Hyperspace is a fully managed hybrid search database, designed to make search faster, more cost-effective, and scalable. It offers a solution for organizations that were previously constrained, enabling them to handle larger data sizes without compromising business logic.

Hyperspace offers fully managed hybrid search, with unprecedented latency and throughput, and at reduced cost. Hyperspace search combines lexical search with vector search, allowing the use of multiple vectors in one query.

Hyperspace offers a per-use pricing model, allowing full control of database costs.Additionally, The extreme latency of Hyperspace allows the use of a smaller number of machines, reducing costs by up to 50%.

Setting up Hyperspace is easy, as seen in the docs here.

Hyperspace is fully secured and is compatible with common data privacy and security requirements such as GDPR and SOC2.

Hyperspace offers a high availability of 99.99%, with load time according to customer requirements. Higher availability can be provided according to customer needs.

Hyperspace provides premium ongoing support for its customers. The technical support team can be contacted through our website and through designated channels.

Hyperspace’s stability and extreme performance make it ideal for real-time search applications, as well as data analytics.

Yes, contact us for more details.

Hyperspace allows separating data through two main methods. The first is the use of “data collections”, which are separate segments of data. Queries are performed on data collections, such that all results will be from a single collection.

Hyperspace supports native Python syntax and also offers comprehensive support for Elasticsearch functions, making it easier for users to continue executing classic Elastic queries. This design not only facilitates conducting searches using familiar Python commands but also simplifies the migration process. Moreover, support for additional syntax languages will be introduced soon.