AI tools

Vector databases

4 listings are ranked by release freshness and adoption until verified reviews arrive; then review quality, volume and vendor responsiveness join the score. Open any row to see its receipt. See the formula

  • 3 fresh released in the last 90 days
  • 1 aging no release for 90 to 179 days
  • 0 stale no release for 180 days or more

4 ranked listings

  1. Milvus

    Distributed vector database for storing embeddings and running approximate nearest neighbor search at scale.

    Milvus No reviews yet Open source
    Updated 3 days ago
    1 88.8 =
    Show how this rank is calculated
    Rank receipt: Milvus
    #68 overall · #1 in category
    88.8/100
    1. Release freshness +50.0 100.0 × 50%
    2. Adoption signals +38.9 77.7 × 50%
    Data from GitHub Last sync Computed on Sponsorship weight 0%, never counted Read the full formula
  2. Qdrant

    Vector database written in Rust for similarity search with payload filtering and a REST/gRPC API.

    Qdrant No reviews yet Open source
    Updated 17 days ago
    2 87.8 =
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    Rank receipt: Qdrant
    #85 overall · #2 in category
    87.8/100
    1. Release freshness +50.0 100.0 × 50%
    2. Adoption signals +37.9 75.7 × 50%
    Data from GitHub Last sync Computed on Sponsorship weight 0%, never counted Read the full formula
  3. pgvector

    PostgreSQL extension that adds a vector type, distance operators and indexes for similarity search.

    pgvector No reviews yet Open source
    Updated 54 days ago
    3 82.8 =
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    Rank receipt: pgvector
    #135 overall · #3 in category
    82.8/100
    1. Release freshness +46.4 92.8 × 50%
    2. Adoption signals +36.4 72.7 × 50%
    Data from GitHub Last sync Computed on Sponsorship weight 0%, never counted Read the full formula
  4. Chroma

    Embedding database for AI applications, usable embedded in Python or as a client-server service.

    Chroma No reviews yet Open source
    No release for 139 days
    4 71.0 =
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    Rank receipt: Chroma
    #173 overall · #4 in category
    71.0/100
    1. Release freshness +33.8 67.5 × 50%
    2. Adoption signals +37.2 74.5 × 50%
    Data from GitHub Last sync Computed on Sponsorship weight 0%, never counted Read the full formula