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A free hit-counter endpoint is still a running service. It has versions, access rules, usage quotas, and a lifecycle that can end. On a phone, three more problems stack on top: the connection can vanish in the middle of a request, the app can be suspended at any moment, and a retry can count the same event twice. Most failures fall into one of the categories below, and each leaves a different signal you can read in your code and logs.
Why the request works on Wi-Fi and fails on mobile data
A direct HTTP request needs a usable connection at the exact moment it is sent and until the response returns. A phone can switch from Wi-Fi to cellular, lose signal in a lift or tunnel, or have the app suspended when the user switches away. When that happens, your code may receive no response at all. That silence is the core problem: the app cannot tell whether the server never got the request or got it and the reply was lost.
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Before you change any counting logic, separate the failure types in your client:
- No HTTP response (timeout, dropped connection, no route): the server state is unknown. Treat the event as possibly delivered.
- An HTTP status code arrived: the server answered, and the code tells you what to do next.
- The app was suspended mid-request: the request may be cancelled by the operating system, so do not assume it completed.
Set explicit request timeouts, and log the network type (Wi-Fi, cellular, none) alongside each failure. Without that field, a flaky cellular connection and a rejected request look identical in a dashboard.
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Why did the free API stop working?
Usually because the service changed underneath a client that was never updated. The CounterAPI documentation states that V1 was deprecated and was no longer available as of August 7, 2026, and points to a V1 Endpoints Documentation page for migration notes. Its V2 documentation describes signup as required and a limit of 600 requests per minute per URL path. Those are CounterAPI-specific, dated facts, but the pattern is general: a tutorial that calls an unauthenticated endpoint may be calling a version whose access model has since changed.
Check these before you debug the code:
- Which version is in your base URL, and is that version still listed as current in the provider’s documentation?
- Does the current documentation require signup, an API key, or a token that the old sample did not send?
- Is there a deprecation notice, migration guide, or changelog entry with a date?
- Does the response now return an authentication or “not available” error, rather than a count?
Store the version and the documentation URL you relied on in your project notes. When the provider retires a version, you then know exactly which assumption broke.
What HTTP 429 and 403 mean in your app
HTTP 429 (Too Many Requests) means you have exceeded a rate limit. Google Analytics documentation says that quota overages return either 403 or 429, so a 403 is not always an authorization problem. Read the response body and headers before deciding the key is wrong.
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Limits are scoped differently from service to service. The table below lists the values each cited provider documents, with the scope and the date of the page where they appear. None of these numbers should be treated as a general limit for other APIs.
| Provider and source | Documented limit | Scope | What it means for you |
|---|---|---|---|
| CounterAPI, “Rate Limits” (V2 documentation, accessed 2026) | 600 requests per minute | Per URL path | Applies to V2 only, and V2 also requires signup. Each path has its own budget. |
| Google Analytics API, “Limits and quotas on API requests” (accessed 2026) | 50,000 requests per project per day; 10 queries per second per IP address | Per project; per IP address | Google Analytics limits only. Overages return 403 or 429. |
| Apple, “Identifying Rate Limits” (App Store Connect API) | Illustrative header example: 3,500 requests in a rolling hour, 500 remaining | Rolling hour | Example values from the page, not a guaranteed limit for every Apple endpoint. Read the headers your call actually returns. |
| GitHub Docs, “Rate limits for the REST API” | Not stated in the cited guidance; the guidance relies on reset and Retry-After information | Not stated | Use the reset timestamp or Retry-After value the response provides. |
Handling a 429 without making it worse
Most apps fail here by retrying immediately in a loop, which spends the remaining quota faster and can trigger secondary limits. A safer sequence looks like this:
- Read the status code and headers. Separate quota denial (429, or a quota-specific 403) from invalid requests (400) and authentication errors (401 or 403 with an auth message). Do not retry the last two the same way.
- If the response includes a reset timestamp or a Retry-After value, wait until that time. GitHub’s guidance is to wait for the indicated reset, and to use increasing delays when secondary limits persist.
- If no timing information is provided, use bounded exponential backoff with jitter, and cap the number of attempts.
- Stop tight loops. A counter call inside a screen’s draw or list-scroll path can exhaust a per-minute budget in seconds.
