Google Maps Scraper Alternatives: Building a Local Lead List

Someone wants every pizza place in a city, with a phone number and a website, and they want it as a spreadsheet by lunchtime. It is one of the oldest jobs in lead generation and the tooling around it is genuinely good, which is why the question people actually ask is not how to do it but what to use instead of the thing they tried first.

This guide answers that with runs rather than opinions. Monid is the OpenRouter for agent tools, so the actors below are reachable on one balance and we can show what each returns.

Fair disclosure: you are on the Monid blog, and Apify is a provider in our catalogue. One of the two actors measured here returned nothing on a reasonable query, and that is in the post because it is the most useful thing in it.

What are the alternatives for the Google Maps scraper on Apify?

Mostly other Google Maps scrapers on Apify, and the useful discovery is that they are not interchangeable.

We searched the catalogue on 2026-08-19 for the job rather than the vendor:

monid discover -q "google maps business listings reviews"

Two Apify actors came back, plus three endpoints that are not Google Maps at all:

apify /compass/google-maps-reviews-scraper   reviews and place metadata
apify /damilo/google-maps-scraper            local business listings
trustpilot /get_company_reviews              a different review source
clutch /get_company_reviews                  agency and B2B reviews
loopnet /search_listings                     commercial real estate

The two Apify rows do different jobs. One starts from a place and returns its reviews; the other starts from a search and returns places. Asking which is the better alternative to the other is the wrong comparison, and it is the comparison the question invites.

The three non-Apify rows matter for a different reason. If the underlying need is "reviews about a business" rather than "reviews on Google", then Trustpilot and Clutch are separate sources with separate coverage, and a Google-only pull is answering a narrower question than the one that was asked.

Why did my scraper return an empty list?

Because a 200 is not a promise of data, and we hit this on the first try.

We ran the listings actor with a query any human would call reasonable:

monid run -p apify -e /damilo/google-maps-scraper \
  -i '{"query":"restaurant","location":"San Francisco, CA, USA"}' -w

Provider Response: 200
Output:            []
Cost:              nothing

Two hundred, empty array, no charge. The values we passed were the actor's own documented example values, so this was not a nonsense query. Something in that combination produced no rows, and the response says nothing about why.

Three things to take from it:

Check the body, not the status. A pipeline that branches on response.ok treats this run as a success and writes an empty batch downstream. That failure is silent and it compounds: the next stage does its work correctly on nothing.

Per-result billing means an empty result is free. No records, no charge, which is why probing is cheap. On a per-call endpoint the same empty response costs full price, and the billing shape is in inspect before you run.

An empty result is not proof the actor is broken. It is one query on one day. The correct next step is a different input shape or the other actor, not a conclusion about quality, and this is exactly why keeping a second endpoint reachable matters.

The reviews actor, run immediately afterwards on the same day with a place URL, returned real data. Same platform, same subject, different job, different outcome.

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-key> -l main

What does a place record actually tell you about a business?

More than a name and a phone number, and the extra fields are the ones that decide whether a lead is worth calling.

The reviews actor returned roughly fifty fields per record on 2026-08-19. Grouped by what they are for:

identity     placeId, cid, fid, kgmid, businessProfileId
location     title, address, street, city, state, postalCode, countryCode,
             neighborhood, lat, lng
commercial   categories, price, hotelStars, totalScore, reviewsCount
status       permanentlyClosed, temporarilyClosed
review       reviewId, text, textTranslated, stars, publishedAtDate,
             likesCount, reviewImageUrls, reviewDetailedRating, visitedIn
reviewer     reviewerId, reviewerUrl, reviewerNumberOfReviews, isLocalGuide
owner        responseFromOwnerText, responseFromOwnerDate

Four of those are worth more than the rest for a lead list, and none of them is the phone number:

permanentlyClosed and temporarilyClosed. A list that includes closed businesses is worse than a shorter list, because somebody spends time on every row. This is the cheapest filter available and most exports ignore it.

responseFromOwnerText. Whether anybody replies to reviews. An owner who answers is an owner who logs in, which for anything sold to small businesses is a better qualifier than the rating.

reviewsCount with publishedAtDate. Volume and recency together. Forty reviews with the newest from two years ago describes a different business from forty with the newest from last week, and the count alone cannot tell them apart.

lat and lng. Territory assignment without a geocoding step, which is a whole second API call you do not have to make.

