Google Keywords Suggest Scraper Pro: 120 of 122 successful runs (2026)

Each record carries 11 fields, including the suggestion text, original seed, expansion query, and Google's relevance score. 120 of 122 public runs in the last 30 days finished successfully, making this Actor a solid tool for SEO teams, content strategists, and PPC specialists who need real-time autocomplete data across hundreds of regional Google markets. It is built specifically for phrase expansions; it is not for marketers needing search volume, CPC, or historical click metrics, which Google Suggest does not provide.

How reliable is Google Keywords Suggest Scraper Pro in production?

Across the last 30 days of public runs on the Apify platform, Google Keywords Suggest Scraper Pro recorded 122 runs with the following outcomes.

Outcome	Runs	Share
Succeeded	120	98.4%
Failed	1	0.8%
Aborted by the user	1	0.8%
Timed out	0	0.0%
Total	122	100.0%

With a 98.4% success rate over recent operational cycles, this Actor handles scheduled runs reliably. Expect about 1 run in a hundred to fail or abort. Set automated retry rules for failed jobs and keep seed lists manageable to minimize recovery overhead.

What does it cost to run Google Keywords Suggest Scraper Pro?

Each result costs $0.002 on the free tier, which is $2.00 per 1,000 results. Starting a run is charged separately at $0.005 per GB of Actor memory. Apify also bills the platform usage each run consumes, at the rates of your Apify plan, on top of these charges.

Apify plan	Per result	Per 1,000 results
FREE	$0.002	$2.00
BRONZE	$0.00167	$1.67
SILVER	$0.00133	$1.33
GOLD	$0.001	$1.00
PLATINUM	$0.001	$1.00
DIAMOND	$0.001	$1.00

Worked example: collecting 10,000 results costs $20.00 in result charges before run-start fees and platform usage. At the observed 0.8% failure rate, budget for re-running a portion of those batches rather than assuming every run completes.

At $0.002 per result, pricing is driven entirely by total suggestions emitted. Setting mode to 'alphabet' expands every seed into 26 distinct queries, raising result counts faster than 'exact' or 'questions'. Validate seed inputs using low maxItemsPerKeyword caps to preview output structures before running large batches.

How do you run Google Keywords Suggest Scraper Pro from the API?

The schema marks 1 of its 9 controls as required: keywords. Every value in the payload below comes from the published schema's own prefills, which means you can paste it, swap the token, and get a real result.

Call the synchronous endpoint to start a run and receive dataset items in one request:

curl -X POST "https://api.apify.com/v2/acts/crawlerbros~google-keywords-suggest-scraper-pro/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords":["python tutorial"],"mode":"all","country":"US","language":"en","maxItemsPerKeyword":200,"outputFormat":"flat"}'

The same run from Python, using the official client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
  "keywords": [
    "python tutorial"
  ],
  "mode": "all",
  "country": "US",
  "language": "en",
  "maxItemsPerKeyword": 200,
  "outputFormat": "flat"
}

run = client.actor("crawlerbros~google-keywords-suggest-scraper-pro").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

And from Node.js:

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' })

const input = {
  "keywords": [
    "python tutorial"
  ],
  "mode": "all",
  "country": "US",
  "language": "en",
  "maxItemsPerKeyword": 200,
  "outputFormat": "flat"
}

const run = await client.actor('crawlerbros~google-keywords-suggest-scraper-pro').call(input)
const { items } = await client.dataset(run.defaultDatasetId).listItems()
console.log(items)

That endpoint blocks until the run completes. Fine while you are testing a handful of records, risky once a run takes minutes: a dropped connection loses the response even though the run itself finished. Switch to an asynchronous start with polling or a webhook before you schedule anything.

Which Google Keywords Suggest Scraper Pro inputs matter, and which can you skip?

The keywords array is the only required input control. Selecting the right mode directly governs result volume, as 'alphabet' queries every letter while 'exact' returns simple suggestions. Most initial runs should stick to default country and language settings unless localized regional ranking is specifically required.

keywords (array): One or more seed keywords. Each is expanded according to mode. Default: ["python tutorial"].
mode (string): How each seed keyword is expanded. exact = no expansion. all = Google's related suggestions. questions = adds who/what/when/where/why/how prefixes. prepositions = adds for/with/vs/like/near. comparisons = adds vs/or/versus. alphabet = appends each letter a-z to surface long-tail. Default: "all".
country (string): Two-letter Google country code (e.g. US, GB, IN, DE, BR). Affects suggestion ranking by region. Default: "US".
language (string): Two-letter language code (e.g. en, es, fr, de). Default: "en".
maxItemsPerKeyword (integer): Hard cap on how many suggestions are emitted per seed keyword (across the whole expansion). Default: 200.
minLength (integer): Drop suggestions shorter than this many characters.
maxLength (integer): Drop suggestions longer than this many characters.
containsKeyword (string): Only emit suggestions that contain this substring (case-insensitive). Prefix with ! to invert (drop matches).
outputFormat (string): flat (one record per suggestion) or tree (one record per seed with nested suggestion list). Default: "flat".

