Qdrant vs. KDB.AI
Compare Qdrant 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.
Qdrant vs. KDB.AI on Scalability
No. Only scale at the server level.
Yes.
Static sharding
Neither.
Qdrant scalability
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
KDB.AI
KDB.AI is a scalable vector database.
Qdrant 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.
No. Authentication only
Yes (qFlat and qHNSW)
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes. Hybrid Sparse & Dense Search
1 (HNSW)
Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.
Qdrant functionality
Qdrant uses three types of indexes to power the database. The three indexes are a Payload index, similar to an index in a conventional document-oriented database, a Full-text index for string payload, and a vector index. Their hybrid search approach is a combination of vector search with attribute filtering.
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.
Qdrant 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
Yes.
Python, Go, Rust
Python
Qdrant vs. KDB.AI: what’s right for me?
Qdrant
Open source Qdrant is maintained by the commercial company offering a cloud version of Qdrant.
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 Qdrant 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.