A caching strategy, in 30 seconds: measure first, then cache whatever gets read a hundred times more than it's written.
Why this matters
Most teams reach for caching backwards, either too early, caching guesses before measuring anything, or too late, buying servers while the same expensive query runs ten thousand times an hour for data that changed once all day.
The common mistake
A cache is a bet that yesterday's answer is still right. Teams get in trouble not by betting, but by never writing down how long they're willing to lose.
How we approach it
The whole strategy: measure to find the hot reads, your profiler will name three or four queries doing most of the damage. Cache those first, query results, rendered fragments, third-party API responses. Set TTLs honestly, a cache is a bet that yesterday's answer is still right, and the TTL is how long you're willing to be wrong. Invalidate on write for anything users edit. And re-measure, because the second bottleneck only appears after the first one dies.
A checklist you can use
Measure first — the profiler names the hot reads
Cache what's read 100x more than it's written
Targets: query results, rendered fragments, API responses
TTL = how long you're willing to be wrong; invalidate on write
Caching buys 10x headroom before infrastructure does
When to bring in help
Run your profiler for one hour today and list the top three reads. Caching those three is usually an afternoon of work and the biggest single speedup your product will get this quarter. If the honest answer is that nobody on the team owns this end to end, that's the moment to borrow the depth rather than improvise it.
Takeaway
Done in this order, caching buys you ten times the headroom before infrastructure spend does, and the app feels faster to every user, not just the ones near your servers.
Building this? Devxhub → devxhub.com
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