LanceDB vs. KDB.AI
Compare LanceDB 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.
LanceDB vs. KDB.AI on Scalability
Yes.
Yes.
No (static data sharding coming soon)
Neither.
LanceDB
LanceDB is an open-source vector database that's designed to store, manage, query and retrieve embeddings on multi-modal data. LanceDB and its underlying data format, Lance, are built to scale to really large amounts of data (hundreds of terabytes, 200M+ vectors).
KDB.AI
KDB.AI is a scalable vector database.
LanceDB vs. KDB.AI on Functionality
Yes (qFlat and qHNSW)
Yes, vector search & keyword search
Yes. Hybrid Sparse & Dense Search
IVF-PQ, HNSW
(LanceDB adopts a disk-based indexing philosophy.)
Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.
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.
LanceDB vs. KDB.AI on Purpose-built
Yes.
Python, Javascript/Typescript, and Rust
Python
LanceDB vs. KDB.AI: what’s right for me?
LanceDB
LanceDB is an open-source vector database that's designed to store, manage, query and retrieve embeddings on multi-modal data. It also provides a SaaS solution called LanceDB Cloud that runs serverless in the cloud.
Apache 2.0
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
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