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Pyyan / Compare / Atlas Vector Search vs pgvector vs Qdrant vs Milvus

Atlas Vector Search vs pgvector vs Qdrant vs Milvus

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Vector Databases · verified 13 Aug 2026

×Atlas Vector SearchMongoDBcurrent
×pgvectorPostgreSQLcurrent
×QdrantQdrantcurrent
×MilvusZillizcurrent
1 slot left
SpecificationAtlas Vector SearchpgvectorQdrantMilvus
SummaryVectors beside the documents they came from.Vectors inside the database you already run.The fastest of the purpose-built stores.Built for billions of vectors.
IndexHNSWHNSW, IVFFlatHNSWHNSW, IVF, DiskANN, GPU
Hybrid searchYesYes, with SQLYesYes
HostingManagedSelf-host or any managed PostgresSelf-host and managedSelf-host and managed
LicenceProprietaryPostgreSQLApache 2.0Apache 2.0
p50 latency~20ms~15ms4ms~10ms
CategoryVector DatabasesVector DatabasesVector DatabasesVector Databases
OfficialMongoDB ↗PostgreSQL ↗Qdrant ↗Zilliz ↗

Highlighted rows are where these differ.

Atlas Vector Search

  • No second datastore to keep in sync
  • Same argument as pgvector, different database

Best for teams already on MongoDB.

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 →