Fixed-choice controls: mode accepts exact (Exact (no expansion)), all (All (Google's default related)), questions (Questions (who / what / why / how)), prepositions (Prepositions (for / with / vs / like)), comparisons (Comparisons (vs / or / versus)), alphabet (Alphabet (a-z long-tail)); country accepts 135 values, including US, GB, CA, AU (default US); language accepts 78 values, including en, es, fr, de (default en); outputFormat accepts flat (Flat (one record per suggestion)), tree (Tree (one record per seed)).

What does Google Keywords Suggest Scraper Pro return?

Returned data is ideal for discovering user search intent, grouping FAQ topics, and finding long-tail modifiers across distinct regions. The records do not contain monthly search volume, keyword difficulty, or advertising costs. If your workflow depends on hard search metrics, you must combine this dataset with external reporting sources.

Output (flat mode - one record per suggestion)

recordType (e.g. suggestion)
text (e.g. python tutorial for beginners)
source_keyword (e.g. python tutorial)
expansion_query (e.g. python tutorial f)
mode (e.g. alphabet)
country (e.g. US)
language (e.g. en)
relevance (e.g. 601)
suggestion_type (e.g. QUERY)
sub_types
scrapedAt

Output (tree mode - one record per seed)

recordType
source_keyword
mode
country
language
suggestion_count
suggestions

These are the documented fields. Optional ones can be empty on a given record, so measure how often each field your deliverable depends on is populated across a real sample before automating the handoff.

How do you build the workflow end to end?

Open Google Keywords Suggest Scraper Pro and work through these in order. Each step ends with something to check, so a bad configuration surfaces on a small run rather than a scheduled one.

Populate keywords with a single seed string to inspect base outputs before running full keyword lists.
Select mode based on intent, choosing 'alphabet' for exhaustive long-tail coverage or 'questions' for informational queries.
Set country to a specific Google country code like 'US' or 'GB' and set language to the matching primary locale like 'en' to align geographic autocomplete ranking.
Apply maxItemsPerKeyword to cap output volume while testing, leaving default limits active until you verify keyword patterns.
Configure outputFormat to 'flat' for tabular datasets or 'tree' to keep nested groups keyed directly to your initial seeds.
Run the Actor and verify that returned records contain expected strings in text and valid numbers in relevance.

How do you apply it? Three worked playbooks

These are Google Keywords Suggest Scraper Pro's own documented use cases, each worked through as an operating pattern rather than a description.

Use case 1: SEO research

Outcome: find the long-tail variants real users search for

Configure: keywords = ["crm software"], mode = "alphabet", country = "US", language = "en", outputFormat = "flat", maxItemsPerKeyword = 500

Working method: Execute an initial alphabet expansion on a single core phrase, then filter for high relevance scores. Standardize query strings across returned records to isolate repeated search modifiers and export the resulting flat records into your primary keyword management tool.

Deliverable: A flat CSV dataset listing long-tail variations, their generating queries, and relevance scores.

Stop condition: Zero new suggestions returned across 5 consecutive letter expansions.

Use case 2: Content planning

Outcome: group suggestions by question modes to map "people also ask" topics

Configure: keywords = ["data warehouse", "data lake"], mode = "questions", country = "US", language = "en", outputFormat = "tree", maxItemsPerKeyword = 200

Working method: Run query sets with questions mode across seed topics to cluster common interrogative prefixes. Group outputs by who, what, why, and how patterns to identify core search intent clusters, mapping each question set directly to specific article sections.

Deliverable: A structured JSON tree file grouping question-based keyword arrays under each original seed term.

Stop condition: Output datasets containing fewer than 3 unique question prefixes per seed keyword.

