Apify Google Maps Scraper Alternatives Without Actor Maintenance
If you have run an Apify Google Maps Scraper run in the last year, you already know why it is the most popular actor in the marketplace. The grid logic works, the field coverage is deep, and the maintainer community has been at it long enough that most edge cases have a known workaround. The reason teams start shopping for alternatives is rarely "the actor is bad" — it is more often "my daily lead-gen runs have outgrown the actor model." This post is an honest map of the Google Maps scraping landscape around Apify: where the actor still wins, where the friction shows up at scale, and what each alternative is actually good at.
Why the Apify Maps Actor Is the Default
Before talking about alternatives, it is worth being specific about what the Apify Google Maps Scraper does well, because the answer is "most things."
The grid logic is mature. Maps caps a single search-URL pagination at roughly 120 results before relevance drops off. The Apify actor handles geographic slicing as a built-in concept — you give it a city or a polygon, it walks viewport tiles internally, and you get back a deduped result set that covers the whole area. That logic took years to settle and the actor's implementation is one of the better ones in the market.
The fields are complete. Most actors return the obvious surface — name, address, phone, website. The popular Apify Maps actor goes further: category and full category list, neighborhood, latitude/longitude, full weekly hours keyed by day, timezone, rating and review count, and Google's internal identifiers (cid, feature_id, data_id). The cid in particular is what makes diff-scanning possible across runs, and many cheaper scrapers omit it.
The maintainer community is responsive. Google ships Maps UI tweaks on its own cadence. The popular actors have multiple contributors and a long backlog of fixed-this-quarter issues, which is how they keep working through DOM changes that break simpler scrapers.
For a one-off lead pull — "give me every plumber in Austin once, I do not care if it takes 20 minutes" — this is the tool. The friction shows up when the shape of the job changes from "once" to "every Monday."
Where the Actor Model Strains at Scale
Two specific places, both predictable in hindsight.
Compute-unit plus per-result billing stacks unpredictably. Apify's base subscription gets you a budget of platform credits. The Maps actor charges on top — per actor compute unit (roughly tracking how long the run takes) and per result returned. A one-shot scrape barely registers. A daily run across 50 viewports, each pulling 80–120 results, multiplies both axes at the same time. Two consecutive months can land with materially different bills if Google's response latency drifted up and your runs ran longer per viewport. For a lead-gen agency where the data spend is a forecastable line item that goes to clients, the variance is the real cost.
Patch cadence depends on the actor author. This is not a knock — community actors are how Apify works, and the model has clear advantages — but it matters at the operational level. When Google changes its Maps UI, the actor needs a fix. Sometimes the maintainer pushes that fix the same day; sometimes they push it a week later if the actor is no longer their priority project; sometimes the popular actor forks and you have to pick which one to migrate to. Enterprise vendors carry that patch obligation as a contract. Managed first-party endpoints carry it as a product responsibility. Community actors carry it as a maintainer's reasonable best effort.
For one-off runs the patch lag is invisible. For a daily cron feeding a client's pipeline, a two-day outage in the middle of a sales sprint is a real problem.
A third, smaller friction is the operational surface area. Apify is a general platform, not a Maps-specific product, so the dashboard, the dataset model, the scheduling layer, and the input-schema editor are all designed to support every actor in the marketplace. That generality is the point, but it also means a team doing only Maps work navigates the same configuration surface as a team running 40 different actors. For a lead-gen agency where every operator only ever touches the Maps actor, the cognitive load of the generality shows up in onboarding new hires and in writing internal runbooks.
The Shape of the Comparison
Before the table, it helps to draw the buckets honestly. Tools that scrape Google Maps fall into four categories.
Community actor marketplaces. Apify is the prototype. You rent an actor written by a third-party developer. Strength: depth of coverage and field detail, mature grid logic. Weakness: billing variance and a patch model that depends on the individual maintainer.
General-purpose scraping APIs with a Maps endpoint. SerpAPI, Outscraper, ScraperAPI variants. You pass a search URL or a query, you get structured JSON back. Strength: predictable per-request pricing, fast to integrate. Weakness: field depth varies, monitoring is DIY.
Enterprise proxy and scraper stacks. Bright Data, Oxylabs. Mature Maps-specific scraper APIs, the largest residential proxy pools, contract-grade SLAs. Strength: reliability and scale. Weakness: enterprise sales cycle and pricing complexity that does not pencil out until volume is high.
Official Google API. Places. Strength: it is the source. Weakness: terms of service and field-level metering rule out most lead-generation use cases.
Multi-platform structured APIs. Outscraper and LogPose live here for Maps specifically — a managed first-party Maps endpoint with consistent JSON output across a wider platform set so your code does not branch when you add Yellow Pages, Yelp, or a directory site alongside Maps.
Knowing which bucket your real shape fits narrows the decision before you start comparing fields.
