Best Google Maps Reviews Scrapers of 2026 (No Code + API)

⚡ 30-Second Summary

1. I tested the Google reviews scrapers people actually recommend... no-code tools, APIs, the lot... by buying every paid plan and running multiple tests across thousands of reviews from the same listings, then comparing field-by-field. Three made the cut. Here's the short version.
2. lobstr.io ... best overall, and best at scale. Cleanest, deepest review data (owner replies, visit dates, edit tracking, the most accurate Local Guide flag I tested), the cheapest of the three ($0.40/1K → $0.10/1K at scale), and the fastest by a mile (~448 reviews/min). Pick it for high-volume review monitoring and sentiment work. Trade-off: no keyword-search filter, and a couple of filters the others have.
3. Apify ... best for everything-in-one-actor. Solid review data and the same listing context baked into every row. Pick it for pulling reviews + business metadata in a single run. Trade-off: priciest at scale, and it misjudged Local Guide status on ~36% of reviewers in my test.
4. Outscraper ... best for a no-subscription one-off. Pay-as-you-go, with a keyword-search filter the others lack. Pick it for a quick one-time pull. Trade-off: no translation, no Local Guide flag, empty likes, and by far the slowest (~38 reviews/min).
5. Didn't make the cut: Bright Data API, ScrapeHero, and Livescraper. Reasons at the end.

If you've gone looking for a way to pull Google reviews at scale, you've hit the same wall everyone does.

The official API hands you 5 reviews per place. Five. 🙃 The DIY scripts die every other week. And half the "scrapers" you find return 20 reviews and call it a day.

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

So I did. I bought the paid plans and ran every tool through the same jobs... thousands of reviews, multiple tests each. Here are my picks.

Just tell me which one

Want the full picture before you scroll? Here's every tool's overall score, plus a field-by-field and filter-by-filter breakdown.

But before tools... is this even legal?

Is it legal to scrape Google reviews?

Yes, as long as the data's public and you don't misuse it.

Does Google allow it? No.

They'd rather you stay inside the Places API and its 5-review prison.

But does that make scraping public reviews illegal? No.

The reviews are already public. Anyone with a browser can open a listing and read every review without logging in.

No paywall, no auth, no private content.

And the law backs it up.

Where it gets risky is what you do after.

Reviews are user-generated content, so republishing them as your own can raise copyright issues.

So if the content's public, you're not bypassing technical barriers, and you use the data responsibly, you're usually on safe ground:

1. Respect rate limits
2. Comply with GDPR and similar laws
3. Don't republish reviews as your own content
4. Don't use reviewer data to harass or impersonate
5. Respect takedown requests

But hold on... how do I even define "the best"? Let me walk you through my criteria.

How did I choose the best Google reviews scrapers?

First, I went where the complaints live... Reddit threads, community discussions, and review sites... to find the pain points that actually matter.

{SCREENSHOT: community research gif/montage}

Then I shortlisted the 5 criteria that decide it:

1. Data
2. Usability
3. Speed
4. Cost
5. Scalability

But how did I actually measure each one?

Data

Everyone counts fields. I don't do generic.

So I tested quantity and quality the honest way. I bought each tool's paid plan and ran the same review sets through all three... thousands of reviews, multiple listings... then mapped every field by value.

That matters because raw column counts lie.

One tool returned 74 columns... but 30 of them were just the business listing copied onto every single review row, and a chunk more sat empty.

So I looked at:

1. Depth ... which review data points each tool actually returns (owner replies, visit dates, sub-ratings, reviewer profiles, likes, translation).
2. Consistency ... of the columns a tool returns, how many are actually filled.
3. Accuracy ... when two tools disagree on the same review, who's right? I verified the disputed fields against Google directly.

