Aerospike
A multi-model AI database designed for high-throughput vector processing at scale, supporting real-time AI use cases with a patented Hybrid Memory Architecture and efficient infrastructure usage, capable of handling large volumes of data and concurrent users.
About this tool
Aerospike
Aerospike is a massively scalable, millisecond latency, real-time distributed NoSQL database. It offers blazing-fast reads/writes and unmatched uptime, designed to handle infinite scale, speed, and savings.
Features
- Real-time Performance: Provides sub-millisecond access and predictable, consistent performance for any amount of data, leveraging a patented Hybrid Memory Architecture.
- Massive Scalability: Capable of growing from gigabytes to petabytes without performance tradeoffs.
- Cost Efficiency: Achieves reduced operational costs and infrastructure usage by requiring fewer servers, contributing to sustainability goals.
- AI-Driven Future Readiness: Helps deliver more accurate AI by ingesting massive amounts of streaming data from tens of thousands of sources.
- Multi-Model Capabilities:
- Aerospike Graph: Utilizes Apache TinkerPop and Gremlin, suitable for applications relying on vital data relationships, offering easy integration and powerful traversal.
- JSON Document Models: Supports storing, searching, and managing complex, hierarchical datasets and workloads with flexible JSON document querying.
- Key-Value Store: Offers a high-performance, scalable solution for applications requiring simple, efficient access to large volumes of data with low-latency read and write operations, ideal for caching, session management, and real-time analytics.
- Managed Service: Available as an always-on, secure managed service, allowing users to focus on delivering value rather than database management and optimization.
Pricing
Pricing information is not available in the provided content.
Loading more......
Information
Categories
Tags
Similar Products
6 result(s)Vald is an open-source, highly scalable distributed vector search engine known for its asynchronous auto-indexing and ability to efficiently handle large-scale vector data in real time, making it suitable for demanding vector search applications.
Vespa.ai is a scalable open-source platform for real-time big data serving and vector search. It supports vector similarity search and is used for applications like retrieval augmented generation and e-commerce search, making it highly relevant for vector database and vector search use cases.
Milvus Distributed is a horizontally scalable, distributed deployment of the Milvus vector database designed for enterprise workloads, offering high reliability and the ability to handle billions of vectors with a comprehensive management toolkit.
HAKES is a system designed for efficient data search using embedding vectors at scale, making it a relevant solution for vector database applications.
Qwak provides a vector store solution engineered for optimized storage and querying of vector embeddings, offering efficient search capabilities, high performance, scalability, and data retrieval by identifying similarities among data points.
Apache Cassandra is a distributed NoSQL database that is adding native support for high-dimensional vector storage and approximate nearest neighbor search, making it a scalable choice for AI and vector search workloads.