Similar Apps Finder
Discover the apps Apple links to any app through its “customers also bought” recommendation graph.
Apple maintains a recommendation graph built from real user behavior — the apps people who downloaded one app also downloaded. This finder exposes that graph for any app you search: enter an app and see the neighbors Apple itself considers most closely related, ranked by actual co-install patterns rather than keyword overlap.
That distinction matters. Keyword-based competitor lists tell you who targets the same searches; the similarity graph tells you who shares your actual audience. The two lists rarely match completely, and the apps that appear in the graph but not in your keyword research are competitors (or partners) you did not know you had.
How to find similar apps
- 1
Search for a seed app — your own, or the app whose niche you want to map.
- 2
Review the list of similar apps returned from Apple’s recommendation data.
- 3
Click through to any result to inspect its metadata, then use it as a new seed to walk further out into the niche.
- 4
Collect the recurring names — apps that appear in several apps’ similar lists are the gravitational centers of the category.
Where the “similar apps” data comes from
The results reflect Apple’s own relatedness signals — the same machinery behind the “You Might Also Like” shelf on product pages, driven heavily by co-download behavior across millions of users. Because it is behavioral, it captures relationships that metadata never would: a meditation app and a sleep-sounds app may share few keywords but a large overlapping audience, and the graph will connect them.
This also means the graph updates as user behavior shifts. When a new app breaks into an established niche, it starts appearing in incumbents’ similar lists — often before it shows up in keyword rankings. Checking who has newly entered your app’s neighborhood is a lightweight early-warning system for emerging competition.
Practical uses beyond competitor discovery
The similar-apps graph is a targeting list. For Apple Search Ads, competitors’ brand names sourced from the graph make strong keyword candidates, because the graph proves their audiences convert on apps like yours. For cross-promotion or bundle partnerships, apps adjacent in the graph but not directly competitive — the sleep-sounds app next to your meditation app — are the natural first calls.
It is also a positioning tool before you build. Walking the graph around a niche shows how crowded it truly is, which sub-niches have few strong players, and what the incumbent apps’ shared weaknesses are. Ten minutes of graph-walking often reframes “we compete with the #1 app” into “we compete with these six apps, and two of them are beatable”.
Frequently asked questions
How does Apple decide which apps are similar?
Primarily from user behavior — apps frequently downloaded by the same people — combined with signals like category and genre. It is the data behind the related-apps shelf on App Store product pages, which is why the results reflect real audience overlap rather than just matching keywords.
Why is a direct competitor missing from the results?
The graph is behavioral, so an app only appears if enough users co-install it with your seed app. A new competitor with a small install base, or one whose audience skews to a different country, may target identical keywords yet not surface here. Use keyword-based competitor research alongside the graph to cover both angles.
Can I influence which apps mine appears next to?
Only indirectly. The associations come from who downloads your app, so your acquisition channels shape your neighbors over time. There is no field in App Store Connect to declare related apps — which is exactly why the graph is trustworthy as competitive intelligence.
Do the results differ by country?
Yes. Co-install behavior varies by market, so an app’s similar list in Japan can look quite different from its list in the US. If you are planning a localized launch, checking the graph in the target storefront shows the competitors that matter there specifically.
Is this the same as searching “apps like X” in the App Store?
No — a search for “apps like X” just runs a keyword query and mostly returns apps that put such phrases in their metadata. This finder reads the recommendation relationships directly, which surfaces genuinely related apps regardless of what their metadata says.
Know your rivals’ keywords, not just their names
Once you have mapped the neighborhood, Appalize shows which keywords each of those apps ranks for, where they are climbing, and which of their terms you can realistically take.
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