Analyze App Store Reviews with AI | FetchLayer
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App Store review analysis

Turn App Store reviews into product decisions.

Retrieve public app reviews as structured JSON, then use your own code or AI agent to identify what users praise, what is breaking, and what they ask for next. Keep the underlying review evidence behind every insight.

The workflow

Go from scattered feedback to a clear next step.

FetchLayer gives you the public data in a predictable shape. Your code, automation, or AI agent can turn it into an ongoing source of customer insight.

  1. 01

    Collect the right review set

    Fetch the public reviews for your app or a competitor, with the country, language, platform, sorting, and pages relevant to the question.

  2. 02

    Preserve the review evidence

    Store rating, review text, app version, date, and source fields so a theme can always be checked against the original feedback.

  3. 03

    Ask a focused question

    Use an LLM or your own analysis to group themes, find changes after a release, and turn the highest-signal feedback into a prioritized brief.

What to surface

Raw feedback becomes useful when it is specific.

Bugs and regressions

Find the issues users mention most often and connect them to the app version where the complaint appears.

Feature requests

Separate explicit requests from general dissatisfaction and identify the product capabilities customers describe repeatedly.

Competitor gaps

Read public competitor feedback to find pain points that your product can solve or avoid.

Regional differences

Compare feedback across countries and languages instead of treating every market as one undifferentiated review pool.

What users value

Use positive reviews to identify differentiators, wording, and outcomes that customers already care about.

Release feedback

Track newly retrieved reviews around a release to spot whether a new change is helping or creating friction.

AI-ready input

Give your agent the raw feedback and a useful question.

Use FetchLayer through REST or MCP. The important part is keeping the original feedback alongside the summary so your team can inspect the evidence behind every conclusion.

agent prompt

Read these App Store reviews. Group the feedback into bugs, feature requests, and pricing objections. For each theme, show the review count, representative quotes, and the app versions mentioned.

The highest-confidence issue is failed background sync after version 4.2. The strongest feature request is offline access. Keep the cited reviews in the output so product can verify every conclusion.

Pricing

Start free. Pay for what you use, or lock in a flat rate.

Credits for flexible, no-commitment usage. Subscriptions for a fixed monthly rate with built-in savings. Subscriptions+ for teams that want volume pricing at scale.

Free plan

30 free requests

A small test drive to verify the API, inspect real responses, and decide whether you want to stay on credits or move to a subscription.

Try free

Pay as you go

$1.99 per 1,000 requests

$0.00199 per request

How credits are counted

One credit = one request. No matter how many results come back — replies, comments, search pages — you pay for the call, not the output size.

Your first successful payment unlocks unlimited API keys on the account, including pay-as-you-go credits.

  • No monthly commitment — buy only what you need
  • Credits never expire — they stay until you use them
  • Same API, same MCP access, same data quality
  • ∞ req/min
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Credits never expire

Buy once, use whenever. There's no monthly reset, no pressure to hit a quota, no wasted credits at the end of a billing cycle.

No strings attached

No subscription to cancel, no seat minimums, no contract to negotiate. Start, pause, or scale whenever you want.

Full access from credit one

Every endpoint, MCP included, is available on credits. You're not on a limited tier — you get the same data as any subscriber.

FAQ

Common questions.

Does FetchLayer perform sentiment analysis itself?

FetchLayer retrieves structured public App Store review data. You can analyze it with your own code, analytics stack, or an LLM through REST or MCP.

What can I identify from app reviews?

Common workflows identify recurring bugs, feature requests, product friction, pricing objections, positive differentiators, and differences between app versions or countries.

Can I analyze competitor feedback?

Yes. Public App Store reviews are a valuable source for competitor research, product discovery, and understanding the pain points users already articulate.

Start with the public feedback.

Retrieve the source data, keep the evidence, and build the feedback workflow your team actually needs.