Redis
Vector search inside Redis: similarity search alongside the cache you already run.
Redis, the in-memory data store, includes vector similarity search. For teams already running Redis as a cache or queue, that means adding retrieval without adding a system, and getting very low query latency from memory.
It is best suited to working sets that fit comfortably in RAM, where its speed is hard to beat.
Where it fits
A fit when you already run Redis and want low-latency vector search without operating a separate database.
Where it does not fit
Holding everything in memory gets expensive for large vector sets: a disk-based store scales more cheaply.
Alternatives to Redis
The closest options on the map, and where each fits instead.
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pgvector Embedded vector search
The right call when your data already lives in Postgres and the vector workload is moderate: retrieval without a second database.
Unlike Redis Open sourceNo hosted APIMaturity: established
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Qdrant Dedicated vector database
A strong pick when retrieval needs to combine similarity with metadata filtering, or has outgrown an in-database extension.
Unlike Redis Open sourceMaturity: established
Pairs with
Commonly used alongside it.
