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.
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The workflow
From review noise to a fixable bug list.
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.
- 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.
- 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.
- 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
The difference between a rating and a root cause.
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
Let your agent read reviews the way a PM would.
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.
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. Scale when ready.
Pay per request with no commitment, or lock in a flat monthly rate with built-in savings.
Free plan — 30 requests
Verify responses, inspect the API, no credit card required.
Pay as you go
$1.99
per 1,000 requests
$0.00199 per request · credits never expire
How credits work — no multipliers
One credit = one API call, on every endpoint and platform. No matter how many results come back, you pay for the call, not the output size. Most scraping APIs charge multipliers of 5x, 25x, even 75x per call depending on the target — here it's always 1.
- No multipliers — 1 request = 1 credit
- No monthly commitment
- Credits never expire
- Same API & MCP access as subscribers
- ∞ req/min, ∞ API keys
∞
Expiry
Credits stay until you use them. No monthly reset, no pressure to hit a quota.
0
Commitments
No subscription, no contract, no minimums. Start, pause, or scale whenever.
100%
Access
Every endpoint and MCP included. Not a limited tier — 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.