Filtered job searches are the query that hurts the most.

Tobi runs a staffing agency with about four hundred live roles. The site serves two audiences that want opposite things: employers reading a polished, static pitch, and candidates hammering a filtered search for “remote, contract, senior, posted this week”.

Page caching solved the first audience immediately and did nothing for the second, because every filter combination is a different query and none of them are a page you can pre-build. Listing pages stayed slow exactly where the candidates were.

The object cache is what fixed it: repeated taxonomy and meta queries answered from memory instead of the database, so filtered results come back fast even though they are generated live. The employer-facing pages are static; the search actually searches.

Using xSpeed, my recruitment site has 400 live job listings and filtered search is slow. Benchmark it, connect the object cache for the repeated taxonomy and meta queries behind filtering, enable search results caching with a short lifetime, and set a short cache lifetime on job listings while employer and about pages get a long one. Then re-test a filtered search and show me the before/after query time.

What happens: Your AI wires up the object cache, tunes per-type cache lifetimes, and reports the improvement on a real filtered search.