The Honest Comparison
| Tool | Pricing shape | Results per search cap | Structured field coverage | Diff monitor for new businesses | Grid logic | ToS posture | Best for |
|---|---|---|---|---|---|---|---|
| Apify Google Maps Scraper | Compute units + per-result | ~120 per viewport (Google cap) | Deep (incl. cid, hours, lat/lng) | No (schedule + DIY diff) | Built-in tile walker | Scrapes public Maps, accepts the gray area | One-off and weekly lead pulls, novel geographies |
| Outscraper | Per-result | ~120 per viewport | Deep, similar to Apify | No (export + DIY) | Built-in by city/region | Same as Apify | High-volume one-shots with simple integration |
| Bright Data Google Maps Scraper | Custom enterprise (per-request + contract) | ~120 per viewport | Deep, contract-grade | Limited (dataset deltas) | Built-in | Enterprise contract, explicit data-use terms | Enterprise lead programs with procurement budget |
| SerpAPI Google Maps | Per-search | 20 per page, ~100 pageable | Moderate (name, address, phone, hours; lighter on internals) | No (DIY) | Manual — you pass lat/lng | Scrapes public Maps SERP | Developers who want a thin, SERP-style JSON layer |
| Google Places API (official) | Metered per-request + per-field | 60 per query (3 pages of 20) | Authoritative, but field-restricted by ToS | No | Manual via location bias | Strict — most lead-gen storage forbidden | In-app maps, autocomplete, store locators |
| LogPose | Per-credit (per-page) | ~120 per viewport | Deep (name, address, phone, website, category, hours, lat/lng, cid) | Yes (saved-search monitor + webhook/email) | Manual viewport list, bulk in parallel | Scrapes public Maps, same posture as Apify | Daily lead-gen across multiple platforms with alerts |
A few words on each.
Apify Google Maps Scraper is the right tool when your work is heterogeneous and your Maps runs are infrequent enough that variable billing does not bother you. The actor is genuinely one of the best in the market — the grid walker, the field coverage, and the patch responsiveness from the maintainer community are real strengths. The honest constraints are billing variance at recurring-cron scale and the fact that you are renting from a community author whose attention is not contractually yours.
Outscraper is the closest like-for-like in shape — managed Maps endpoint, deep fields, geographic grid logic built in. The two main differences with LogPose are pricing model (per-result versus per-credit) and platform scope outside Maps. If your work is mostly Maps and you want a thin, focused tool, Outscraper is a fair pick. If you also scrape Yellow Pages, Yelp, real estate listings, or ecommerce sites in the same pipeline, a multi-platform API consolidates the integration.
Bright Data's Google Maps Scraper is the enterprise tier. The product is solid, the SLA is real, and the data-use terms are explicit because they came out of a procurement conversation. The catch is the procurement conversation. If you have not done that evaluation cycle yet, you are probably not at the volume where the contract minimums pencil out.
SerpAPI's Google Maps endpoint is a different shape entirely — a thin SERP-style JSON layer over Maps search results. Field coverage is moderate (you get the basics, less of the deep internals), the pagination model mirrors a search engine, and the integration is fast. Good if you want Maps data shaped like web SERP data and you do not need the grid walker as a built-in.
Google Places API is the official source. For any use case that fits inside the ToS — in-app maps, store locators, address autocomplete — it is the right answer. For lead generation specifically, the storage and use restrictions are the disqualifier, not the price.
LogPose sits in the multi-platform structured-API bucket. Managed Maps endpoint maintained as a first-party product, same JSON shape as the Yellow Pages, Yelp, and directory endpoints, monitor primitive that polls a saved search and fires alerts on new cid values. The honest constraint is grid logic — you pass the viewport list explicitly (or build it with a small helper), rather than handing in a city name and letting the platform pick the tiles.
Per-Use-Case Recommendations
If you run one or two Maps scrapes per month for a single metro and never touch them again, stay on the Apify Google Maps Scraper. The compute-unit billing is fine at that cadence, the actor is the best in its class for one-off depth, and switching tools to save money on a workload that does not exist yet is wasted effort.
If you need authoritative Maps data inside an app the user sees — a store locator, a "find a dentist near you" widget — use the Google Places API. The ToS exists for exactly this use case, and the data is the source of truth.
If you are pulling 50,000+ Maps records per week, have an annual procurement budget, and need a paper SLA, Bright Data or Oxylabs. The contract overhead only makes sense once volume is real.
If your shape is "I need 5,000 plumbers across Texas every Monday with phone + website, no actor babysitting, alert me when new businesses appear in the saved viewports" — that is the LogPose shape. Managed endpoint, monitor primitive built in, same JSON across Maps and the directory sites you will inevitably layer in for the businesses with no website on Maps.
If you want a thin developer-facing Maps SERP API and you will write your own grid logic, SerpAPI.
If you want a focused, Maps-mostly managed API and you do not need the multi-platform scope, Outscraper.
A specific note on the lead-gen agency shape, because it is what most teams shopping for an Apify Maps alternative actually have. The job is usually 5–20 search terms (niches), each across 20–80 viewports (a metro or a state), refreshed weekly. That works out to a few hundred to a few thousand viewport-scrapes per week, with the same saved set re-running each Monday. Two things matter at that shape: bill predictability week over week, and a way to surface "what is new this week" without writing the diff yourself. Compute-unit billing makes the first hard; the missing monitor primitive makes the second hand-rolled. A per-credit managed endpoint with a saved-search monitor matches the shape better — not because the actor is bad, but because the workload outgrew what the actor model is built for.
