Google Maps Scraper Alternative: Build a Local Lead List

Get every pizza place in a city, with phone, website and rating, as a lead table in one prompt. Paste this into your agent:

Set up https://monid.ai/SKILL.md, and then use Monid to pull the first two pages of pizza places in Austin, Texas from Google Maps, and give me a table with name, rating, review count, phone, website and address, sorted by review count.

One prompt builds a local list. 1 min setup · 20-40 hrs saved.

You want the businesses in a city or a category, with the phone number and the website, as a spreadsheet you can work through. The usual answers are a browser extension, a Python script or a marketplace scraper, and each one has a catch. This guide shows what to use instead of a Google Maps scraper, what a record contains (we ran it live on 2026-10-16), and the traps in a city-wide pull, such as the list being ordered by relevance and not by rating. It runs through Monid, the OpenRouter for agent tools: one key, one balance, every tool priced per call.

A note on who is talking: you are on the Monid blog, and Monid sells access to the endpoints below, including two Apify actors. The ones we ran are labelled, and the ones we did not run are labelled too.

What is the easiest alternative to a Google Maps scraper?

Monid. Install it once, add a key, and your agent searches Google Maps by keyword and place and gets typed rows back, with no extension to install, no proxy to rent and no vendor account to set up.

• Pay per call. A Maps search page, 20 places, is one call at $0.00015 (Verified 2026-10-16 on the local data tools page). Buy calls as you need them: no monthly plan and no minimum top-up. Discovery and inspection are free; only a run bills.
• One install, every API. The same key and balance call Google Maps, Yelp, TripAdvisor and everything else on Monid. When the lead list needs reviews or a second source, it is another call on the same balance.
• Lives inside your agent. Install the CLI, add your key, run the prompt above, and the tools are there whenever your agent needs them, with nothing to keep running. Your agent reads SKILL.md and learns the rest, so setup is about a minute.

At that price, 100 places is 5 calls, about $0.00075 (Verified 2026-10-16).

How do you build a local lead list from Google Maps?

Search by keyword and place, follow the next-page call the response hands you, then filter the rows. We ran this on 2026-10-16 with "pizza" in Austin, Texas: 2 calls returned 40 distinct places and cost $0.0003 (Verified 2026-10-16). The same sweep over many cities is the pattern in automating Google Maps business listings into a lead table.

For agents

Grab an API key at app.monid.ai, then paste this to your agent and hand it the key:

set up https://monid.ai/SKILL.md

It learns the whole discover, inspect, run workflow itself. More in the agent quickstart.

For humans

npm install -g @monid-ai/cli
monid keys add -k <your-api-key> -l main

Find the endpoints

monid discover -q "google maps business listings"

The search returns several Google Maps routes from different providers. They do different jobs, and the table later in this post sorts them.

Search a city

monid run -p litescrape -e /google/maps \
  --query '{"q":"pizza","type":"search","location":"Austin, Texas, United States","z":12}'

type is required for a query search, and location needs a zoom z or a radius m. The response holds 20 places in local_results and a pagination.next object with the endpoint and parameters for the next page already filled in. Send that object as it is, with start moved to 20, then 40, and so on.

Filter before you enrich

Drop rows you cannot use before you spend anything else on them: a missing phone or website, the wrong type, a place outside your territory (the coordinates are on every row). The lead list is what is left.

Or hand the whole job to an agent

Can you scrape Google Maps yourself with Python?

Yes, and people do it with Playwright, Selenium or a GitHub scraper, and with Chrome extensions for the no-code version. All of them work until Google changes the page. A scraper you run is yours to keep alive: the markup, the consent screens and the blocking all move, and your list is only as fresh as your last successful run.

There is a rules side too. Google restricts automated collection from Maps in its terms, and the rules on what you may do with the data differ by country and by use, so read yours before you build anything.

Doing it in Python means a browser or proxy setup, a parser for a page built to be read by people, scroll and pagination handling, retries, and a repair every time something changes. That is hours before the first useful row. A call through Monid costs $0.00015 (Verified 2026-10-16), the collection is maintained for you, and the output is typed JSON instead of HTML you parse. For a list you build once and never repeat, an extension is fine. For anything you will run again, Monid is the better fit. If you want to see where the DIY route stops paying, web scraping tools, and which kind fits compares the extension, script and API routes.

What does a Google Maps place record contain?

Two kinds of record, chained by place_id and data_id: the place row from a search, and the reviews of one place. We ran both on 2026-10-16, so the fields below are what came back, not what a spec promises.

The place row

A search row for Home Slice Pizza carried, grouped by what it is for:

identity     place_id, data_id, data_cid, provider_id
location     title, address, country, gps_coordinates
commercial   type, types, rating, reviews
contact      phone, website
hours        open_state, hours, operating_hours
links        reviews_link, photos_link, place_id_search, order_online
other        description, reserve_a_table, extensions, thumbnail

phone, website, description and reserve_a_table appeared on some rows and not others, which the next section counts. reviews_link is a ready-made call to the reviews endpoint for that place, and place_id_search resolves the one place again if you only have its id.

