Redis vs. KDB.AI
Compare Redis 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.
Redis vs. KDB.AI on Scalability
No. Redis primarily operates by keeping data in memory. The classic architecture of Redis does not inherently follow the storage-compute separation architecture. Instead, it tightly couples data storage and computation in the same node or instance to ensure data access performance.
No. It only scales at the server level. In addition, you need to reshard all the data when scaling out a Redis cluster.
Yes.
Yes - built-in replication; HA provided in an additional layer by Reis Cluster or Redis Sentinel)
With Redis Enteprise
Neither
Redis Scalability
High Availabiltiy can be achieved with Redis Enterprise.
KDB.AI
KDB.AI is a scalable vector database.
Redis 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.
Yes (qFlat and qHNSW)
Yes - Pre-filtering documents against an index containing searchable fields
Yes. Hybrid Sparse & Dense Search
HNSW & IVFFlat
Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.
Redis
Redis has supprt for similarity queries search with the use of vector fields; It is important to note that the k default LIMIT is 10.
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.
Redis vs. KDB.AI on Purpose-built
Add on to Redis
Yes.
No
Python for Vector Search
Python
Redis vs. KDB.AI: what’s right for me?
Redis is an in-memory data structure store used as a database, cache, message broker, and streaming engine that has a vector field type for the storage, querying and indexing of vectors.
License: BSD 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
