Best Google Maps Scrapers 2026 [No-Code Edition]

I tested the top no-code Google Maps scrapers on the things that actually decide a purchase... data, usability, speed, cost, and scale. Here are the 3 that delivered, and the 2 that didn't.

⚡ 30-Second Summary

1. I ran 5 no-code Google Maps scrapers through the same job: scrape Chinese restaurants in zip 10003 New York, then push them to full data. I scored them on data, usability, speed, cost, and scalability... the 5 things people actually fight with.
2. lobstr.io is my #1. Cleanest, most accurate output (93% of fields filled on every row, tightest geo precision, zero duplicates), cheapest at scale ($0.50/1K), and the only tool that scales horizontally via Slots. Trade-off: full-data speed is slow (~4 listings/min) before you add Slots.
3. Apify is the fastest on full data (~30/min) and the only one returning full reviews + live popular times. Trade-off: priciest of the bunch, and its location handling pulled results from the wrong part of the city in my test.
4. Outscraper returns the most fields per business (53 base, 75 full) and its filters are free. Trade-off: no built-in scheduling and the lowest throughput ceiling.
5. Bright Data and PhantomBuster didn't make the cut. I'll show you exactly why near the end.

Google Maps has more scrapers built for it than almost any other site.

That makes choosing harder, not easier.

Because the tools vary wildly in what they return, what they cost, and how far they scale before Google's natural ceiling kicks in.

Most "best Google Maps scraper" lists online are recycled marketing. Nobody runs the same job through every tool and counts what comes back.

So I did. Same query, same zip, same everything.

Here are my picks.

Pick by priority

If you already know what matters most to you, here's the shortcut.

The numbers, side by side

And here's everything in one place, grouped by the 5 things I tested.

But before tools... is this even legal?

Is it legal to scrape Google Maps?

Yes, it's legal under certain conditions.

Where things get risky is what you do after you collect it.

Copying or republishing reviews and photos can raise copyright issues.

If the content is public, you're not bypassing technical barriers, and you use the data responsibly, you're usually on safer ground.

Now, here's how I actually picked.

How did I choose the best Google Maps scrapers?

First, I went where the complaints live... Reddit threads, community discussions, and review sites.

The same 5 pain points kept coming up:

1. Data ... how much, how clean, how accurate
2. Usability ... how fast you get from "open the tool" to "usable export"
3. Speed ... how long a run actually takes
4. Cost ... the real price per 1,000 results, not the sticker
5. Scalability ... how far it goes before it falls over

So I built one test and ran every tool through it: scrape Chinese restaurants in zip 10003, New York, first at base data, then with every data function switched on.

Data

I didn't just count the columns each tool advertises. I counted the fields that actually came back filled for each business.

Then I checked three things people never measure:

1. Fill consistency ... of the columns a tool returns, how many are actually populated on every row (vs empty padding)?
2. Geo accuracy ... I gave all three the same zip. How many results actually landed in that zip vs drifting to other neighborhoods... or other cities?
3. Duplicates ... did the same business show up twice?

That third one matters more than it sounds, and it's where I caught something interesting (more on that in the lobstr.io section).

Usability

I looked at the whole workflow. How many clicks to your first result. How clear the input is. Whether you can catch a mistake before you spend credits. And whether scheduling and exports are built in or bolted on.

Speed

I timed one full search per tool, then normalized it to listings per minute... at base data and at full data. Because the gap between those two numbers is huge, and tools love to quote you the fast one.

Cost

I reduced everything to cost per 1,000 results, then split it into two numbers: base (just the listing scrape) and full (listing + place details + emails/social + email verification). Entry price and scale price for each.

Scalability

This is the one nobody calculates. If a scraper ran 24/7 for a month, how many rows could you realistically pull, and at what cost?

I took each tool's base speed, multiplied by the minutes in a month, and checked whether it can run things in parallel.

With concurrency, you scale horizontally. Without it, scaling is linear... more locations just means more waiting.

Then I made a list of every tool I could find via Google and AI recommendations.

