Milvus vs. KDB.AI
Compare Milvus 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.
Milvus vs. KDB.AI on Scalability
Yes. At the component level (which provides more fine-grained scalability).
Yes.
Dynamic segment placement
Neither.
Milvus scalability
Regarding scalability, Milvus uses worker nodes for each type of action (components to handle connections, data nodes to handle ingestion, index nodes to index, and query nodes to search). Each node has its own assigned CPU and memory resources. Milvus can dynamically allocate new nodes to an action group, speeding up operations or reducing the number of nodes, thus freeing resources for other actions. Dynamically allocating nodes allows for easier scaling and resource planning and guarantees latency and throughput.
KDB.AI
KDB.AI is a scalable vector database.
Milvus 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 (DiskANN)
Yes (qFlat and qHNSW)
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes. Hybrid Sparse & Dense Search
Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.
Milvus functionality
- Milvus supports multiple in-memory indexes and table-level partitions results in the high performance required for real-time information retrieval systems.
- RBAC support is a requirement for enterprise-grade applications.
- In regards to partitions, by limiting searches to one or several subsets of the database, partitions can provide a more efficient way to filter data compared to static sharding, which can introduce bottlenecks and require re-sharding as data grows beyond server capacity. Partitions are a great way to manage your data by grouping it into subsets based on categories or time ranges. This can help you to easily filter and search through large amounts of data, without having to search through the entire database every time.
- No single Index type can fit all use cases since each use case will have different tradeoffs With more index types supported, you have more flexibility to find the balance between accuracy, performance and cost.
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.
Milvus 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
Milvus vs. KDB.AI: what’s right for me?
Milvus
Milvus is a fully open source and independent project, maintained by a number of companies and individuals, some of whom also offer commercial services and support. Graduate of LF AI Data. License: Apache-2.0 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
Compare Milvus with others
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.