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

Weaviate vs pgvector vs Qdrant vs Pinecone vs Milvus

5 of 5

Vector Databases · verified 13 Aug 2026

×WeaviateWeaviatecurrent
×pgvectorPostgreSQLcurrent
×QdrantQdrantcurrent
×PineconePineconecurrent
×MilvusZillizcurrent
SpecificationWeaviatepgvectorQdrantPineconeMilvus
SummaryHybrid search with batteries included.Vectors inside the database you already run.The fastest of the purpose-built stores.Managed, and the smoothest to operate.Built for billions of vectors.
IndexHNSWHNSW, IVFFlatHNSWProprietaryHNSW, IVF, DiskANN, GPU
Hybrid searchYes, nativeYes, with SQLYesYesYes
HostingSelf-host and managedSelf-host or any managed PostgresSelf-host and managedManaged onlySelf-host and managed
LicenceBSD-3PostgreSQLApache 2.0ProprietaryApache 2.0
p50 latency~12ms~15ms4ms<10ms~10ms
CategoryVector DatabasesVector DatabasesVector DatabasesVector DatabasesVector Databases
OfficialWeaviatePostgreSQLQdrantPineconeZilliz

Highlighted rows are where these differ.

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 →

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 →