How would you choose a cache TTL for data returned by an API?

Choose a TTL and expiration strategy for cached data

Explains how TTL choices balance freshness, load reduction, user expectations, and operational safety.

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TL;DR

I would choose a TTL based on how stale the data is allowed to be and how expensive it is to reload. Data that changes rarely and is not safety-critical can have a longer TTL. Data that changes often or affects permissions, money, inventory, or availability needs a short TTL, explicit invalidation, or no cache at all.

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Freshness tolerance, update frequency, stale data risk, load reduction, TTL jitter, and expiration behavior.

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Core answer
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Why this question matters
TTL looks simple, but it encodes product freshness and system load. It connects caching mechanics to real user behavior.
I would choose a TTL based on how stale the data is allowed to be and how expensive it is to reload. Data that changes rarely and is not safety-critical can have a longer TTL. Data that changes often or affects permissions, money, inventory, or availability needs a short TTL, explicit invalidation, or no cache at all.
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Stronger answer
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I would separate cacheable reference data from data that needs immediate correctness. For example, country lists, public product details, or feature flag snapshots can tolerate short staleness if the product accepts it. Account balances, authorization decisions, and inventory reservations need much tighter rules. I would also add jitter to TTLs for popular keys so many values do not expire at exactly the same second and overload the source.

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Details

Using one default TTL for unrelated data types.
Setting a long TTL for permissions or financial state.
Ignoring synchronized expiration of hot keys.
Changing TTLs without measuring source load or stale reads.

How would you choose a cache TTL for data returned by an API?

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