How do companies handle API limits in analytics?

They design around the limits: cache and store data rather than re-pulling it, so views are served from a stored copy instead of re-querying the source, which is the biggest lever for staying within caps.

They prioritise which data to fetch, batch requests efficiently, respect the rate-limit rules and refresh on a cadence that balances freshness against limits rather than calling the API on every view.

API limits are a real constraint that makes some data slightly stale, so good systems work within them by being efficient and accepting reasonable lag rather than pretending the limits do not exist or brute-forcing around them.

The general approach is to design around the limits rather than fight them and the foundation is caching and storing data instead of re-pulling it. Platform APIs cap how much data you can request and how often, so a system that re-queries the source every time someone looks at a number burns through limits fast, which is likely what is happening to you. The fix is to pull data once, store your own copy and serve views and analysis from that stored copy, so you are not hitting the API again for data you already have, which dramatically reduces API calls and is the single biggest lever for staying within limits. From there, you refresh the stored data on a sensible schedule rather than constantly, so the API is called to update on a cadence, not on every view. That cache-and-refresh pattern is how most analytics systems handle rate limits and it is the first thing to put in place.

Beyond caching, companies prioritise, batch and accept some lag. Prioritise what to fetch: with limited API budget, you fetch the data that matters most (the creators and metrics you actually use) more frequently and more completely and deprioritise or fetch less frequently the data that matters less, so the limited calls go to the highest-value data. Batch and fetch efficiently: pulling data in efficient batches and requesting only what is needed (rather than everything repeatedly) stretches the available calls further and respecting the API rate-limit rules (spacing requests, handling limit responses gracefully) avoids being throttled or blocked. Accept reasonable staleness: the honest trade-off is that working within API limits means some data is not perfectly real-time, since you are refreshing on a schedule rather than live, so analytics built on capped APIs carry a small lag, which is fine for most decisions as long as you know it is there. The honest framing is that API limits are a real constraint that shapes what is feasible and makes some data slightly stale, so good systems work within them by caching, prioritising and refreshing efficiently and by accepting sensible lag, rather than pretending the limits do not exist or trying to brute-force around them, which just gets you throttled. For your situation, the move is to stop re-pulling live and instead store and refresh on a cadence, prioritise your key data and accept that the stored data is current as of its last refresh rather than to the second. So companies handle API limitations in analytics by caching and storing data rather than re-pulling it, prioritising which data to fetch, batching requests efficiently and refreshing on sensible schedules while accepting some lag, since API limits are a real constraint that makes some data slightly stale and shapes what is feasible, so good systems work within them rather than pretending they do not exist.

Handling API limits is data-engineering work inside your own analytics stack, so the caching, refresh scheduling and rate-limit handling are yours to build and sit well outside what a discovery tool does. The only related point is that a discovery-and-vetting tool like Flinque deals with the same underlying reality (platform data refreshes on a cadence rather than live, which is partly why its audience figures are current-as-of-last-refresh estimates rather than to-the-second), so the slight lag you are designing around is a normal property of platform data broadly, not a flaw in any one system. But the API-limit engineering itself, the caching and scheduling, is your analytics work rather than anything Flinque is involved in. So Flinque is not where API limits are handled and the cache-prioritise-refresh discipline is the data-engineering you apply in your own stack.