Use case 3: PPC research

Outcome: discover negative-match candidates with containsKeyword: "!free"

Configure: keywords = ["accounting software"], mode = "all", containsKeyword = "!free", country = "US", language = "en", outputFormat = "flat"

Working method: Run the initial seed with negated substring criteria to prune irrelevant budget searches. Inspect the returned suggest phrases to isolate non-buying intent modifiers and construct precise negative keyword lists for immediate campaign import.

Deliverable: A cleaned export of autocomplete suggestions filtered to exclude low-intent terms.

Stop condition: More than 5% of returned records contain the excluded substring.

What breaks, and how do you design around it?

Over the last 30 days, 0.8% of public runs failed and 0.0% timed out. Build retries and alerting around those rates rather than assuming every run completes.

When hitting the hard cap of maxItemsPerKeyword, break large seed batches across separate runs rather than increasing caps on a single run. If Google throttle responses occur during intensive alphabet expansions, introduce brief delays between automated execution calls. For multi-country research, run separate jobs per target market rather than passing combined global queries.

When should you not use Google Keywords Suggest Scraper Pro?

Do not use this Actor if your project requires actual search engine results pages, organic SERP ranks, featured snippets, or paid ad placements. Autocomplete suggestions only reflect search bar predictions, not live web listings. If you need full page scrape capabilities including meta titles, URLs, and People Also Ask boxes, use Google Search Results Scraper instead. Similarly, if you require historical monthly search volumes or estimated pay-per-click bidding costs, build your pipeline directly against the official Google Ads API or search tools like Ubersuggest Keyword Scraper.

What should you check before trusting the output?

Alert when text returns empty or null across flat mode outputs.
Fail the pipeline if relevance is missing, which indicates unexpected response structures from Google Suggest.
Monitor country and language fields in output items to confirm geographic targeting matched input parameters.
Flag datasets where expansion_query fails to vary when mode is set to 'alphabet' or 'questions'.
Stop runs immediately if consecutive outputs yield zero results due to overly restrictive containsKeyword patterns.

None of this proves a record is correct. It gives a scheduled Google Keywords Suggest Scraper Pro run defined points where it should stop instead of quietly passing bad data downstream.

Frequently asked questions

How much does running this Actor cost?

Each result costs $0.002 on the free tier, which equals $2.00 per 1,000 results. There is also a run-start charge of $0.005 per GB of Actor memory applied to every run that starts.

How reliable is this scraper for automated schedules?

Telemetry shows 120 of 122 public runs in the last 30 days finished successfully, yielding a 98.4% success rate. Only 1 run failed and 1 was aborted by users.

Does this Actor provide search volume or CPC data?

No. Google Suggest only exposes autocomplete text and internal relevance scores. To access search volumes or CPC estimates, you must integrate official Ads APIs or dedicated volume estimation tools.

What is the difference between flat and tree output formats?

Flat format outputs one dataset record per autocomplete suggestion, making it easy to export to CSV. Tree format outputs one record per seed keyword with all suggestions nested inside an array.

How does the alphabet mode expand my seed keyword?

Alphabet mode appends each letter from a to z to your seed term, sending 26 individual requests to Google Suggest to extract comprehensive long-tail variants.

Where to go next

Start with the Google Keywords Suggest Scraper Pro Actor page for the current input schema, pricing tier, and run history.

It is part of the Google Scraping Suite, which puts every related Actor on one page with its price and run history.

Readers running Google Keywords Suggest Scraper Pro commonly pair it with:

Google Search Results Scraper: Scrape Google Search result pages (SERPs) and extract structured data: organic results, paid ads, related queries, and People Also Ask.
Google Scholar Scraper: Scrape academic papers, articles, and citations from Google Scholar.
Google Ads Transparency Center Scraper: Scrape live and recent ads shown through Google - search by advertiser name, domain, or exact advertiser ID.
Google Images Scraper: Extract image data from Google Images search.
Google Earth Scraper: Advanced Google Earth Scraper.
Ubersuggest Keyword Scraper: Scrape keyword suggestions from Ubersuggest / Neil Patel.
Amazon Keyword Suggestions Scraper: Discover Amazon's search-autocomplete keyword suggestions for any seed keyword, across 23 marketplaces.
Trustpilot Reviews Scraper Pro: Scrape Trustpilot business reviews, ratings, replies, and rating distributions across any business domain.

Related guides:

Resources

Actor documentation, input schema, and pricing: verified against the published Actor on 2026-09-23.
Actor last updated by its maintainers on 2026-05-03.
Run outcome figures cover the 30 day public window ending 2026-09-23.
Google Keywords Suggest Scraper Pro on Apify