How can you optimize Redis memory usage for large datasets?

Since Redis keeps data in RAM, memory efficiency directly determines both cost and how much data fits on a given instance, and a handful of concrete techniques typically account for most of the achievable savings.

1. Use compact encodings for small collections — Redis automatically stores small Hashes, Lists, Sets, and Sorted Sets using memory-efficient internal encodings (like listpack) instead of full hash tables, as long as they stay under configurable size thresholds (hash-max-listpack-entries, etc.); keeping collections small where the data model allows it takes advantage of this automatically.
2. Choose Hashes over separate keys — storing related fields in one Hash has meaningfully less per-key overhead than the same data spread across many individual String keys.
3. Use Bitmaps or HyperLogLog for large boolean/cardinality tracking instead of a Set of millions of individual entries, when the exact-membership guarantee a Set provides isn't actually needed.
4. Set appropriate TTLs so stale cache data doesn't accumulate indefinitely and silently consume memory nobody's using.
5. Enable an eviction policy with a maxmemory cap so memory usage has a hard, predictable ceiling rather than growing until the instance runs out of RAM.
6. Analyze actual usage with MEMORY USAGE and redis-cli --bigkeys before optimizing blindly, to find which specific keys or patterns are actually consuming the most memory.