Comparisons
Pgvector vs KDB.AI

Pgvector vs. KDB.AI

Compare Pgvector vs. KDB.AI by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.

Pgvector vs. KDB.AI on Scalability

PgvectorKDB.AI

Separation of storage and compute

Yes. pgvector enables separation of storage and compute by allowing you to store your application data on one database while storing vectors, lookup values, and filter values on a separate database.

❌

Separation of query and insertions

❌

Yes.

Multi-replication

✔️

❌

Dynamic segment placement vs. static data sharding

❌

Neither.

Cloud-native

❌

Billion-scale vector support

❌

pgvector scalability

You can use a solution like YugaByteDB to extend the capabilities of Postgres for distributed environments.

KDB.AI

KDB.AI is a scalable vector database.

Pgvector vs. KDB.AI on Functionality

Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.

Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.

PgvectorKDB.AI

Role-based Access Control (RBAC)

✔️

❌

Disk Index support

❌

Yes (qFlat and qHNSW)

Hybrid Search (ie Scalar filtering)

Yes. Sparse & Dense Vectors and Scalar filtering.

Yes. Hybrid Sparse & Dense Search

Partitions/namespaces/logical groups

❌

Index type supported

HNSW & IVFFlat

Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.

Multiple in-memory indexes support

❌

KDB.AI

Built by KX, a database provider known for time-series data management, KDB.AI enables developers to bring temporal and semantic context and relevancy to their applications. It supports various search types, including vector similarity search, hybrid sparse and vector search, and Non-Transformed TSS, a similarity search algorithm specific for time series data. It uses Cosine Similarity, Inner Product, and L2 Distance (Euclidean) for similarity metrics.

Pgvector vs. KDB.AI on Purpose-built

PgvectorKDB.AI

Purpose-built for Vectors

pgvector is an add-on to Postgres

Yes.

Tunable consistency

✔️

❌

Support for both stream and batch of vector data

✔️

❌

Binary Vector support

❌

Multi-language SDK

Use pgvector from any language with a Postgres client

Python

Database rollback

✔️

❌

Pgvector vs. KDB.AI: what’s right for me?

Pgvector

pgvector is a PostgreSQL extension designed to facilitate the storage, querying, and indexing of vectors within a PostgreSQL database.

License: PostgreSQL License

KDB.AI

KDB.AI is a powerful knowledge-based vector database and search engine that allows you to build scalable, reliable AI applications using real-time data.

Proprietary license