Apple App Store Scraper — Metadata & Reviews | $2/1K | No Proxy

ASO managers, indie developers, and market researchers use this actor to monitor competitor ratings, track review sentiment across countries, and build app datasets for NLP pipelines.

Pricing: $2.00 per 1,000 results (app metadata + reviews). No proxy costs — Apple's official iTunes API.

No API key. No proxy. No auth. Zero setup friction.

Extract Apple App Store app details and customer reviews with a single actor. Uses Apple's official iTunes public API — no authentication, no proxies, zero extra cost.

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What you get

Each output record is a flat, typed row. Two record types share the same schema:

Field	Type	Description
record_type	string	"app_meta" or "review"
app_id	string	Numeric Apple App Store ID
app_name	string	App display name
developer	string	Developer / publisher name
category	string	Primary category (Music, Productivity…)
price	number	Price in local currency (0 = free)
rating_avg	number	All-time average user rating
rating_count	number	Total ratings count
version	string	Current (or reviewed) app version
country	string	App Store country code
review_id	string	Review ID (review records only)
review_title	string	Review title
review_text	string	Review body text
review_rating	integer	1–5 star rating
review_author	string	Reviewer username
review_date	string	Review date (ISO 8601)
parse_confidence	number	0.0–1.0 data quality score
warnings	array	List of missing/degraded field codes
scraped_at	string	Run timestamp (ISO 8601 UTC)

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Sample output

{
  "record_type": "app_meta",
  "app_id": "324684580",
  "app_name": "Spotify: Music and Podcasts",
  "developer": "Spotify AB",
  "category": "Music",
  "rating_avg": 4.8,
  "rating_count": 11500000,
  "price": 0,
  "version": "8.9.62",
  "parse_confidence": 1.0
}

{
  "record_type": "review",
  "app_id": "324684580",
  "review_title": "Best music app",
  "review_text": "Absolutely love this app, UI is smooth...",
  "review_rating": 5,
  "review_author": "JohnD",
  "review_date": "2026-05-30T12:00:00Z",
  "parse_confidence": 1.0
}

---

Input options

Parameter	Default	Description
appIds	["324684580"]	Numeric Apple app IDs
searchTerms	[]	Keyword queries (up to 200 results each)
country	"us"	Two-letter App Store storefront code
maxReviews	50	Reviews per app (max 500, 0 = all available)
includeReviews	true	Fetch customer reviews
sortBy	"mostRecent"	"mostRecent" or "mostHelpful"
maxSearchResults	50	Max apps returned per search term

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Why this scraper beats the competition

• Unified schema — app metadata and reviews in one flat record. No post-join needed.
• parse_confidence — every record reports its own data quality (0.0–1.0). No competitor offers this.
• Search + direct ID — one actor covers both use cases.
• Zero COGS — Apple's official public iTunes API, no proxy required.
• Flat typed output — 19 well-named fields, no nested blobs.

Feature	This actor	apify/itunes-scraper	bebity/apple-app-store-scraper
parse_confidence score	✅	❌	❌
Search + direct ID in one actor	✅	partial	✅
Review sentiment by country	✅	❌	partial
Price	$2/1K	$3.50/1K	$3/1K

(Verify competitor prices at publish time — adjust table accordingly.)

parse_confidence — every record carries a 0.0–1.0 data quality score. If Apple updates their API response shape, confidence drops before results go silently empty. Score < 0.5 → check the warnings field.

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Pricing

Pay per result (app-data-item event). $2.00 per 1,000 items — competitive with the market rate.

Apple's RSS feed is capped at 500 reviews per app per country. Each app lookup is one result; each review is one result.

Worked example:

• 10 apps + 50 reviews each = 10 + 500 = 510 items → $1.02
• 100 apps (metadata only, no reviews) = 100 items → $0.20
• 1 app with 500 reviews (full RSS cap) = 501 items → $1.00

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FAQ

Do I need an API key or proxy? No. This actor uses Apple's official public iTunes API — no auth, no proxy, zero setup.

What formats can I export? JSON, CSV, Excel, or JSONL — directly from the Apify dataset UI or via the REST API.

Can I schedule it to run automatically? Yes. Use Apify Scheduler to run daily/weekly. Pair with a webhook to Slack or email to alert on rating changes.

What if it returns no results? The warnings field explains missing data. If parse_confidence < 0.5, Apple may have changed their API format — check the warnings and file an issue. App IDs not found in the selected country will be skipped with a warning in the run log.

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Common use cases

• ASO (App Store Optimization) — track competitor ratings and review trends
• Market research — discover apps in a category, compare developer portfolios
• Sentiment analysis — feed review text into NLP pipelines
• Competitive intelligence — monitor rating changes and user feedback for rival apps
• App monitoring — daily review ingestion with parse_confidence alerts

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Technical details

• Endpoints used:
  • iTunes Lookup API: https://itunes.apple.com/lookup?id={appId}&country={country}
  • iTunes Search API: https://itunes.apple.com/search?term=...&entity=software
  • Reviews RSS (JSON): Apple's customer reviews RSS feed (10 pages × 50 reviews)
• No authentication — all endpoints are Apple's public APIs
• No proxy — no CAPTCHA, no IP blocks on these endpoints
• Stack: Python 3.13 + Apify SDK v2

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Use with AI agents (MCP)

This actor is registered in the Apify MCP server. Connect it to Claude, GPT-4, or n8n to let your AI agent pull live App Store data on demand — no manual runs needed.

MCP config: https://mcp.apify.com/?tools=bovi/app-store-scraper

Also in this suite

• Google Play Store Scraper — same schema, Android ecosystem
• App Store Reviews Scraper — reviews-only, bulk NLP use case
• App Store Charts Scraper — market surveillance, top charts
• ASO Keyword Rank Tracker — ranking intelligence, 10× cheaper than Sensor Tower
• iOS App Update Tracker — version monitoring, change detection
• F-Droid Scraper — FOSS/privacy research

Limitations

• Reviews capped at 500 per app per country (Apple RSS hard limit)
• Search returns up to 200 apps per query (Apple API limit)
• Historical reviews beyond the most recent ~500 are not accessible via the public RSS
• Paid app prices reflect the storefront specified by country

Integrations

Built for ASO managers and mobile researchers monitoring competitor app metadata and review sentiment across countries — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• App Update Tracker
• Appstore Charts
• Appstore Reviews
• ASO Keyword Tracker
• Google Play Scraper
• Google Search (SERP) Scraper

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.