Code: Same Job, Two Tools
To make the difference concrete, here is the same task — pull plumbers from one Austin viewport — done two ways.
On Apify, you run the Google Maps Scraper actor by POSTing a JSON input, then polling the run, then fetching the dataset:
# 1) Start the actor run
curl -X POST "https://api.apify.com/v2/acts/<actor-id>/runs?token=YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"searchStringsArray": ["plumbers"],
"locationQuery": "Austin, TX",
"maxCrawledPlacesPerSearch": 120,
"language": "en"
}'
# → returns { "data": { "id": "<run-id>", ... } }
# 2) Poll the run status, then fetch the dataset when done
curl "https://api.apify.com/v2/actor-runs/<run-id>/dataset/items?token=YOUR_TOKEN"
You hand off the grid decision to the actor. The output is deep but the schema is whatever this actor version returns, and the run cost depends on how long Maps decided to take that morning.
On LogPose, you pass the viewport URL explicitly and get a consistent shape back:
# 1) Submit
curl -G "https://api.logposervices.com/api/v1/ecommerce/googlemaps/search" \
-H "X-API-Key: lp_xxxxxxx" \
--data-urlencode "url=https://www.google.com/maps/search/plumbers/@30.2672,-97.7431,13z" \
--data-urlencode "pages=5"
# → {"job_id": "gm_8f3a..."}
# 2) Fetch result once status == "completed"
curl https://api.logposervices.com/api/v1/jobs/gm_8f3a/result \
-H "X-API-Key: lp_xxxxxxx"
Same flow for Yellow Pages, Yelp, and the other directory endpoints — only the path changes. The grid is your code's job; the parsing is not.
For weekly net-new-listing alerts, the monitor primitive is one call:
curl -X POST https://api.logposervices.com/api/v1/monitors \
-H "X-API-Key: lp_xxxxxxx" \
-H "Content-Type: application/json" \
-d '{
"platform": "googlemaps",
"endpoint": "search",
"params": {
"url": "https://www.google.com/maps/search/plumbers/@30.2672,-97.7431,13z",
"pages": 5
},
"interval_minutes": 10080,
"rules": [{"type": "new_item", "key": "cid"}],
"notify_targets": ["email:ops@yourdomain.com"]
}'
That removes the cron host, the state storage, and the diff loop from your build.
Common Gotchas When Migrating Off the Apify Actor
Run-vs-request billing. Apify bills per compute unit, which roughly tracks how long the run took. Per-request APIs bill per call or per page. For deep, slow crawls Apify can be cheaper; for many shallow per-viewport calls the per-request model wins. Estimate both on the actual workload shape — number of viewports, pages per viewport, frequency — before you switch.
Schema drift between actors. If you migrated to the Apify Maps actor from a different actor, you have already done a parser rewrite. Switching off the Apify actor to a managed endpoint is a similar one-time tax — the upside is no further drift because the first-party schema is the contract.
Grid logic ownership. Apify and Outscraper handle the geographic grid internally. SerpAPI and LogPose expect you to pass viewports. That trade is real: built-in grids are easier to start; explicit viewports are easier to reason about when results look wrong because you can see exactly which centroid produced which row.
Diff scanning vs. dataset history. Apify's dataset stores every run, which makes manual diffs possible after the fact. Per-request APIs typically return the result once and expect you to store it. If diff-scanning is core to your workflow, either pick a tool with a monitor primitive (alerts in the platform) or budget the storage layer in your migration plan.
Cloudflare 100-second edge timeout. api.logposervices.com sits behind Cloudflare, so any synchronous job that takes 100+ seconds returns a 524 to your client even though the job continues server-side. Always poll for completion or use the webhook callback; do not expect an inline response on a big page count.
The Honest LogPose Fit
LogPose works well when the shape is "I need clean structured Maps data on a schedule, across the same viewports each week, with an alert when something new appears." The async job pattern is the same across the Maps, Yellow Pages, Yelp, and ecommerce endpoints, so your integration stays one shape as you add platforms. Monitors are a single API call rather than a custom cron-plus-state-store build. The honest constraints are the grid (explicit viewports, not city names), and platform scope — if your real need is scraping a long-tail directory site that no managed vendor covers, an Apify actor will get you there faster than waiting for a new endpoint.
Get Started
Sign up at logposervices.com, generate an API key from Tool → API Keys, and submit a request against /api/v1/ecommerce/googlemaps/search?url=...&pages=5. The free tier covers enough credits to validate the integration on real viewports before any commitment.
Related reading: How to scrape Google Maps for local business leads for the end-to-end pipeline, How to enrich business leads with emails, phones, and socials for the chained workflow that turns Maps rows into outreach-ready records, and Apify alternatives for ecommerce scraping in 2026 if your work spans Maps and product data.