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Pyyan / Compare / Vertex AI Vector Search vs pgvector vs Qdrant

Vertex AI Vector Search vs pgvector vs Qdrant

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Vector Databases · verified 13 Aug 2026

×Vertex AI Vector SearchGooglecurrent
×pgvectorPostgreSQLcurrent
×QdrantQdrantcurrent
2 slots left
SpecificationVertex AI Vector SearchpgvectorQdrant
SummaryGoogle's own, built on ScaNN.Vectors inside the database you already run.The fastest of the purpose-built stores.
IndexScaNNHNSW, IVFFlatHNSW
Hybrid searchYesYes, with SQLYes
HostingManagedSelf-host or any managed PostgresSelf-host and managed
LicenceProprietaryPostgreSQLApache 2.0
p50 latency~15ms~15ms4ms
CategoryVector DatabasesVector DatabasesVector Databases
OfficialGoogle ↗PostgreSQL ↗Qdrant ↗

Highlighted rows are where these differ.

Vertex AI Vector Search

  • The research behind it powers Google's own retrieval
  • Tied to the platform

Best for workloads already on Google Cloud.

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 →