- Where the event still means something if it arrives late, log the failure and queue it. Apple’s documentation gives this pattern as an example: “log the failure and queue the job to be processed again at a later time.”
- Send failures to telemetry with the endpoint path, status code, app version, and network type, so a quota problem shows up as a trend rather than a mystery.
Counting events while the user is offline
Before choosing a mechanism, decide what you are counting. “Page views,” “unique visitors,” “button taps,” and “app opens” are different measures, and a raw counter endpoint does not make them equivalent. Once that is settled, the offline question reduces to three approaches.
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Best-effort send
The app fires a request and moves on. This is the simplest option and it loses every event that occurs without a connection or during a suspension. It is acceptable only when an approximate count is good enough and you know the loss rate for your audience.
Local queue, then send
Each event is written to persistent storage on the device, given a unique identifier at creation, and sent when connectivity and quota allow. The identifier matters because it lets the server recognize a resend (see the retry section below). A hosted counter that only accepts “increment by one” cannot do that, so this approach generally needs a backend you control.
Managed database with offline persistence
Firebase Cloud Firestore supports offline persistence on Android, Apple, and web apps, and synchronizes local changes when the connection returns. When several changes target the same document, the documented rule is last-write-wins. That means a single shared total updated from several offline devices can lose updates. A safer design is one record per event, or a per-device contribution aggregated on the server.
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Platform behavior differs. For web apps, Firestore persistence is disabled by default and depends on the browser. Firebase Realtime Database’s web documentation says a client keeps functioning after network loss, but web data is not persisted offline beyond the session. Do not assume that a Firebase product behaves identically on every platform.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Retries can overcount
A retry is not automatically safe for an increment. Suppose the server accepted the first request but the acknowledgment never reached the phone. Your retry then adds a second increment. The provider documentation cited here does not describe this specific case; it is a general engineering consequence of retrying a non-idempotent operation, and it applies to any counter you do not control end to end.
The usual remedy is a unique event identifier generated once on the device, stored with the queued event, and sent unchanged on every retry. The server records identifiers it has already accepted and ignores repeats. This only works if you own or can modify the receiving side. If the provider’s endpoint accepts a bare increment, you cannot fully prevent double counting from the client.
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Can you put an API key in a mobile app?
Treat any credential shipped inside an app as readable by anyone who downloads it. Renaming a key or moving it into a resource file does not hide it. Firebase is a special case: its documentation states that its API keys identify the project and are not authorization. Access to covered resources is enforced by Firebase Security Rules for Realtime Database, Cloud Firestore, and Cloud Storage, and by Firebase App Check. Firebase’s own wording is that protection of covered APIs is by App Check, “not by keeping your Firebase API key secret.” Firebase still recommends restricting keys and quotas.
That model does not transfer to other vendors. If a third-party key is an actual credential, call that API from a server you control and keep the key there. Check each provider’s documentation to find out which category its key belongs to.
Choosing an approach
The right design depends on which of these trade-offs matters most to your app.
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|---|---|---|---|
| Control and maintenance | Provider controls versions and retirement; you migrate when told | You own versioning, monitoring, and migrations | Provider runs the infrastructure; you own data model and security rules |
| Offline behavior | Requests need a live connection; no local queue unless you build one | Whatever queue you build on the device | Local cache and synchronization on reconnect (Android, Apple, and web, with platform limits) |
| Counting semantics | Raw increments; deduplication depends on the provider | Unique event identifiers and server-side deduplication are possible | Per-event records or server aggregation avoid last-write-wins overwrites |
| Quota visibility | Read from each provider’s headers and documentation | Under your control | Limits defined in the product’s documentation; check current quotas |
| Access control | Anonymous or key-based, depending on the version | Your authentication and server-side secrets | Security Rules and App Check for Firebase resources |
| Operational cost | Not stated in the cited sources; depends on the provider’s plan and your usage | Not stated in the cited sources; depends on your infrastructure and staffing | Not stated in the cited sources; depends on your data volume and reads and writes |
The cited sources do not compare prices or prove that one architecture is cheaper, so the cost row is left for you to measure against your own usage.
The Bottom Line
A free counter endpoint is a dependency with a lifecycle. Pin its version, read its quota headers, and handle 429 with waits rather than loops. If the number matters, record events with unique identifiers on a backend you control, and do not treat a shared client-side total as reliable offline.
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