A: Milvus is an open-source vector database designed for AI applications, and Zilliz is the primary contributor and offers a managed cloud service for it.

Q: How does Zilliz Cloud differ from self-hosting Milvus?

A: Zilliz Cloud provides a managed, scalable, and highly available service, eliminating the need for users to manage the underlying infrastructure.

Q: What are the main use cases for Zilliz?

A: Main use cases include semantic search, image/video analysis, recommendation systems, fraud detection, and RAG for LLMs.

Q: Does Zilliz support real-time data ingestion and querying?

A: Yes, Zilliz Cloud is designed for low-latency queries and supports efficient data ingestion for real-time AI applications.

Q: What kind of support does Zilliz offer?

A: Zilliz offers community support for open-source Milvus and tiered support (developer, business, enterprise) for Zilliz Cloud users.

Zilliz

Zilliz provides cloud-native vector database services, including the managed Milvus, enabling developers to build and scale AI applications requiring vector similarity search.

Price: Freemium

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Description

Zilliz is the company behind Milvus, an open-source vector database, and offers Zilliz Cloud, a fully managed service for Milvus. It's engineered to handle the complexities of storing, indexing, and querying billions of vector embeddings, which are critical for AI applications like semantic search, recommendation engines, fraud detection, and RAG systems for large language models. Zilliz Cloud simplifies the deployment and management of Milvus, allowing developers to focus on building their applications rather than infrastructure. It differentiates itself by offering a highly scalable, performant, and reliable managed vector database solution based on the popular Milvus project, making advanced AI capabilities accessible to a broader range of enterprises and developers.

How to Use

1.Sign up for Zilliz Cloud and create a new Milvus cluster.

2.Install the Milvus client SDK (Python, Java, Go, Node.js) in your application.

3.Define your collections, including schema and vector dimensions.

4.Insert your vector embeddings and associated metadata into the Milvus collection.

5.Perform similarity searches by querying the collection with new vectors.

6.Integrate the search results into your AI application or LLM workflow.

Use Cases

Semantic searchRecommendation enginesFraud detectionRetrieval Augmented Generation (RAG)Image/video analysisDrug discovery

Pros & Cons

Pros

Fully managed, cloud-native vector database service.
Highly scalable for billions of vectors and high query throughput.
Based on the popular open-source Milvus project.
Simplifies deployment and management of vector search infrastructure.
Strong community and enterprise support options.

Cons

Pricing can become substantial for very large-scale deployments.
Requires understanding of vector embeddings and AI concepts.
Potential vendor lock-in risk for deeply integrated applications on Zilliz Cloud.

Pricing

https://zilliz.com/pricing

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