Browser extensions and visual scrapers might survive on lightly protected pages. They don't survive Google Maps.

And API-only tools? You need to write code. Who are we kidding.

So I cut the usual suspects... Chrome extensions, desktop apps, visual scrapers, and API-only tools. Only cloud-based, no-code tools with a dedicated Google Maps scraper made the list.

So which ones held up?

Best Google Maps scrapers

1. lobstr.io

Key Features

1. 39 base fields + 4 add-on tiers: Collect Business Details, Extract Emails from Website, Fetch Business Images, Verify Emails
2. Smart filter for category-first setup; URL input mode available
3. Pre-scrape filters with Match Filters + No Match Reasons transparency in the output
4. Launch confirmation with credit estimate before any run starts
5. Concurrency via Slots... scale throughput up to 20x
6. Schedule recurring scrapes, Chain runs into other scrapers
7. Export to CSV or auto-deliver to Google Sheets, Amazon S3, SFTP, or email
8. Cloud-based, integrates with Make.com and 3,000+ apps

Data

lobstr.io returned 39 fields per business on the base run, expandable to ~62 on full data with its 4 add-on tiers.

That's not the most on paper. But here's what the raw counts hide: lobstr.io was the most consistent tool I tested.

Across every listing, 93% of its columns came back filled. Apify managed 68%. Outscraper 86%. So you get the same complete row every single time... no half-empty exports to clean up later.

The bigger story is accuracy.

One honest gap: lobstr.io doesn't do employee-level enrichment (decision-maker names, firmographics like revenue or headcount). If that's your workflow, Outscraper and Apify have add-ons for it.

Usability

lobstr.io has the most guided setup of the five. You're walked through every step, with no blank forms and no nasty surprises after you click run.

The default is category-first.

As you set filters, lobstr.io shows a live Tasks count... a running tally of how many runs you're about to launch. Small detail, saves you from a "why is this taking forever" moment later.

Already have the exact Google Maps URL? Flip off the smart filter and paste it directly.

The best usability touch is the launch confirmation screen. Before a single credit is spent, you see the estimated cost and a last chance to fix mistakes.

Scheduling and Chains live right in the launch step, so recurring runs and multi-step workflows don't need a separate setup screen.

The one usability nitpick: when you run email extraction, lobstr.io returns one row per email. So a business with 6 emails shows up as 6 rows. It's by design (great for outreach), but it can surprise you if you expected one row per business.

Speed

On base data, lobstr.io was the fastest tool I tested... comfortably over 200 listings per minute.

But turn on every add-on and it drops to ~4 listings per minute. That's the honest trade-off, and it's lobstr.io's weakest number... Apify is much faster on full data.

The catch? Slots. lobstr.io is the only tool here that runs scrapers in parallel. Add Slots and throughput climbs linearly... so a heavy full-data job that crawls on one Slot finishes far faster on four.

Cost

lobstr.io runs on a credit-based subscription.

1. FREE: 100 results/month
2. Base data: $2.00/1K → $0.50/1K at scale
3. Full data (details + emails/social + verification): ~$10/1K → $2.50/1K at scale

At scale, lobstr.io is the cheapest tool here on both base and full data. No one else gets close to $0.50/1K.

At low volume the full-data price ($10/1K) sits just above Outscraper ($9/1K), mostly because lobstr.io charges separately for place details and its bounce verification costs a bit more. But the moment you scale, it wins outright.

Scalability

This is lobstr.io's real moat.

At base speed on a single Slot, you're looking at roughly 8.6M rows/month if it ran 24/7. Add Slots... 2 Slots doubles it, 20 Slots takes you to ~172M/month.

Bulk input is clean too... upload a CSV of search URLs or locations and run the whole list in one instance.

It's the only tool in this comparison that scales horizontally. Everyone else is stuck with linear throughput.

Customer support

Live chat on the site, and it's one of the few things users praise consistently... quick, technically competent, actually helpful.

Best for

High-volume lead gen against long location lists, where the output needs to land flat, deduplicated, and accurate without cleanup. Slots let you multiply throughput as volume grows, and the scale pricing is unmatched.

