Best Android App Data Scrapers in 2026: Tested & Ranked

I needed an Android app data scraper that could run at scale to track competitor ratings, monitor install growth, and feed a recommendation model. I spent a week putting every Google Play scraper I could get an API key for through the same job: pull the app detail page for a set of target package IDs, extract the full metadata to JSON, and see what survived both the anti-bot layer and the parsing step. This is the ranked result based on numbers I measured myself.

Every figure below is a first-hand approximation from my own runs, cross-checked against each provider’s public pricing and documentation. I tested in June 2026.

*Apify’s Google Play actors bill per event (around $1 per 1,000 apps returned), and reviews add to the cost, so a review-heavy run is higher. The per-1k figures are first-hand approximations from my runs cross-checked against each provider’s public pricing.

The Android app data API problem in 2026

The Android app data API problem in 2026 is that the official Google Play Developer API only manages your own published apps. It gives you access to reviews, subscription data, and stats for apps under your developer account, and that is where it stops.

There is no official endpoint to fetch a competitor’s app detail page, browse a category, or run a keyword search and collect the results. The Developer Reporting API has a default limit of 10 queries per second, and even at that ceiling the data returned covers only your own apps. Google’s APIs Terms of Service also prohibit using its API endpoints to scrape or build a database of Google content, so the official route is closed for market intelligence, competitive monitoring, or category research.

The gap is wide. Google Play hosts roughly 1.85 million Android apps as of June 2026, and AppBrain’s live statistics recorded about 64,500 new apps launched in May 2026. A team tracking competitors or building an app recommendation engine needs to read those public listing pages at scale, which means one of three routes: an open-source library you host yourself, a headless browser scraper with residential proxy rotation, or a managed API that handles all of that automatically.

The open-source route is worth knowing about because it shapes the whole market. The most popular library, google-play-scraper, installs from npm (npm install google-play-scraper), reads the public store directly in Node.js, and exposes app, list, search, reviews, and permissions methods, with a widely used Python port as well. It is free, and it carries a cost the managed tools absorb: the maintainer notes the project is no longer actively maintained and warns to “expect the parser to break when Google Play’s layout changes.” Running it at volume also means respecting Google Play’s own rate limit and supplying your own proxies. Parser maintenance is the recurring tax on every scraper that reads the public HTML, so I tracked who carries it for you.

That is the problem every tool in this list is trying to solve, and why success rate on a live Play Store target was the primary metric I ranked them on.

What Android app data is worth extracting

The Android app data worth extracting from public Google Play listings falls into a rich set of structured fields, and I scored each tool on how many it returned cleanly in a single call.

• App metadata: name, package ID, developer, category, content rating, last-updated date. The baseline fields every tool returns.
• Ratings and reviews: aggregate score (0-5), total review count, the rating histogram (the 1-to-5 star breakdown), and individual review text with star ratings. Critical for sentiment tracking and competitive benchmarking.
• Install range: Google reports installs as a banded range (“10M+” or “500K+”). Exact install counts stay private, so a good scraper parses the band correctly.
• Pricing: free or paid, with the price, currency, and in-app purchase ranges when applicable. Useful for market pricing research.
• Screenshots and store art: image URLs for visual analysis or app-store creative benchmarking.
• Search results and category listings: app rankings for a given keyword or category, the most valuable data for ASO (App Store Optimization) work.

A scraper that returns metadata but misses install ranges or drops the review count is only partially useful for competitive analysis. I weighted data completeness alongside success rate.

For a full breakdown of what each endpoint covers, see the Android App Data Scraper API and Google Play Store Scraper API pages.

The 6 best Android app data scrapers in 2026

1. ChocoData - best overall

ChocoData was the best overall Android app data scraper in my testing, returning fully parsed JSON at a 97% success rate on live Google Play targets without any proxy configuration on my side. It was the only tool where I sent a package ID and got back clean, structured metadata on the first try every time but a handful across several hundred requests. Median response time sat around 2.6 seconds end to end, including proxy routing, anti-bot, and parsing.

The data completeness was also the highest I measured. In my runs it returned app name, developer, description, category, aggregate rating, review count, install range, pricing, content rating, screenshots, and last-updated date as a single structured object. No additional parsing step needed on my side.

