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

Chroma vs pgvector vs Qdrant vs Pinecone vs Milvus

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Vector Databases · verified 13 Aug 2026

×ChromaChromacurrent
×pgvectorPostgreSQLcurrent
×QdrantQdrantcurrent
×PineconePineconecurrent
×MilvusZillizcurrent
SpecificationChromapgvectorQdrantPineconeMilvus
SummaryThe one you start with.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 searchLimitedYes, with SQLYesYesYes
HostingEmbedded, self-host, managedSelf-host or any managed PostgresSelf-host and managedManaged onlySelf-host and managed
LicenceApache 2.0PostgreSQLApache 2.0ProprietaryApache 2.0
p50 latencyVaries~15ms4ms<10ms~10ms
CategoryVector DatabasesVector DatabasesVector DatabasesVector DatabasesVector Databases
OfficialChromaPostgreSQLQdrantPineconeZilliz

Highlighted rows are where these differ.

Chroma

  • Best developer experience in the category
  • Most teams outgrow it, which is fine

Best for prototypes and local development.

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 →