2. Apify

The marketplace is crowded and quality varies, so I picked the Google Maps actor with the most users.

Key Features

1. 36 base fields + 6 add-on groups (place details, contacts enrichment, business-leads enrichment, reviews, images, search filters)
2. Full review text with date/keyword filters... unique here
3. Live popular times, Q&A, "people also search", image categories
4. Export to CSV, Excel, JSON, XML, HTML and more
5. Integrates natively with Make, Zapier, n8n

Data

Apify returned 36 filled fields on the base run, up to ~53 on full data.

Its standout is depth of type, not count. Apify is the only tool here that returns full review data... complete review text, reviewer profiles, star ratings, dates, all filterable.

But two things hurt.

First, fill consistency was the lowest of the three at 68%. Apify returns wide CSVs where lots of columns sit empty (hotel and gas-station fields on a restaurant, for example), so the full run swung between 35 and 59 filled fields per listing.

Second... and this is the big one... geo accuracy.

That's not a deal-breaker if you feed it exact URLs or place IDs. But for a plain location search, you have to babysit the input.

Usability

Apify gives you a lot of control, but the setup is heavy.

There's a simple happy path... search term + location + language... then Save & Start. But the location field is free-text and fussy, with long tooltips warning about city boundaries and "don't combine these settings" gotchas.

Most filters sit behind paid "($)" fields, and there's no instance management... reopen the actor and it loads your last inputs, so it's easy to launch a run you didn't mean to.

There's also no built-in deduplication for this actor, and on Google Maps duplicates are normal across overlapping areas.

Exports and integrations, on the other hand, are excellent... JSON, CSV, XML, Excel, plus webhooks, Make, Zapier, n8n.

Speed

Apify was the fastest tool on full data... ~30 listings/min, roughly 7x faster than lobstr.io's full-data rate.

On base data it managed ~90/min. Solid, but slower than lobstr.io. And there's no concurrency control... Apify decides parallelism based on load, so it can also slow down without warning.

Cost

Apify uses usage-based pricing, and it's the most expensive here.

1. Free: ~1,000 results/month ($5 credit)
2. Base: $3.00/1K → $1.50/1K at scale
3. Full: ~$11/1K → $5.50/1K at scale

At scale, full-data Apify ($5.50/1K) costs more than double lobstr.io ($2.50). Add-ons like business-lead enrichment ($5/1K) and social-profile enrichment ($8/1K) push it higher still.

Scalability

At ~90/min base, the 24/7 ceiling is around 3.9M rows/month.

But there's no concurrency, so scaling is strictly linear... more locations, more time. And bulk input is a weak spot: no CSV upload, just pasting lists via bulk edit.

Customer support

Live chat, a ticketing system, and a Discord community. Each actor has its own issue tab... for this one, the listed response time is about 1.6 days. Reasonable, not instant.

Best for

Review-heavy research... reputation monitoring, rating trends, sentiment analysis... and full-data jobs where speed matters more than price. Just feed it precise input so the geo doesn't wander.

3. Outscraper

Key Features

1. 53 base fields → ~75 on full data, all flat columns (no nested JSON)
2. Built-in Delete duplicates + a Quick Filters panel (all free)
3. 14 enrichment services, incl. company insights (revenue, headcount, industry) and phone identity
4. Custom locations + Plain queries input
5. Bulk input via CSV, XLSX, TXT, or Parquet
6. One-click re-enrichment of finished runs

Data

Outscraper returned the most fields of any tool... 53 on base, ~75 on full. And they're all flat columns, no nested JSON to parse.

It also goes furthest on enrichment... 14 services covering contacts, firmographics (revenue, founding year, headcount), and even reverse phone-identity lookup. If you want company-level B2B data alongside the listing, Outscraper has the widest menu.

Usability

Outscraper is one of the cleaner setups. Pick a category, choose a location, set a max, hit Get Data.

A genuinely nice touch lobstr.io doesn't have: one-click re-enrichment. You can enrich a finished run without re-scraping it.

