Similar App Store Applications Finder

Find similar iOS App Store applications from seed app URLs, app IDs, or keyword-discovered seed apps.

Use this actor to build ASO competitor maps, discover adjacent apps, compare categories, and monitor app-market neighborhoods without logging in or managing API keys.

What does Similar App Store Applications Finder do?

Similar App Store Applications Finder turns one or more iOS App Store seed apps into a ranked dataset of related applications.

It combines public Apple App Store pages with iTunes Search and Lookup JSON endpoints.

For each seed app, it extracts candidates from App Store shelves such as “You Might Also Like” and, optionally, same-developer sections.

When shelves are short, it can fall back to iTunes Search using the seed app name, genre, and your extra fallback terms.

The output is normalized into one dataset with seed context, source section, ranking, app metadata, ratings, prices, URLs, artwork, version details, and scrape timestamps.

Who is it for?

• 📈 ASO teams researching competing apps around a seed application.
• 🧭 App marketers building market maps for a category or niche.
• 🧪 Product managers validating adjacent products before roadmap planning.
• 💼 Investors scanning similar apps for app-store due diligence.
• 🧰 Agencies creating recurring competitor discovery reports for clients.
• 📊 Data teams enriching app databases with related-app signals.

Why use this actor?

Apple's public pages are useful, but manually clicking every related app is slow and hard to repeat.

This actor makes the workflow repeatable and exportable.

You can run it from the Apify Console, API, scheduler, webhooks, or MCP.

The dataset is ready for spreadsheets, BI dashboards, data warehouses, and downstream enrichment actors.

How it works

1. Provide App Store app URLs, numeric app IDs, search terms, or a mix.
2. The actor resolves seed apps with the iTunes Lookup/Search endpoints.
3. It downloads each public App Store page over HTTP.
4. It parses related-app and same-developer shelves when present.
5. It expands short shelves with optional search fallback terms.
6. It looks up candidate app metadata in batches.
7. It emits one normalized record per similar app per seed.

Input options

• appUrls — App Store URLs, for example https://apps.apple.com/us/app/spotify-music-and-podcasts/id324684580.
• appIds — Numeric iOS app IDs such as 324684580.
• searchTerms — Terms used to discover seed apps through iTunes Search.
• country — Two-letter App Store country code such as us, gb, or de.
• maxSeeds — Maximum seed apps to process.
• maxResultsPerSeed — Maximum similar apps emitted per seed.
• includeSameDeveloper — Include “More by this developer” apps.
• includeSearchFallback — Use search fallback when page shelves are short.
• fallbackSearchTerms — Extra market/category terms used during fallback.

Example input

{
  "appUrls": [
    { "url": "https://apps.apple.com/us/app/spotify-music-and-podcasts/id324684580" },
    { "url": "https://apps.apple.com/us/app/duolingo-language-lessons/id570060128" }
  ],
  "country": "us",
  "maxSeeds": 2,
  "maxResultsPerSeed": 50,
  "includeSameDeveloper": true,
  "includeSearchFallback": true,
  "fallbackSearchTerms": ["music streaming", "language learning"]
}

Output data

Each dataset item is one related app for one seed app.

Field	Description
seedAppId	Numeric ID of the seed app.
seedAppName	Seed app name.
seedDeveloperName	Seed app developer.
seedUrl	App Store URL for the seed.
country	App Store country used for the run.
sourceSection	Discovery source such as you_might_also_like, same_developer, or search:<term>.
rank	Rank within the seed/source candidate list.
appId	Numeric ID of the similar app.
trackName	Similar app name.
developerName	Similar app developer.
developerId	Apple developer ID.
bundleId	iOS bundle ID.
primaryGenreName	Primary App Store genre.
genres	Genre list returned by Apple.
averageUserRating	Average user rating when available.
userRatingCount	Rating count when available.
price	Numeric price.
formattedPrice	Display price such as Free.
currency	Currency code.
isFree	Whether the app is free to download.
trackViewUrl	App Store URL.
artworkUrl	High-resolution artwork URL.
descriptionSnippet	Short app description snippet.
version	Current version.
releaseDate	First release timestamp.
currentVersionReleaseDate	Current version release timestamp.
contentAdvisoryRating	App Store age rating.
sellerName	Seller/legal entity.
minimumOsVersion	Minimum iOS version.
scrapedAt	Actor scrape timestamp.

How much does it cost to find similar App Store applications?

This actor uses pay-per-event pricing.

