Zilliz Cloud
Fully managed, high-performance vector database built on Milvus for scalable AI applications and unstructured data search.
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Product Overview
What is Zilliz Cloud?
Zilliz Cloud is a cloud-native vector database platform designed to efficiently store, index, and search billions of vector embeddings for AI-driven applications. Built on the open-source Milvus engine, it offers enterprise-grade scalability, security, and performance optimization through AI-powered indexing and query optimization. The platform supports multi-cloud deployment and flexible compute resources, enabling seamless integration with popular AI models and frameworks to power semantic search, recommendation systems, anomaly detection, and more.
Key Features
AI-Powered AutoIndex and Cardinal Search Engine
Automatically selects optimal indexing and search strategies using AI, delivering superior speed and accuracy without manual tuning.
Scalable, Fully Managed Cloud Service
Elastic, distributed architecture supports scaling up to hundreds of compute units and managing over 100 billion vectors with high availability.
Multi-Cloud and Flexible Deployment
Deploy on AWS, Azure, or Google Cloud with options for fully managed service or Bring Your Own Cloud (BYOC) for compliance and security.
Enterprise-Grade Security and Compliance
Includes SOC2 Type II, ISO27001 certifications, role-based access control, encryption, and robust operational controls.
Cost-Efficient Tiered Storage and Compute
Automated storage tiering and tailored compute options optimize total cost of ownership while maintaining performance.
Rich Search Capabilities
Supports hybrid search across text, image, audio, and video embeddings with multiple similarity metrics like Cosine, Euclidean, and Inner Product.
Use Cases
- Semantic Search : Enable fast, accurate retrieval of semantically similar documents, images, or multimedia from massive unstructured datasets.
- Recommender Systems : Build personalized recommendation engines by efficiently matching user preferences with product or content vectors.
- Retrieval-Augmented Generation (RAG) : Enhance large language models by integrating external vector data sources for more informed and context-aware AI responses.
- Anomaly Detection : Identify unusual patterns or outliers in data by leveraging vector similarity comparisons at scale.
- Multimodal Similarity Search : Perform unified searches across different data types such as text, images, audio, and video within a single query.
FAQs
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