The main gap is scheduling. There's no built-in scheduler... recurring pulls mean rerunning jobs manually. And there's no live console or progress tracker; you launch and check back later.

Speed

This is where Outscraper struggles.

Base data ran at 20–40 listings/min. Full data dropped to under 1/min. And it doesn't even show a completion time, so I had to clock it myself.

Cost

Pay-as-you-go, no subscription.

1. Free: first 500 businesses
2. Base: $3.00/1K → $1.00/1K at scale
3. Full: ~$9/1K → $3.00/1K at scale

At entry, Outscraper's full-data price ($9/1K) is the cheapest of the three... because it bundles place details into the base scrape. But the scale rate ($3/1K) still lands above lobstr.io ($2.50). And the free filters are a real plus... lobstr.io and Apify both charge for some.

Scalability

Input is the strongest here... CSV, XLSX, TXT, and Parquet uploads.

But throughput is the weakest. At ~30/min base with no concurrency, the 24/7 ceiling is about 1.3M rows/month... the lowest of the three. Predictable, but it won't speed up on bigger lists.

Customer support

Live chat, and responses are fast. Just note their guidance can be optimistic... support told me 500 results/query, but Google still caps you around 250.

Best for

Outbound lead workflows that want the widest field coverage per business in one export, plus firmographic enrichment... and don't need scheduling or millions of rows a month.

After those three, two big names kept coming up that I tested and cut: Bright Data and PhantomBuster.

So why didn't they make it?

The scrapers that didn't make the list

These aren't bad tools. They just lost on the cores that matter for Google Maps lead gen.

Bright Data

Why it didn't make it:

1. Slowest of everything I tested. 102 records took 3m39s... about 35 min/1K results. A 500-location list would take 60+ hours, and there's no concurrency to fix that.
2. No email scraping, no pre-scrape filters, no scheduling. For lead gen, those are table stakes.
3. Nested JSON output. Several fields come back as JSON objects you have to parse before you can even filter in a spreadsheet.

It's a fine pick for structured one-off pulls when you already have the URLs or IDs and want pay-as-you-go. Just not for scaled, clean lead lists.

PhantomBuster

Why it didn't make it:

1. Slowest in the main field, and priced by runtime. 120 results took 12m38s (~105 min/1K). Because you pay for time, slow runs cost more... the worst combo.
2. Fewest fields (21) and no email scraping. No coordinates, no place ID, no review distribution. The lightest output of any tool.
3. No deduplication and no pre-scrape filters. More cleanup after every run.

Scheduling is genuinely good and built into the flow, so it works for light, recurring pulls on a fixed set of URLs. Beyond that, the speed and pricing model work against you.

And one more: Hasdata

Hasdata kept showing up in threads too, with tempting pricing ($0.74/1K). But in my test, 236 results took 70+ minutes and the run never finished (~297 min/1K... nearly 3x slower than PhantomBuster), and there's no bulk task automation. Competitive price, but it can't deliver at volume.

FAQ

Which Google Maps scraper returns the most data?

Outscraper ... 53 fields per business on base, ~75 on full, all flat columns. But lobstr.io is the most consistent (93% of fields filled on every row) and the most accurate on location.

Which is the cheapest at scale?

lobstr.io ... $0.50/1K on base data and $2.50/1K on full data, the lowest of any tool here. At entry/low volume, Outscraper's full-data run is slightly cheaper.

Which one is the most accurate?

Can I scrape Google reviews with these?

Apify has a full Reviews add-on (text, reviewer data, owner responses, date/keyword filters). lobstr.io has a dedicated Google Maps Reviews Scraper at $0.40/1K → $0.10/1K, and you can Chain your leads run straight into it. For high-volume reviews, the chained lobstr.io route is cheaper.

Which scrapers have built-in deduplication?

How do I find Google Maps businesses with an email?

Conclusion

That's a wrap on the best Google Maps scrapers for 2026.

If accuracy, clean output, and cost-at-scale matter most, lobstr.io is the pick. Want raw full-data speed and reviews? Apify. Want the widest field coverage in one export? Outscraper.