Usability

More than "is the dashboard pretty." I broke it into:

1. Input options ... place URLs, Place IDs, bulk upload.
2. Filters ... can you narrow reviews before you pay? By date, rating, language, sort order, content, origin.
3. Ease of use ... how fast you get from opening the tool to a clean export, and how easy it is to avoid an expensive mistake.

Speed

I clocked how many reviews each tool pulls per minute, across multiple runs of thousands of reviews so I'd land on a fair average... not one lucky run.

Cost

I normalized everything to cost per 1,000 reviews, at entry pricing and at scale, and folded in the stuff tools love to bury... filter costs, add-ons, and free tiers.

Scalability

The one nobody calculates. If a scraper ran 24/7 for a month, how many reviews could you realistically pull? I took each tool's speed, multiplied by the minutes in a month, then factored in concurrency... how many bots it can run in parallel.

Then I found my candidates the obvious way... Google, AI recommendations, Reddit... and tested the ones that held up.

So which ones made it?

Best Google reviews scrapers

You've seen the scorecard. Here's the story behind the numbers, tool by tool.

1. lobstr.io

Here's how it scored on my 5 criteria.

Data

lobstr.io returns 39 data points per review.

That's place info, review content, reviewer profile, engagement, owner response, and the detailed sub-ratings Google shows under the main star score (Food, Service, Atmosphere). Here's the field set:

The raw count isn't lobstr.io's real edge though. Depth and accuracy are.

And its Local Guide flag is the most accurate I tested. This one surprised me.

Across the reviews I ran through both, lobstr.io and Apify disagreed on Local Guide status for a big chunk of reviewers... and the disagreement was 100% one-directional.

So I checked the disputed profiles on Google directly.

lobstr.io was right every time. Apify was under-flagging Local Guides by roughly 36%.

If you segment or score reviewers by Local Guide status, that's a real accuracy gap... in lobstr.io's favor.

One honest limitation: the data's deep, but a few filters are missing (more on that below).

And the CSV export drops any column that's 100% empty for your listing... so if a business has no owner replies, you won't see that column at all.

It's tidy, but it can surprise you if you're diffing exports.

Usability

lobstr.io was the easiest tool to use. The whole flow is a simple wizard: Create Squid → add tasks → settings → launch.

Ways to feed it a job:

1. A Google Maps place URL
2. A Place ID (e.g. ChIJaZUyZj6-3zgR0Xw7zvtDDj8)
3. Bulk upload a CSV/TXT... up to 10,000 place URLs per run

Pre-scrape filters (narrow reviews before you pay):

1. Sort by (newest, most relevant, highest, lowest)
2. Newer than (a specific date, to the second + timezone, or a relative window like 7 days)
3. Language
4. Skip reviews without text / without image
5. Rating filter (minimum or maximum)

Where it pulls clear of the pack:

1. Proper instance management... runs live inside their Squid
2. A live progress tracker and console, plus per-run timestamps
3. A daily credit cap so you don't overspend
4. Runs pause when credits run out... no overage, no half-finished exports
5. Abort anytime, plus webhook and email alerts
6. Built-in scheduling, which makes weekly review monitoring trivial

Speed

lobstr.io was the fastest tool I tested by a wide margin.

Across my runs it averaged ~448 reviews per minute. That's more than 3x Apify and nearly 12x Outscraper.

And that's on a single slot. There's a lever to go faster still, but I'll get to that under scalability.

Cost

lobstr.io runs on a credit-based monthly subscription. Reviews are cheap... each review is just 0.2 credits.

1. Free tier: 500 reviews/month
2. Entry: $0.40 / 1K reviews
3. At scale: $0.10 / 1K reviews

Scalability

This is lobstr.io's real moat. At ~448/min on a single slot, running 24/7, that's ~19.3M reviews/month on paper.

And you can add up to 100 slots per Squid... each one another bot pulling in parallel, so throughput scales almost linearly.

I kept the math at one slot on purpose, to give you the most modest number.

