Vector Database built for enterprise-grade AI applications
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Zilliz is an enterprise-grade vector database management system, powered by the popular open-source Milvus, engineered for high-performance and scalable AI applications. Zilliz Cloud offers a fully-managed Milvus service, abstracting away complex infrastructure management. It excels in billion-scale vector search, boasting 10x faster retrieval speeds than standard Milvus through its Cardinal search engine. The platform ensures high availability (99.95% monthly uptime) and massive scalability, easily expanding clusters to 500 CUs capable of managing over 100 billion items. Technical capabilities include an optimized AUTOINDEX balancing recall and performance, built-in embedding pipelines for converting unstructured data, and robust multi-cloud support across AWS, Azure, and GCP. Zilliz Cloud integrates seamlessly with leading AI models and frameworks, providing official SDKs for Python, Java, Go, and Node.js. It adheres to strict security and governance standards, including SOC2 Type II, ISO27001, and Role-Based Access Control, making it a secure and efficient solution for AI-driven applications like RAG and semantic search.
Zilliz is an enterprise-grade vector database management system, powered by the popular open-source Milvus, engineered for high-performance and scalable AI applications.
Explore all tools that specialize in vector similarity search. This domain focus ensures Zilliz delivers optimized results for this specific requirement.
Explore all tools that specialize in retrieval-augmented generation (rag). This domain focus ensures Zilliz delivers optimized results for this specific requirement.
Explore all tools that specialize in recommender systems. This domain focus ensures Zilliz delivers optimized results for this specific requirement.
Explore all tools that specialize in semantic search. This domain focus ensures Zilliz delivers optimized results for this specific requirement.
Explore all tools that specialize in image similarity search. This domain focus ensures Zilliz delivers optimized results for this specific requirement.
Explore all tools that specialize in audio similarity search. This domain focus ensures Zilliz delivers optimized results for this specific requirement.
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A proprietary search engine integrated into Zilliz Cloud that delivers up to 10x faster vector retrieval speed compared to the standard Milvus implementation.
Automates the process of converting unstructured data (text, images, audio, video) into searchable vector embeddings. This includes data preparation, chunking, model selection, and transformation.
An intelligent indexing mechanism within Zilliz Cloud that automatically balances the trade-off between search recall accuracy and query performance. It optimizes index structures for enhanced efficiency.
Zilliz Cloud is deployed and available across leading cloud providers including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure in multiple global regions.
LLMs can suffer from outdated knowledge, factual inaccuracies (hallucinations), or lack of domain-specific context. Zilliz Cloud solves this by providing a mechanism to inject real-time, proprietary, or specific external data into the LLM's context during query time.
Unstructured data (e.g., enterprise documents, articles, internal knowledge bases) is processed and converted into vector embeddings using Zilliz's built-in pipelines or external models.
These vector embeddings are stored and indexed for high-speed similarity search within Zilliz Cloud.
When an end-user poses a query to the LLM, a vector embedding of the query is generated.
Zilliz Cloud is queried with the user's query embedding to retrieve semantically relevant document chunks or data snippets.
The retrieved information is then appended to the original LLM prompt, providing the LLM with up-to-date and contextual information to generate a more accurate and relevant response.
Traditional recommender systems often struggle with cold starts, scalability for vast item catalogs, and generating truly personalized suggestions. Zilliz Cloud enables semantic matching that overcomes these limitations by understanding underlying preferences and item similarities.
Item data (e.g., product descriptions, movie synopses, article content) and user interaction data (e.g., past purchases, viewed items) are converted into vector embeddings.
These item and user preference embeddings are stored and indexed in Zilliz Cloud.
When a user requests recommendations or interacts with an item, their preference vector or the item's vector is used to perform a real-time vector similarity search in Zilliz Cloud.
The system retrieves a list of semantically similar items or items aligned with the user's preferences.
These highly relevant items are then presented as personalized recommendations, improving user engagement and conversion rates.
Keyword-based search often fails to capture the true meaning or intent behind a user's query, leading to irrelevant results, especially with natural language. Zilliz Cloud enables search based on semantic meaning, providing more accurate and contextually relevant results.
A large collection of text documents (e.g., legal documents, research papers, customer support tickets) is pre-processed, chunked, and each chunk is converted into a vector embedding representing its semantic content.
These document embeddings are stored and indexed in Zilliz Cloud for efficient retrieval.
When a user inputs a natural language query, that query is also converted into a vector embedding.
Zilliz Cloud performs a vector similarity search comparing the query embedding against the document embeddings.
The system returns the document chunks or full documents that are semantically most similar to the query, providing a more intelligent and intuitive search experience than traditional keyword matching.
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Pinecone is another popular managed vector database service, known for its focus on developer experience and enterprise readiness. Zilliz often positions itself as a more cost-effective and performant alternative, especially given its open-source Milvus foundation.
Weaviate is an open-source vector database that also offers a managed cloud service. Zilliz differentiates itself with specific performance optimizations like the Cardinal search engine and deep integration with the Milvus ecosystem, offering distinct scalability and feature sets.
Qdrant is an open-source vector similarity search engine and database, which can be self-hosted or used as a managed service. Zilliz provides a comprehensive managed platform built on the widely adopted Milvus, emphasizing its enterprise-grade features, multi-cloud flexibility, and 100 billion item scale capabilities.
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