There is a small start charge per run and a per-item charge for every similar app record produced.

Current baseline pricing before volume discounts:

• Start: $0.005 per run.
• Similar app item: formula-derived tiered price from $0.000026195 on the free tier, $0.000022778 on Bronze, and lower prices on higher Apify plans.

Because the actor uses public HTTP endpoints and no browser automation, compute costs are typically low.

Use maxSeeds and maxResultsPerSeed to control dataset size.

Tips for better results

• Use real App Store URLs when you know the exact seed app.
• Use searchTerms when you want to start from a market niche instead of a known app.
• Keep includeSearchFallback enabled for larger competitor maps.
• Turn off includeSameDeveloper when you only want broader competitors, not portfolio neighbors.
• Set the country to the market you actually analyze because App Store rankings and availability vary by country.
• Add category-specific fallbackSearchTerms such as habit tracker, invoice maker, or meal planner.

Common workflows

ASO competitor map

Start with your own app URL.

Set maxResultsPerSeed to 100.

Export the dataset to CSV and group by genre, rating count, and developer.

Investor app-market scan

Start with several leading apps in a category.

Keep same-developer apps enabled.

Use output fields such as rating count, rating score, seller, genre, and release dates for a first-pass market map.

Agency monitoring

Save one Apify task per client app.

Run it weekly.

Compare new related apps against previous datasets in your BI tool or database.

Integrations

• Send dataset items to Google Sheets for lightweight ASO research.
• Connect Apify webhooks to Slack when new runs finish.
• Store app IDs and URLs in BigQuery, Snowflake, or PostgreSQL.
• Chain the results into automation-lab App Store detail, reviews, keyword, or chart actors when those are useful for your workflow.

API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('automation-lab/similar-app-store-applications-finder').call({
  appIds: ['324684580'],
  country: 'us',
  maxSeeds: 1,
  maxResultsPerSeed: 50,
});
console.log(run.defaultDatasetId);

Python

from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('automation-lab/similar-app-store-applications-finder').call(run_input={
    'appIds': ['324684580'],
    'country': 'us',
    'maxSeeds': 1,
    'maxResultsPerSeed': 50,
})
print(run['defaultDatasetId'])

cURL

curl -X POST "https://api.apify.com/v2/acts/automation-lab~similar-app-store-applications-finder/runs?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"appIds":["324684580"],"country":"us","maxResultsPerSeed":50}'

MCP usage

Use the actor from Claude Desktop, Claude Code, or another MCP-compatible client through Apify MCP.

MCP server URL:

https://mcp.apify.com/?tools=automation-lab/similar-app-store-applications-finder

Claude Code setup:

$claude mcp add apify-similar-apps "https://mcp.apify.com/?tools=automation-lab/similar-app-store-applications-finder"

Claude Desktop JSON config:

{
  "mcpServers": {
    "apify-similar-apps": {
      "url": "https://mcp.apify.com/?tools=automation-lab/similar-app-store-applications-finder"
    }
  }
}

Example prompts:

• “Find similar App Store apps for Spotify in the US and summarize the top music competitors.”
• “Run the Similar App Store Applications Finder for this app ID and return apps with more than 100k ratings.”
• “Build a competitor table for these three language-learning apps.”

Legality

This actor uses public App Store pages and public iTunes endpoints.

It does not log in, bypass paywalls, or collect private user data.

You are responsible for using the results in compliance with applicable laws, Apple terms, and your own data policies.

FAQ

Why are there fewer results than my limit?

Apple may expose only a small related-app shelf for some seed apps.

Keep search fallback enabled and add relevant fallback terms to increase coverage.

Why do country results differ?

App availability, localization, categories, and App Store shelves can vary by country.

Run separate tasks for each market you monitor.

Can I use Android apps?

No.

This actor targets Apple's iOS App Store.

Use a Google Play actor for Android workflows.

Related automation-lab actors

• https://apify.com/automation-lab/app-store-reviews-scraper
• https://apify.com/automation-lab/apple-app-store-scraper
• https://apify.com/automation-lab/app-store-keywords-discovery-tool
• https://apify.com/automation-lab/google-play-scraper

Limitations

• The actor depends on public Apple endpoints and page markup.
• Some pages expose fewer related apps than others.
• Ratings and pricing are returned by Apple's country-scoped endpoints and may differ across markets.
• The actor does not install, open, or interact with apps.

Changelog

• 0.1 — Initial private build for seed-app similar application discovery.