kajolshah_bt

We often see apps where hosting on AWS or GCP costs around $200 per month, but outside services like Twilio, Google Maps, or other data providers run more than $3,000 per month.

Free tiers make it easy to ignore efficiency. Common issues we see: - Location checks: The app asks for location updates constantly instead of only when the user moves a set distance. - External data requests: Paid financial or weather data is requested every time a screen loads instead of saving it locally for 15 minutes. - SMS login messages: No limits on messages, so bot attacks generate real bills.

We spend more time fixing these inefficiencies, caching data and limiting outside calls, than we do adding new features. In many cases, using outside services ends up costing more than building a simple in-house solution.

Are developers paying too little attention to ongoing service costs in the push to launch apps quickly?

By the time deletion happens, it’s weeks later.

For people building mobile products: – What usually triggers the mute? – Is it notification volume, relevance, timing… or something else? – Have you found reliable signals before this happens?

Genuinely curious what others have seen.

In practice, it often just speeds up the wrong behavior. The app reacts faster, but to the wrong signals.

Have others seen cases where adding AI made the product feel worse, even if the metrics looked okay at first?

In practice, the failures I’ve seen rarely come from bad models or tools. They come from skipping basic questions: – What decision or task is being improved? – Are rules still working at their current scale? – Is there real usage, or just expectations?

I put together a simple breakdown of how AI projects tend to succeed when they do, and where teams usually go wrong early on.

Curious how others here decide when AI is worth the added complexity and when it’s better to wait.