Elastic vs KDB.AI

Elastic vs. KDB.AI

Compare Elastic 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.

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Elastic vs. KDB.AI on Scalability

ElasticKDB.AI

Separation of storage and compute

❌

Separation of query and insertions

No. Only scale at the server level.

Yes.

Multi-replication

✔️

❌

Dynamic segment placement vs. static data sharding

Static sharding

Neither

Cloud-native

✔️

❌

Billion-scale vector support

❌

Elastic 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.

Elastic 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.

ElasticKDB.AI

Role-based Access Control (RBAC)

✔️

❌

Disk Index support

❌

Yes (qFlat and qHNSW)

Hybrid Search (ie Scalar filtering)

Yes. (combine vector and traditional search)

Yes. Hybrid Sparse & Dense Search

Partitions/namespaces/logical groups

❌

Index type supported

1 (HNSW)

Flat, qFlat, IVF, IVFPQ, HNSW, and qHNSW.

Multiple in-memory indexes support

❌

Elastic functionality

Elasticsearch uses reverse index and builds vector search capability on top of the exsting search architecture. Elasticsearch is good at text search, but the whole architecture is not purpose-built for vector search.

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.

Elastic 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

ElasticKDB.AI

Purpose-built for Vectors

❌

Yes.

Tunable consistency

❌

Support for both stream and batch of vector data

✔️

❌

Binary Vector support

✔️

❌

Multi-language SDK

Python, Java, Go, C++, Node.js, Rust, Ruby, .NET (C#), PHP, Perl

Python

Database rollback

❌

Elastic vs. KDB.AI: what’s right for me?

Elasticsearch

Elasticsearch is built on Apache Lucene and was first released in 2010 by Elastic. License: Dual-licensed Server Side Public License (SSPL) or the Elastic 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