The reviews

The reviews endpoint takes the data_id from a row. We asked for 3, newest first, for the first place in the list. Each review had the star rating, a date in two forms (date as "a month ago" and iso_date), the date of the last edit, the number of likes, and a user object with the reviewer's name, profile link, how many reviews and photos they have, and a local_guide flag on one of the three. Sub-ratings for food, service and atmosphere came with two reviews. The response also carried place_info, the topics Google groups reviews under, and a next_page_token for more.

The same typed-row shape holds for travel places, as in the TripAdvisor hotel guide.

What you can build with it

• A prospect list for a local service. Search a category per city, keep the rows with a website, and you have a list ready for enrichment, .
• A territory map. Every row has latitude and longitude, so assigning places to a rep or a region needs no geocoding call.
• A review read on one competitor. Pull the newest reviews for a place, group them by star rating and topic, and read what changed this quarter.

What goes wrong when you pull a whole city from Google Maps?

Four things show up once you pull more than a page, and the same one-source-per-city trap is the subject of why one Google Maps scraper is not enough. All four came out of the Austin pizza run on 2026-10-16.

Why is the first result not the best business?

Because the order is Google's relevance order, not rating and not review count. Rank 1 in our run was a pizza place with a 5.0 rating on 23 reviews and no phone or website. Rank 3 was Home Slice Pizza with 4.7 on 11,157 reviews.

Sort on the field you mean. Rating and review count are on every row, so this costs nothing, and a rating on its own misleads: weight it by the count or set a minimum first.

rows.sort((a, b) => b.reviews - a.reviews);

Why are some phone numbers and websites missing?

Because Google does not hold them for every business. On the first page, 16 of 20 rows had a phone and 19 of 20 had a website. On the second page, 19 of 20 had both. Over the 40 places, 35 had a phone and 38 had a website.

The rows without a phone were not obscure: one had 210 reviews and no phone. Decide what a lead needs before you count the list. A website with no phone is a lead you reach by email or form, and a phone with no website is one you call.

Is the same business listed twice?

Not the same place, but the same name. "Via 313 Pizza" appeared twice on page 1, with two different place_id values, two addresses and two review counts (2,737 and 3,057). They are two locations of one chain. Between page 1 and page 2, no place_id repeated, so 40 places over 2 calls were 40 distinct places.

Key your store on place_id, never on the title. A name collapses a chain into one lead; the id keeps each location apart.

Where is the closed flag?

There is none in this response. Neither page returned a closed-status or an email field, so a closed business is not marked as one. The row has open_state and operating_hours, which describe today's hours, and the review dates tell you whether anyone has been there lately. Our reviews call showed the newest review of the first place was dated 2026-08-26, about seven weeks before the run. Treat the most recent review date as the signal of life and pull it with the reviews endpoint for the shortlist you keep.

How many calls is a city?

Each page is 20 places, so 100 places is 5 calls and 1,000 places is 50 calls, about $0.0075 at $0.00015 a call (Verified 2026-10-16). Each page is roughly 50 KB of JSON, so have the agent keep the columns it needs and drop the rest before it holds 50 pages in memory.

Which endpoint should I use for which job?

Figures from monid inspect on 2026-10-16 (Verified 2026-10-16; the current figure is always on monid.ai/tools): the Maps and reviews calls above are $0.00015 each. The Apify listings actor lists $0.003 per result, and the Apify reviews actor lists $0.0003 per result plus a flat fee. For a per-result endpoint the bill depends on how many results come back, so check the inspect output for the unit before you size a batch.

The two Apify rows start from different inputs: the listings actor takes a query and a place, and the reviews actor takes a place URL or ID. Pick on input shape and billing shape. If your agent also needs the Yelp side of a business, the Yelp business search guide has its record.

When is another route a better fit?

You need the licensed Google data. For anything customer-facing, or where a partner or platform reviews your sources, use Google's own offering and pay for it. A scraped place record is not licensed data.

You are building a maps product. Continuous, high-volume place data at the centre of a product is a licensing conversation, not a per-call one.

You need a closed flag or an email address in the row. The Maps response we ran has neither. Closed status needs another source, and an email needs a separate enrichment step.

You want a scheduler and a console. Run history, storage and an actor editor belong to the marketplace's own console, and Monid exposes the endpoint, not that product.

And the disclosure: Monid resells these endpoints, including the Apify ones. The most useful thing in this post is free whoever you buy from: sort and filter the rows yourself, and key on place_id.

Conclusion

The easiest alternative to a Google Maps scraper is a search call per page through Monid: $0.00015 a call (Verified 2026-10-16), 20 places per call, one key for Maps and everything else, about a minute to set up and ready inside your agent when you need it. Scraping it yourself works until the page changes, and costs hours before the first useful row.

Two things matter more than which endpoint you pick. The first result is not the best business: rank 1 in our run had 23 reviews and rank 3 had 11,157, so sort on the field you mean. And contact fields are patchy: 35 of 40 places had a phone and 38 of 40 a website, so decide what a lead needs before you count the list.

How we estimated the time saved: building and running your own Maps scraper is roughly 10 to 20 hours for getting past blocking, parsing rows and paging, and the first month of upkeep, and a reviews scraper is another 10 to 20. That is 20 to 40 hours. The 1 minute is the install, key and first call once you already have a Monid key.

Free next step: run monid inspect -p litescrape -e /google/maps, make one call for your own city, and print title, rating, reviews, phone and website. The gaps show up in the first page. Start at monid.ai.

FAQ

Published August 2026.