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Milvus

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description Milvus Overview

Milvus is a purpose-built vector database designed specifically for high-performance similarity searches. As AI and LLM applications explode, Milvus provides the infrastructure to store and query embeddings at scale. It decouples storage from computing, allowing it to handle billions of vectors with low latency, making it essential for recommendation engines and image recognition systems.

help Milvus FAQ

What kinds of searches is Milvus built to perform?

Milvus stores vector embeddings and searches for nearby vectors using approximate nearest-neighbor indexes. Typical uses include semantic text retrieval, image similarity, recommendation systems, and retrieval-augmented generation for LLM applications.

What is the difference between Milvus and Zilliz Cloud?

Milvus is the open-source vector database originally created by Zilliz. Zilliz Cloud is the managed service built around Milvus, removing much of the work of provisioning, scaling, upgrading, and monitoring clusters.

Can Milvus store metadata alongside embeddings?

Yes, a Milvus collection can contain vector fields and scalar fields such as strings, numbers, or Boolean values. Applications can filter on those scalar values before or during a similarity search.

How does Milvus differ from PostgreSQL with pgvector?

Milvus is purpose-built for distributed vector workloads and offers multiple vector index types and separate storage and compute components. pgvector keeps embeddings inside PostgreSQL, which can be simpler when vector search must remain close to existing relational data.

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