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: anyone doing high-volume review monitoring, sentiment analysis, or multi-location reputation work who wants the deepest data at the lowest price.

2. Apify

Here's how it scored on my 5 criteria.

Data

Apify returned the most columns... 74 of them.

But here's the catch: 30 of those are the business listing copied onto every single review row (address, categories, city, lat/lng, and so on), and a handful more came back empty.

Strip those out and you're left with 39 genuinely review-level fields.

Two things hold it back.

It doesn't track edited reviews (no modified flag).

And on Local Guide accuracy, it under-flagged by ~36% versus the verified truth... I checked the disputed profiles on Google myself, and Apify kept marking real Local Guides as regular users.

Usability

Good news here: unlike Apify's busy Google Maps actor, the reviews actor is simple. All the input options sit on a single page... no hunting through dense settings.

Ways to feed it a job: place URLs, Place IDs, search terms.

Filters: newer than (day-level), language, review origin (Google only vs all sources), and a personal-data toggle to include/exclude reviewer info. Notably missing: a sort-order control and a rating filter on reviews.

The catch is the platform basics. No instance management... reopen the actor and it loads your last inputs, so it's easy to fire off a run you didn't mean to.

And no built-in scheduling as clean as lobstr.io's.

Speed

Apify averaged ~130 reviews per minute across my runs. Middle of the pack, and honestly slower than I expected from Apify.

There's no concurrency slider either... parallelism is tied to the memory you allocate, so to go faster you pay for more compute.

Cost

Usage-based, and the most expensive of the three at scale.

1. Free tier: $5 credit/month... and since free-tier reviews bill at the no-discount $0.60/1K, that's about 8,300 reviews
2. Entry (paid plans): $0.45 / 1K reviews
3. At scale: $0.30 / 1K reviews

That $0.30 floor is 3x lobstr.io's $0.10.

Scalability

At ~130/min, 24/7, that's about 5.6M reviews/month on the default config.

You can push higher by paying for more memory and running more concurrent actors... but you scale by spending on compute, not by flipping a switch.

Customer support

Live chat, a ticketing system, and a Discord community. Each actor has its own issue tab, with a listed response time around 1.3 days. Reasonable, not instant.

Best for: anyone who wants reviews and the full business listing context in a single run, and doesn't mind paying more for it.

3. Outscraper

Here's how it scored on my 5 criteria.

Data

Outscraper returned 49 columns, ~32 of them populated per review.

It covers the basics well... review text, rating, sub-ratings, reviewer profile, owner reply field, and review images (with a stable photo ID, which is a nice touch).

It even splits a reviewer's reviews count from their ratings count, which the others roll into one.

But it's the thinnest on the review data that matters:

1. No translation. Just review_text... no original-language text, no translated version, not even a language field. For non-English reviews, that's a real hole.
2. No Local Guide flag at all.
3. Likes came back empty across the board.

So if your reviews are multilingual or you care about reviewer credibility signals, Outscraper leaves you short.

Usability

Clean one-page setup... input → data → filters → export, laid out in order.

Ways to feed it a job: search queries, Place IDs, place URLs, bulk upload (CSV, XLSX, TXT, Parquet).

Filters (all free): sort by, a from→to date window (the only tool that lets you cap the end date), rating (min or max, one at a time), review origin, review content (all / with text / without text), language, and... the one filter lobstr.io and Apify don't have... keyword search inside review text.

Where it gets rough: once you hit Get Data, you're flying blind. No live console, no progress tracker, no run timestamps. You launch and check back later.

Speed

This is where Outscraper struggles. It crawled at about 38 reviews per minute... nearly 12x slower than lobstr.io.

And there's no completion timer in the dashboard, so I had to clock the runs myself.

Cost

Pay-as-you-go, no subscription.

1. Free tier: first 500 reviews of every scrape (and free entirely if you stay under 500/month)
2. Entry: $3 / 1K reviews
3. At scale: $1 / 1K reviews

That's the priciest entry point by a mile ($3 vs $0.40-0.45), though the no-subscription model suits genuine one-offs.