What it returns. In my runs the app-detail endpoint returned: app_id, title, developer, category, rating (float), rating_count, installs (banded string), price, currency, content_rating, updated_at, description, and an array of screenshots. Reviews come back from a separate endpoint with author, rating, text, and date.

The sample request from the Android App Data Scraper API page:

curl "https://chocodata.com/api/v1/appstore/app?id=389801252&api_key=$CHOCO_API_KEY"

A Python version for pulling a batch of package IDs:

import requests, os

API_KEY = os.environ["CHOCO_API_KEY"]
BASE = "https://chocodata.com/api/v1/appstore"

def get_app(package_id: str) -> dict:
    resp = requests.get(
        f"{BASE}/app",
        params={"id": package_id, "api_key": API_KEY},
        timeout=15,
    )
    resp.raise_for_status()
    return resp.json()

# Pull three competitor apps
apps = [get_app(pid) for pid in ["com.spotify.music", "com.netflix.mediaclient", "com.instagram.android"]]
for app in apps:
    print(app["title"], app["rating"], app["installs"])

In my test run this returned clean JSON for all three package IDs in under 3 seconds each. No proxy setup, no cookie management, no UA rotation.

Pricing. Pro plan works out to about $0.60 per 1,000 records ($49/month for 82,000 requests). Free plan covers 1,000 requests. Pay-as-you-go top-ups at $0.90 per 1,000. The high success rate meant fewer retries, so effective cost per usable record was among the lowest here.

Best for. Teams that want Google Play app data as structured JSON and do not want to own proxy rotation or anti-bot infrastructure.

Get started with ChocoData free

2. Apify - best actor-based option

Apify was the strongest option for teams that want to control the scraping logic directly. The Apify Store has several maintained Google Play actors, and I hit a 91% success rate in my testing using the most popular one. The platform is flexible, and that flexibility means more setup: you configure inputs, pick an actor, and model the per-event cost yourself.

What it returns. App metadata and review data as JSON or CSV, with the exact shape depending on which actor you run. The well-maintained actors returned solid metadata; older community actors had gaps in fields like install ranges.

Pricing. Apify’s plans layer a platform subscription (a Free tier with $5 of monthly credit, then Starter at $29/month) under per-event actor billing. A popular Google Play actor charges around $1 per 1,000 apps returned plus a small per-run start fee, and reviews count toward the total and multiply with your maxReviews setting. App-details runs are cheap; review-heavy runs cost more, so a test run before committing volume is worth the time.

Best for. Developers who want to control and extend the scraping logic and are comfortable configuring actors and modeling the per-event cost.

3. Bright Data - best for the largest pulls

Bright Data was the best fit for large-scale, ongoing collection jobs. It hit a 92% success rate in my testing and is backed by one of the largest residential proxy networks available. The technology is excellent at scale, and it feels proportionally heavy for small jobs.

What it returns. Structured datasets through its Web Scraper product or raw responses when driving its proxies directly. Both routes returned solid app metadata; I did a bit of my own parsing for some edge-case fields.

Pricing. Bright Data’s public Google Play scraper pricing lists pay-as-you-go at $1.5 per 1,000 records and a Scale plan at $499/month for 384,000 records (then $1.3 per 1,000). Blended against the volume I ran, my effective cost landed around $0.70 per 1,000 records. Value improves substantially at committed scale, and there is a free tier of 5,000 records per month to test on.

Best for. Large, ongoing Android app data collection where proxy depth and throughput matter more than setup simplicity.

4. Oxylabs - best for enterprise SLAs

Oxylabs was the best choice when a contract and named support matter more than self-serve speed. I hit an 89% success rate in my testing, which is solid if below the top two. The technology is comparable to Bright Data; the packaging is more enterprise-oriented, with a sales-led onboarding path.

What it returns. Structured results through its Scraper API, with reliable app metadata and serviceable field coverage. Output shape is clean and well documented.

Pricing. Oxylabs publishes a pay-per-result rate starting at $0.25 per 1,000 results without JavaScript rendering and $1.35 per 1,000 with rendering, billed only for successful results. Play Store detail pages need rendering, so my effective cost sat around $0.75 per 1,000 records, with better rates under contract. A free trial covers up to 2,000 results. Best value appears at committed enterprise volume.

Best for. Organizations that need a formal contract, an SLA, and dedicated account management.

5. ScrapingBee - best for simple projects

ScrapingBee was the easiest to start with for a simple project. One clean endpoint, good docs, and an 87% success rate in my testing. It is a general-purpose scraper without Google Play-specific features, so I parsed the returned HTML myself for most fields.

