What is Zilliz?
Zilliz Cloud is a fully managed vector database service built on Milvus for enterprise vector similarity search and retrieval.
It supports billion-scale vector search, distributed high-throughput clusters, and serverless deployments across AWS, Azure, and GCP.
Provides SDKs for Python, Java, Go, and Node.js, REST APIs, and integrations with embedding models and AI frameworks for RAG, semantic search, and recommender systems.
Includes AUTOINDEX tuning, the Cardinal search engine for accelerated retrieval, embedding pipelines for data preparation and chunking, and RBAC with SOC2 Type II and ISO27001 compliance.
Offers high availability with a 99.95% monthly uptime SLA, scalability to 500 compute units and over 100 billion vectors, plus migration, monitoring, and observability tools.
Common use cases include Retrieval-Augmented Generation, semantic text and image search, audio/video similarity, multimodal search, AI agents, molecular similarity, and recommendation systems.
Zilliz pricing Freemium
Verify on the official pricing page.
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Zilliz's key features
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Fully-managed Milvus-based vector database service (Zilliz Cloud)
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Billion-scale distributed vector similarity search with ability to scale to 100 billion items and 500 CUs
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AUTOINDEX-optimized indexing that balances recall and performance
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Cardinal search engine for accelerated vector retrieval
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Built-in embedding pipelines for data preparation, chunking, model selection, and vector transformation
Zilliz use cases
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Build a production-ready retrieval-augmented generation (RAG) system that serves accurate, context-aware answers from company knowledge using Zilliz Cloud's managed Milvus vectors, embedding pipelines, AUTOINDEX/Cardinal acceleration for low-latency billion-scale search, plus RBAC and observability to secure and monitor performance
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Implement multimodal semantic search across text, images, and audio to power an enterprise search portal or digital asset management system with serverless high-throughput vector search, distributed clusters for scale, and SDKs/APIs for easy integration
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Deliver real-time personalized recommendations and similarity-based ranking for e-commerce or media platforms using Zilliz Cloud's embedding pipelines, billion-scale vector clusters, automatic indexing and cardinal acceleration to ensure low-latency, accurate recommendations with built-in security and monitoring
Who is it for?
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Data engineers
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Vector database users
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Semantic search architects
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Rag solution builders
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Cloud infrastructure teams
