Zilliz
Provides a fully-managed vector database service built on the open-source Milvus framework, enabling billion-scale vector similarity search for AI applications. It simplifies deployment and scaling by eliminating infrastructure complexity, offering 10x faster retrieval speeds and seamless integration with cloud platforms like AWS, Azure, and GCP.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
Funding
Raised to date
$113MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 2 rounds
5CHCMV+4$60M · Series B Extension
Announced Aug 2022 · from article date
Disclosed cumulative: $103M
Founders
Product
Problem
Building and maintaining vector search applications at scale can be complex and resource-intensive, requiring specialized infrastructure and expertise. Deploying and scaling open-source vector databases like Milvus involves managing intricate configurations, handling infrastructure scaling, and ensuring high availability.
Solution
Zilliz Cloud offers a fully-managed vector database service built on Milvus, streamlining the deployment and scaling of AI applications that rely on billion-scale vector similarity search. The platform eliminates the complexities of infrastructure management, allowing developers to focus on application logic rather than operational overhead. Zilliz Cloud provides optimized indexing and search capabilities, delivering faster retrieval speeds and reduced total cost of ownership compared to self-managed Milvus deployments. The service integrates with cloud platforms like AWS, Azure, and GCP, offering a multi-cloud solution for vector database management.
Target Audience
The primary audience includes enterprise-level AI application developers and data scientists who need a scalable, high-performance vector database without the operational overhead of self-managing infrastructure.
Features
- Fully-managed Milvus service, eliminating the need for manual infrastructure configuration and maintenance
- Optimized AUTOINDEX balancing recall and performance
- Cardinal search engine enabling 10x faster vector retrieval speeds
- Distributed architecture for handling large-scale vector data, scaling to 500 CUs and over 100 billion items
- High availability with 99.95% monthly uptime SLA
- SOC2 Type II and ISO27001 compliance with Role-Based Access Control (RBAC) for data protection
- Built-in embedding pipelines for converting unstructured data into searchable vector embeddings
- Multi-cloud availability on AWS, Azure, and GCP across multiple regions
- Integrations with leading AI models and frameworks