Scalability

At ~38/min with no concurrency, the 24/7 ceiling is about 1.6M reviews/month. Predictable, but it won't speed up on bigger jobs.

Customer support

Live chat, and responses are fast and pretty responsive. Most of the reviews are positive. I did have some issue with their ability to answer technical questions in the past, but overall it's good.

Best for: a no-subscription, one-time pull... especially if you need keyword search inside reviews and your reviews are mostly English.

After those three, a few more names kept coming up that I looked at and cut.

The scrapers that didn't make the list

These aren't bad tools. They're actually all faster than Outscraper.

But they lost on the thing that decides a review scrape... filtering.

When you can't narrow reviews before you pull them (by date, rating, language, content), you pay to scrape everything and clean up after. And a couple are thin on data too.

Bright Data

Its Google reviews collector is genuinely data-rich... it pulls owner replies, Local Guide status, likes, even the listing's Q&As. So why isn't it in the top 3?

No built-in filters... at all. There's no date, rating, language, or sort control.

The workaround is to apply filters on the Google Maps listing page yourself, copy the final URL, and hand it over... it just collects whatever that URL shows.

For monitoring "only reviews from the last 7 days" across hundreds of places, that manual URL-building doesn't scale.

It's a powerful collector for developers, but the wrong shape for filtered, no-code review work.

ScrapeHero

It takes a place URL and returns a clean, lightweight record... and to its credit, it keeps original (translated) text, Local Guide status, and owner replies.

Why it didn't make it: the fewest data points of anything I tested (~20 fields, no likes), and only a sort-by filter. No date filter, no rating filter, no language filter.

Its credit math also lands awkwardly. At entry, 2 credits per review on the $5 (4,500-credit) plan works out to about $2.22/1K, dropping to $0.50/1K only at serious scale (3M credits for $750).

Fine for a quick pull, light for everything else.

Livescraper

Why it didn't make it: only a sort-by filter, no translation, no Local Guide flag, and a glitchy export in my testing.

Flat pricing at $0.002/review ($2/1K, no scale discount) means it never gets cheaper as you grow... so at volume it's more expensive than every tool in the top 3.

It's quick (~100 reviews/min), but quick doesn't help when the export trips over itself and you can't filter what you pull.

FAQ

Which Google reviews scraper returns the most data?

lobstr.io and Apify are neck and neck on review fields, but lobstr.io edges it: it's the only one that tracks edited reviews, and it had the most accurate Local Guide flag in my test. Outscraper trails... no translation, no Local Guide, empty likes.

Which is the cheapest?

lobstr.io. $0.40/1K at entry, dropping to $0.10/1K at scale... the lowest of the three at both ends. Apify is $0.45 → $0.30, and Outscraper is $3 → $1.

Which is the fastest?

lobstr.io, easily. Across my runs it averaged ~448 reviews/min, versus Apify's ~130/min and Outscraper's ~38/min.

Can I scrape owner responses to reviews?

Yes... all three capture the business's reply to a review. lobstr.io and Outscraper expose it as a single field; Apify splits the reply text and date.

How many reviews can I scrape per place?

All of them. None of these three is capped at the official API's 5-review limit. If a business has 12,000 reviews, you can pull all 12,000. (Google's spam filter hides 5 to 20% of reviews from everyone, so counts can look slightly off... that's Google, not the scraper.)

Can I scrape only recent reviews?

Yes. All three support a "newer than" date filter. Only Outscraper also lets you set an end date (a from→to window). Pair the date filter with "sort by newest" for a clean monitoring setup.

Conclusion

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

If you want the deepest data, the lowest price, and real speed at scale, lobstr.io is the pick. Want reviews bundled with full business listing context in one run? Apify. Want a no-subscription one-off with keyword search? Outscraper.