It serves as a vector data storage solution for RAG data retrieval in AI scenarios.

It is also the primary database for semantic similarity matching calculations. Review collected by and hosted on G2.com.

Zilliz's vector database is a cornerstone of our AI-powered customer service, enabling the business to ground responses in a vast knowledge base. Its powerful hybrid search and efficient vector retrieval capabilities allow us to process customer inquiries with remarkable speed and accuracy, which is fundamental to the effectiveness of our Retrieval-Augmented Generation (RAG) systems. Review collected by and hosted on G2.com.

it wrapped the data engineering work alot internally, that using milvus is more like a declarative experience. It accelerates our RAG and Agent development by releasing our focus from storage and storage-interaction side, that we could focus more on the business logic on the agent optimization. Review collected by and hosted on G2.com.

Enhancing the accuracy and reliability of AI applications: Zilliz's vector database is one of the core components for building RAG (Retrieval-Augmented Generation) systems. RAG can provide large models with real-time, accurate information from external knowledge bases, effectively reducing the issue of "hallucinations" (i.e., generating inaccurate or fictitious content) in large models, thereby enhancing the credibility of AI applications.

Accelerating the processing performance of AI applications: Its open-source project Milvus and cloud service Zilliz Cloud can efficiently index and query massive vector data, supporting millisecond-level retrieval of tens of billions of vectors. By supporting GPU acceleration, its performance can be up to 10 times better than versions using only CPUs, which is crucial for applications requiring real-time responses (such as recommendation systems and intelligent Q&A). Review collected by and hosted on G2.com.

Zilliz addresses the challenge of searching through embeddings created from social media creators' content on a large scale. It effectively manages the complexity involved in handling and retrieving relevant information from vast amounts of such data. Review collected by and hosted on G2.com.

We use Zilliz to power our recommendation system. It solves the critical problem of storing and searching millions of image embeddings at high speed. This benefits us by enabling real-time, accurate visual recommendations and 'similar item' search for our users, which significantly improves user engagement. Review collected by and hosted on G2.com.

The maintenance of the vector database ensures that I receive stable and efficient enterprise-level services for both semantic and keyword searches. Review collected by and hosted on G2.com.

Automatic scalability and capacity adjustment Review collected by and hosted on G2.com.

Hosting Vector DB for the claude-conext Review collected by and hosted on G2.com.

We use Zilliz as our RAG Review collected by and hosted on G2.com.