valid cookie. This Actor rotates sessions and IPs automatically, backs off exponentially and falls back to residential IPs, so you don't have to.

Why are related topics off by default? Google returns an empty related-topics list to regular automated sessions. When you enable includeRelatedTopics, the Actor requests related topics through Google Trends' embeddable-widget endpoint and sends a Referer header from a pool of public websites. Turn it on only if that method is acceptable for your use case. It adds 2 requests per term and doesn't change the price.

What do the 0-100 values mean? Google Trends values are relative: 100 is the peak popularity for the term (or the highest of the compared terms) in the chosen geo and period. Compare terms in one line (up to 5) to put them on the same scale. Values for different queries or runs are not directly comparable, and Google samples data, so low-volume values can vary slightly between requests.

How many keywords can I scrape? As many as you like - add hundreds of lines; they are processed in parallel and each is billed once per geo.

Can I get daily trending searches? Yes - add country codes to Trending now (Google replaced the old Daily Search Trends page with Trending Now). Choose the past 4 hours, 24 hours, 48 hours or 7 days, filter by category and get news articles for each trend.

Can I use it from Python, Make, Zapier, n8n or an AI agent? Yes - call the Actor through the Apify API, the Python/JavaScript client, the Apify MCP server or any Apify integration, and read the dataset items. Use outputFormat: "perTerm" for compact JSON.

Is scraping Google Trends legal? The Actor collects publicly available, aggregated and anonymized statistics. No personal data is collected. Check the terms that apply to your use case.

Feedback and support

Missing a field, hit an error, or need another country or option? Open an issue on the Issues tab and include the run ID. We read every issue and reply there.

Acceptable use

• This Actor is an independent tool. It is not affiliated with, endorsed by or sponsored by Google. Google and Google Trends are trademarks of Google LLC.
• Use the data in line with the terms of the sources and the laws that apply to you.