I wonder how often in the wild people are tuning for a 1% false positive rate versus a much lower one, like .1%. You do quickly reach data set sizes where even 1% introduces some strain on resources or responsiveness.

Cuckoo claims 70% of the size of bloom for the same error rate, and the space is logarithmic to the error rate. Looks like about 6.6 bits per record versus 9.56 bits for bloom at 1%. But at .5% error rate a cuckoo is 7.6 bpr. In fact you can get to about a .13% error rate for a cuckoo only a hair larger than the equivalent bloom filter (n^9.567 = 758.5)

Cuckoo filters can do even better with the small adjustment of using windows instead of buckets. See "3.5-Way Cuckoo Hashing for the Price of 2-and-a-Bit": https://scispace.com/pdf/3-5-way-cuckoo-hashing-for-the-pric.... (This significantly improves load factors rather than changing anything else about the filter, and ends up smaller than the semi-sorted variant for typical configurations, without the rigmarole.)

My fairly niche use case for these kinds of data structures was hardware firewalls running mostly on SRAM, which needed a sub one-in-a-billion false positive rate.