Zilliz

Zilliz is a fully managed vector database service built on the open-source Milvus platform, designed for enterprise-grade AI applications. It simplifies deploying and scaling vector search applications by removing infrastructure complexities, allowing developers to focus on building AI features. The platform supports billion-scale vector similarity search with high performance and low latency, making it suitable for real-time AI workloads. The target audience includes enterprises and developers working with large-scale unstructured data such as text, images, audio, and video. Zilliz Cloud is ideal for industries requiring fast, accurate vector search capabilities, including healthcare, finance, and AI-powered recommender systems. Zilliz offers a compelling value proposition by combining high speed, scalability, and cost efficiency. Its Cardinal search engine delivers up to 10 times faster vector retrieval than standard Milvus, while tiered storage optimizes costs for hot and cold data. The platform supports multi-cloud deployment on AWS, Azure, and Google Cloud, ensuring flexibility and global availability. Key differentiators include a fully managed service with automated scaling, AI-powered AutoIndex and query optimization, and enterprise-grade security certifications like SOC2 Type II and ISO27001. Zilliz also provides built-in embedding pipelines to convert unstructured data into searchable vectors, supporting multimodal and hybrid search across different data types. Technically, Zilliz leverages a distributed, cloud-native architecture with component-based scaling up to 500 compute units, capable of handling over 100 billion vectors. It offers various cluster types optimized for performance, capacity, or cost, and supports multiple similarity metrics and consistency levels to tailor search accuracy and speed. Overall, Zilliz stands out as a mature, scalable vector database platform that reduces operational overhead and total cost of ownership while enabling advanced AI search applications across diverse industries.

Key Features

• ⚡ 10x faster vector retrieval with Cardinal search engine for real-time AI

• ☁️ Fully managed multi-cloud service on AWS, Azure, and GCP for flexibility

• 🔒 Enterprise-grade security with SOC2 Type II and ISO27001 compliance

• 📊 Built-in embedding pipelines to convert unstructured data into vectors

• 📈 Scalable architecture supporting over 100 billion vectors and 500 compute units

Pros

• Delivers up to 10x faster vector search than standard Milvus

• Fully managed multi-cloud service reduces operational overhead

• Supports billion-scale vector datasets with easy scaling

• Built-in embedding pipelines simplify unstructured data handling

• Meets enterprise security standards including SOC2 and ISO27001

Cons

• Pricing can be high for business critical and enterprise tiers

• Advanced features may require learning curve for new users

FAQ

What deployment options does Zilliz Cloud offer?

Zilliz Cloud supports fully managed multi-cloud deployment on AWS, Azure, and Google Cloud, as well as a Bring Your Own Cloud (BYOC) option for custom infrastructure needs.

How does Zilliz improve vector search performance compared to Milvus?

Zilliz uses the Cardinal search engine and AI-powered AutoIndex to deliver up to 10 times faster vector retrieval speeds than standard Milvus implementations.

What types of data can Zilliz handle for vector search?

Zilliz supports multimodal data including text, images, audio, video, and molecular structures, enabling hybrid search across different data types.

How does Zilliz ensure data security and compliance?

Zilliz Cloud meets SOC2 Type II and ISO27001 standards and supports Role-Based Access Control (RBAC) to protect data and meet enterprise compliance requirements.

Can Zilliz scale to handle very large vector datasets?

Yes, Zilliz can scale horizontally with up to 500 compute units, supporting over 100 billion vectors with distributed, high-throughput architecture.

What is the purpose of tiered storage in Zilliz Cloud?

Tiered storage automatically manages hot and cold data to optimize performance and cost, improving return on investment for large-scale workloads.