What it returns. Rendered HTML or, with extraction rules, basic JSON. App name and rating came back reliably. Install ranges and content rating needed manual parsing, and I had to write my own extractor for the review section.

Pricing. ScrapingBee’s Freelance plan is $49/month for 250,000 API credits, and a basic request costs one credit. Play Store pages need JavaScript rendering, which costs extra credits per request, so my effective rate worked out to roughly $0.50 per 1,000 records once rendering was factored in. New accounts get 1,000 free credits with no card required.

Best for. Small projects where a generic, easy-to-integrate endpoint beats a Play Store-specific feature set.

6. Octoparse - best for no-code teams

Octoparse was the best fit for teams that want a visual, no-code approach to collecting Android app data from Google Play. I hit an 84% success rate in my testing, lower than the API-first tools, and throughput was the weakest here. For teams without engineering resources it is the most accessible option.

What it returns. App metadata extracted through a visual point-and-click interface. Output as Excel, CSV, or JSON. Field coverage is good when configured correctly, but initial setup takes longer than an API call.

Pricing. Octoparse’s Standard plan is $69/month billed annually (Professional is $249/month), and there is a free-forever tier with 10 tasks and 50,000 rows of monthly export. Per-record cost is hard to pin down because it depends on task setup and cloud run size; blended across my runs it landed near $0.65 per 1,000 records.

Best for. Product managers and analysts who need Android app data without writing any code.

Comparison table

Full feature matrix from my testing, with every tool matched against the criteria that matter most for Android app data collection.

What teams use Android app data for

Teams pulling Google Play data are usually doing one of four things, and the use case determines what volume and fields you actually need.

• Competitive benchmarking: tracking competitor ratings, review counts, and install growth over time. This is the most common use I see, and it runs continuously - a few hundred package IDs pulled daily against a stored baseline. The success rate matters most here because missed requests create gaps in the trend line.

• App Store Optimization (ASO): monitoring keyword rankings, category positions, and featured placement. This requires search and category scraping in addition to app-detail data. For a deeper look at the search side, see my guide on the App Store Search Scraper API.

• Market research and investment signals: building signals from the install-range trajectory, rating velocity, and review sentiment of apps in a category. Hedge funds and analysts use this kind of data to track ecosystem shifts before financial results are public. The Google Play Store Scraper API covers the full category-level data for this.

• Review monitoring and NLP: collecting customer reviews at scale to feed sentiment models or surface product feedback. This needs the dedicated reviews endpoint, since app-detail metadata alone returns only the aggregate score. See the Google Play Reviews Scraper API for that endpoint.

Google Play’s scale makes the data particularly valuable here. With around 1.85 million apps indexed on Google Play as of June 2026 and tens of thousands of new listings appearing every month, the dataset is large enough to support real statistical analysis of market trends.

How to choose

The decision comes down to three variables: volume, code ownership, and budget.

If you want the fastest path to structured JSON with no infrastructure work, a managed API like ChocoData was the cleanest in my testing. You send a package ID or a search query and get back a complete, structured object. No proxy pool to build, no anti-bot to debug. The free plan at ChocoData covers 1,000 requests to validate your pipeline before spending anything.

If you want to control the scraping logic, Apify’s actor model gives you that. You can fork a maintained actor, modify the extraction rules, and run it on Apify’s infrastructure. Best for teams with engineering resources who want the extraction layer to be theirs.

If volume is the primary constraint (tens of millions of records, ongoing), Bright Data’s proxy network depth pays off at that scale. The per-record rate drops substantially at committed volume.

If a contract and SLA are the requirement, Oxylabs is the most straightforward path to a formal enterprise agreement.

If you have no developer on the team, Octoparse’s visual interface is the most accessible entry point, accepting a slower throughput and lower success rate in exchange for point-and-click configuration.

The one pattern I would avoid is building your own residential proxy pool from scratch to scrape Play Store at volume. The operational cost grows quickly - IP refresh cycles, CAPTCHA handling, session management - and managed APIs have already solved all of it. For most teams the time cost of owning that layer outweighs any per-record savings, which is the same conclusion I reached in my guide on how to scrape App Store data.

For a broader look at both Android and iOS scraping options, see my comparison of the best App Store scrapers in 2026.