Google Maps Scrapers Compared: What Each One Costs

Scraped Google Maps data is cheaper per record than the licensed Places API, and any comparison that claims otherwise is not worth reading. Outscraper charges around $3 per 1,000 records for base Maps data — roughly ₹0.26 each — against about ₹3.08 for a billed Places call on our side.

We use the licensed path and sell a product built on it, so that is an uncomfortable number to open a comparison with. It is also the true one, and the rest of this only makes sense once it is on the table.

Published pricing. Both scrapers bill per record and run in the cloud.

The headline numbers understate the scrapers' real cost, though, and in a predictable way. Costs stack across separate tasks — scrape, then find email, then verify, then enrich — and a full lead profile on Outscraper is quoted at around $14 per 1,000 rather than $3. Apify's base is lower and its enrichment costs can match or exceed Outscraper's depending on which actor you run.

The comparison to make is therefore not base-to-base. It is what a usable record costs after everything you actually need is attached, and both platforms are considerably more expensive by that measure than the first line of their pricing page suggests.

Three things, and only one of them is technical.

Terms. Scraping Maps is against Google's terms of service. That is a business risk rather than an engineering problem, it does not go away with better code, and it is a choice worth making knowingly rather than discovering later. The licensed API is the permitted route to the same data and costs more for exactly that reason.

Maintenance rather than collection. A scrape is a photograph. The expensive part of running an index is not gathering it, it is re-checking — and the website field is the one that changes when a business does the thing you were going to sell them. A cheaper record that is wrong in that specific way costs more than it saved.

Stability. Scrapers break when Google changes the Maps interface, which happens regularly. If your prospecting depends on one, it depends on somebody else's maintenance schedule.

None of that makes scraping wrong. It makes it a different product with a different risk profile, and the price gap is roughly what those three things are worth.

That last row is genuine. Below about fifty leads a week, manual Maps prospecting at roughly three minutes a lead beats paying anybody, and it gives you the whole listing while you work rather than a row in a file.

Per-record price is the wrong unit on which to have these compared, and it flatters whichever one you already prefer.

Cost per usable record is closer. A scraped row with no phone number, an unmapped category or a business that closed is not a record you can work, and the share of those is not in anybody's pricing page.

Cost per conversation is the one that matters. Take everything you spent on data in a month and divide it by the number of decision-makers you actually spoke to. That number folds in dedup waste, dead listings, wrong categories and the stale website fields — and it is usually several times the per-record price, whichever route you took.

Run that calculation once and the comparison stops being about ₹0.26 against ₹3.08, because both are rounding errors against the value of an afternoon. What separates the options at that point is how many of your afternoons get wasted, which is exactly what the cheaper column does not tell you.

Whichever route you take, three costs sit outside the per-record price and they are usually larger than it.

Deduplication. Getting past the roughly 120-result cap means searching by area, which means the same business appears in overlapping searches. Deduplicating on the place identifier rather than the name is not optional — names collide constantly.

Category mapping. The labels on a listing and the taxonomy your pipeline expects are not the same vocabulary, and anything unmapped is silently dropped. If a run discovers 900 businesses and stores 500, the problem is mapping rather than scraping, and no amount of per-record savings fixes it.

Re-checking. Published B2B data decay runs 22–30% a year. For this use case it concentrates in the one field the pitch depends on, which makes an unmaintained cheap list expensive in a way that shows up mid-conversation rather than on an invoice.