Pyyan / Compare / pgvector vs Qdrant vs Pinecone vs Milvus vs Weaviate

pgvector vs Qdrant vs Pinecone vs Milvus vs Weaviate

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Vector Databases · verified 13 Aug 2026

×pgvectorPostgreSQLcurrent
×QdrantQdrantcurrent
×PineconePineconecurrent
×MilvusZillizcurrent
×WeaviateWeaviatecurrent
SpecificationpgvectorQdrantPineconeMilvusWeaviate
SummaryVectors inside the database you already run.The fastest of the purpose-built stores.Managed, and the smoothest to operate.Built for billions of vectors.Hybrid search with batteries included.
IndexHNSW, IVFFlatHNSWProprietaryHNSW, IVF, DiskANN, GPUHNSW
Hybrid searchYes, with SQLYesYesYesYes, native
HostingSelf-host or any managed PostgresSelf-host and managedManaged onlySelf-host and managedSelf-host and managed
LicencePostgreSQLApache 2.0ProprietaryApache 2.0BSD-3
p50 latency~15ms4ms<10ms~10ms~12ms
CategoryVector DatabasesVector DatabasesVector DatabasesVector DatabasesVector Databases
OfficialPostgreSQLQdrantPineconeZillizWeaviate

Highlighted rows are where these differ.

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 →

Pinecone

  • Sub-10ms p50 with nothing to maintain
  • Costs draw scrutiny at scale; Notion moved away and cut spend ~60%

Best for teams who do not want to run a database.

Full spec sheet →

Milvus

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

Best for very large corpora.

Full spec sheet →

Weaviate

  • Vector, BM25 and metadata filters in one query
  • Modules for embedding generation built in

Best for hybrid keyword and semantic search.

Full spec sheet →