Hyperspace

Search beyond limits.

Free Trial

Description

Hyperspace provides an elastic compatible, cloud-native search database designed for performance-intensive applications. It utilizes custom computing power, specifically a purpose-built chip, to overcome the limitations of traditional software-based search solutions. This approach allows Hyperspace to deliver significantly faster search speeds, increased throughput, and rapid data ingestion, enabling operations at a billion-item scale.

The platform supports various search types, including lexical, vector, and hybrid search, combining keyword-based methods with state-of-the-art similarity queries. Hyperspace is engineered to optimize computation efficiency and memory footprint, aiming to reduce search database costs substantially while ensuring high availability, security, and seamless integration with existing Elastic environments. It enables users to scale their search capabilities without compromising performance or consistency.

Key Features

Custom Computing Power: Utilizes a purpose-built chip for enhanced search performance.
High Performance Metrics: Delivers up to 10x faster queries, 5x throughput, and 4x ingestion speed.
Scalability: Built to handle billion-scale search operations efficiently.
Cost Efficiency: Aims to cut search database costs by up to 50%.
Hybrid Search Capability: Combines vector search with lexical search functions (metadata filtering, aggregations, TF-IDF).
Vector Search: Executes state-of-the-art similarity queries.
Lexical Search: Supports traditional keyword-based search functionalities.
Cloud-Native Architecture: Designed for modern cloud environments.
Elastic Seamless Integration: Offers easy integration with Elastic setups.
High Availability: Engineered for reliable performance.

Use Cases

Implementing high-performance search in data-intensive applications.
Scaling search infrastructure to handle billions of items.
Reducing operational costs associated with large-scale search databases.
Performing real-time similarity searches using vector embeddings.
Executing complex hybrid searches combining keyword and vector queries.
Improving search relevance and latency in demanding environments.
Enhancing applications like fraud detection or recommendation systems requiring fast search.