Zilliz Cloud

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