Why Milvus?
Built on a strong and growing community.
Why Milvus?
Milvus is a cloud-native, open-source vector database built to power embedding similarity search and AI applications.
Easy to Use
With Milvus vector database, you can create a large-scale similarity search service in less than a minute. Simple and intuitive SDKs are also available for a variety of different languages.
Blazing Fast
Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed.
Highly Available
Milvus vector database has been battle-tested by over a thousand enterprise users in various use cases. With extensive isolation of individual system components, Milvus is highly resilient and reliable.
Highly Scalable
Milvus's distributed and high-throughput nature makes it a natural fit for serving large-scale vector data.
Cloud-native
Milvus vector database adopts a systemic approach to cloud-nativity, separating compute from storage and allowing you to scale up and out.
Feature-rich
Support for various data types, enhanced vector search with attribute filtering, UDF support, configurable consistency level, time travel, and more.
Access layer
The access layer comprises a group of stateless proxies and serves as the front layer of the system and endpoint to users.
Coordinator service
The coordinator service assigns tasks to the worker nodes and functions as the system's brain.
Worker nodes
The worker nodes function as arms and legs and are dumb executors that follow instructions from the coordinator service and execute user-triggered DML/DDL commands.
Storage
Storage is the bone of the system and is responsible for data persistence. It comprises meta storage, log broker, and object storage.
Semantic text search
Processing and querying text across multiple vectors like intent, location, and previous search history can provide the context necessary for more accurate and nuanced results.
Targeted advertising
Vector databases can be used in targeted advertising to improve the relevance and effectiveness of ad targeting. In this context, the database can store and index large amounts of data related to user behavior, demographics, and interests as high-dimensional vectors. Ads are then mapped to the same space as the users, making targeted advertising as simple as performing a query in Milvus.
E-commerce
Vector databases such as Milvus can power product recommendation engines by combining multiple sources of unstructured data such as search history and past purchases.
UGC recommendation
User-generated content comes in various formats, ranging from simple text (blog posts, news articles) to short- and long-form videos. Each piece of content is a single vector representation in a vector database. This vector representation makes recommending new content as easy as querying over content users have liked or engaged with previously.
Risk-control and anti-fraud
Anti-fraud systems can also use vector representations to encode similarities between actions and other data points. For example, an anti-fraud system can compare the vectors representing different transactions or behaviors to identify similarities that may indicate a higher risk of fraud or other illicit activities.
New drug discovery
In drug discovery, vector representations of compounds include the overall structure and biological properties. A vector database can store and index this data as high-dimensional vectors, enabling new drug discovery simply by querying.