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
}

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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.