MongoDB Atlas vs. KDB.AI
Compare MongoDB Atlas 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.
MongoDB Atlas vs. KDB.AI on Scalability
Yes. Atlas introduced search nodes, providing dedicated infrastructure for Atlas search and vector search workloads.
Yes.
Yes. Atlas can dynamically balance the data between shards via range migrations.
Neither
KDB.AI
KDB.AI is a scalable vector database.
MongoDB Atlas 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 using an MQL match experssion that compares an indexed field with boolean, number, or string.
Yes. Hybrid Sparse & Dense Search
No. MongoDB organizes data into databases and collections, but it does not have a hierarchical structure like sub-collections within collections.
HNSW
Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.
MongoDB (Atlas Vector Search)
Atlas has support for vector embeddings that are less than or equal to 2048 dimensions.
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.
MongoDB Atlas vs. KDB.AI on Purpose-built
What’s your vector database for?
A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:
- Scalability and tunability
- Multi-tenancy and data isolation
- A complete suite of APIs
- An intuitive user interface/administrative console
Add on to Atlas
Yes.
C#, Java, Node, Pymango
Python
MongoDB Atlas vs. KDB.AI: what’s right for me?
MongoDB (Atlas Vector Search)
Altas is a managed cloud database based on MongoDB document database.
SaaS
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
