Zilliz
Zilliz provides a fully-managed vector database service that integrates with AI models to transform unstructured data into searchable assets, offering high-speed performance and robust data protection standards.
Services
Zilliz offers a fully-managed vector database service designed for speed, scale, and high performance. The service supports deploying vector databases on AWS, GCP, or Azure. Users can start using the service within minutes, enabling quick and efficient deployment. Zilliz ensures 99.9% monthly uptime for all cloud-based products, guaranteeing reliability and consistency.
Products
Zilliz provides an open-source vector database built for billion-scale vector similarity search. Among its key offerings is the Cardinal search engine, which enables a 10x faster vector retrieval speed compared to Milvus. Users can easily scale their clusters to 500 Compute Units (CUs), capable of serving over 100 billion items. This allows Zilliz to handle vast amounts of data efficiently and effectively.
Cloud Availability
Zilliz's vector database services are available on AWS, Azure, and GCP across eight regions worldwide. This widespread availability ensures that users can deploy and manage their vector databases from different geographic locations, providing flexibility and scalability. Zilliz's commitment to cloud availability helps businesses achieve high performance and accessible data services regardless of their location.
Data Security
Zilliz adheres to high data security standards, meeting SOC2 Type II and ISO27001 certifications. The company supports Role-Based Access Control (RBAC) for robust data protection, ensuring that only authorized users can access specific data. These security measures are essential for businesses that handle sensitive information and require stringent data protection protocols.
Integration with AI Models
Zilliz integrates with leading AI models and frameworks, transforming unstructured data into searchable and valuable assets. The platform enables the conversion of unstructured data into searchable vector embeddings, covering all stages from data preparation to chunking, model selection, and transformation. This functionality is crucial for businesses looking to leverage their data through advanced AI capabilities.