The field that flatters and misleads: totalScore on its own. A 4.9 from six reviews and a 4.3 from four hundred are not comparable, and sorting a lead list by rating puts the six-review business on top.

📖 See also why one Google Maps scraper is not enough, which compares actors on review coverage specifically, and automating Google Maps business listings into a lead table for the scheduled version of this job.

How do you turn a place list into a lead list?

By adding the two things Google Maps does not carry: a person and a verified way to reach them.

A place record gives you a business. A lead needs a human, and the gap between those is where most local lead lists quietly fail. Four steps, and only the first is a Maps job:

Pull the places. Search plus location, filtered by category and by the status fields above. Drop the closed ones before anything else touches the list, because every later step costs something per row.

Resolve the business to a company record. The website from the place record is the join key. akta /v1/company/search resolves a name or domain at no cost per call, which makes it safe to run across the whole list before you spend on anything. We covered the ambiguity cases in finding a company's website from its name.

Find a person. Local businesses are small enough that the owner is usually the buyer, and a company employee search returns the roles that exist rather than the roles you hoped for. The LinkedIn scraper guide compares the endpoints that do this.

Verify the address before sending. A local lead list is exactly the shape that produces bounces: small businesses, catch-all mailboxes, addresses that have not been used in years. Verifying an email before it hits your list covers the check, and skipping it is what damages a sending domain.

The rule that keeps this affordable: filter before you enrich, always. Every step after the Maps pull costs something per row, so the closed businesses, the wrong categories and the out-of-territory rows should be gone before the first enrichment call. A thousand places filtered to two hundred and then enriched costs a fraction of a thousand enriched and then filtered.

The four filters that pay for themselves

Applied in this order, because each one is cheaper than the one after it and removes rows the next would have charged for:

Status, first and free. Drop permanentlyClosed and flag temporarilyClosed for review. It is a field you already have and it removes rows nobody could sell to. On a city-wide pull this is routinely a tenth of the list.

Category, second. The categories array is more specific than the search term that produced it, so a query for restaurant returns caterers, food trucks and hotel dining rooms alongside the thing you meant. Filter on the array rather than trusting the query.

Territory, third. lat and lng are in the record, so a bounding box or a radius runs locally with no geocoding call. Doing this before enrichment rather than after is the difference between paying for the rows you keep and paying for every row the search returned.

Signal of life, last. Review recency and owner replies. This is the one that needs judgement rather than a rule, and it is worth spending the judgement: a business with no review in three years and no owner reply is technically open and practically unreachable.

What survives all four is a smaller list than the one you started with and a materially better one, and the whole sequence runs on fields the Maps pull already returned. Nothing here costs an extra call.

Which endpoint should I use for which job?

Verified present on 2026-08-19 with monid discover. The billing column gives the shape rather than a figure; monid inspect prints the current figure for free.

The row people skip is the last one. A local business website carries the owner's name, the services list and often a direct email, and reading one page costs less than most enrichment calls. For a list of two hundred local businesses it is frequently the highest-yield step in the whole pipeline.

When should you not use Monid?

You need Google's own Places API. For anything customer-facing, or where the terms matter to a partner or a platform review, use the official API 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 the Apify console. Scheduling, run history, storage and the actor editor are their product. We expose the endpoint.

Your territory is outside the actors' coverage. Coverage varies by country and by language, and neither actor documents it usefully. Run your own city before assuming.

And the caution about us. One of the two actors here returned an empty array on the actor's own documented example values, with a 200 and no charge. That is the failure mode to design around on any endpoint in this catalogue: read the body, not the status, and run your real query before sizing a batch.

Conclusion

The alternatives to the Apify Google Maps scraper are mostly other Google Maps scrapers, and the more useful reframing is that the two obvious ones do different jobs. One starts from a place and returns reviews; the other starts from a search and returns places, and choosing between them on reputation rather than on input shape is how people end up with an empty result.

Two things matter more than which actor you pick. A 200 with an empty array is the failure that costs you most, because it passes every status check and writes an empty batch into whatever comes next, and we hit it on our first run using the actor's own example values. And the fields that qualify a lead are not the contact fields: closed status, owner replies and review recency decide whether a row is worth a call, and every export that sorts by star rating gets this backwards.

Start with the free part: discovery and inspection cost nothing, and on per-result endpoints an empty result costs nothing either, so probing your actual city is close to free. Run it before you plan the batch. Begin at monid.ai.

FAQ

Last updated August 2026.