KDB.AI vs. Vespa
Compare KDB.AI vs. Vespa by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
KDB.AI vs. Vespa on Scalability
Yes.
Yes.
Neither.
Both
KDB.AI
KDB.AI is a scalable vector database.
Vespa
Vespa is a scalable search engine with a robust distributed architecture that supports horizontal scaling by adding more nodes. It features automatic sharding and data redistribution, allowing it to efficiently manage large datasets and high query volumes.
KDB.AI vs. Vespa on Functionality
Yes (qFlat and qHNSW)
Yes (paged tensor attributes)
Yes. Hybrid Sparse & Dense Search
Yes, vector search & keyword seach
Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.
HNSW, Hybrid HNSW-IF (Inverted File), paged tensor attributes
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.
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
KDB.AI vs. Vespa on Purpose-built
Yes.
Yes.
Python
Python, Java
KDB.AI vs. Vespa: what’s right for me?
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
Vespa
Vespa is a powerful search engine and vector database that can handle multiple searches simultaneously. It's great at vector search, text search, and searching through structured data.
Apache 2.0
The Definitive Guide to Choosing a Vector Database
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