PyyanThe AI Index

Pyyan / Compare / Redis vs pgvector vs Qdrant vs Milvus

Redis vs pgvector vs Qdrant vs Milvus

4 of 5

Vector Databases · verified 13 Aug 2026

×RedisRediscurrent
×pgvectorPostgreSQLcurrent
×QdrantQdrantcurrent
×MilvusZillizcurrent
1 slot left
SpecificationRedispgvectorQdrantMilvus
SummaryIn-memory, so very fast and RAM-bound.Vectors inside the database you already run.The fastest of the purpose-built stores.Built for billions of vectors.
IndexHNSW, FLATHNSW, IVFFlatHNSWHNSW, IVF, DiskANN, GPU
Hybrid searchYesYes, with SQLYesYes
HostingSelf-host and managedSelf-host or any managed PostgresSelf-host and managedSelf-host and managed
LicenceRSALv2 / SSPLPostgreSQLApache 2.0Apache 2.0
p50 latency5ms~15ms4ms~10ms
CategoryVector DatabasesVector DatabasesVector DatabasesVector Databases
OfficialRedis ↗PostgreSQL ↗Qdrant ↗Zilliz ↗

Highlighted rows are where these differ.

Redis

  • Practical to roughly 10 to 100 million vectors
  • You are paying for RAM, which sets the ceiling

Best for caches and hot vector sets.

Full spec sheet →

pgvector

  • One database for vectors, rows and joins
  • The honest default: reach for something else only when this stops working

Best for almost everyone, until scale says otherwise.

Full spec sheet →

Qdrant

  • Written in Rust; 10 to 25% faster than Weaviate or Milvus on common workloads
  • Strong filtering alongside vector search
  • Self-host or managed, same engine

Best for latency-sensitive retrieval.

Full spec sheet →

Milvus

  • Distributed architecture with GPU index support
  • Heavier to operate than the alternatives

Best for very large corpora.

Full spec sheet →