Vector Database
Open Source
Milvus is an open-source vector database specifically designed to facilitate embedding similarity search and AI applications. It simplifies the management of unstructured data and ensures a consistent user experience across various deployment environments. The database is optimized for high performance and scalability, making it ideal for handling massive datasets of high-dimensional vectors. Milvus is particularly suitable for machine learning, deep learning, and recommendation systems, providing robust support for similarity search tasks. It integrates seamlessly with popular AI development tools such as LangChain, LlamaIndex, and OpenAI, further enhancing its utility for developers working on generative AI applications.
Milvus 2.0, the latest iteration of the platform, is a cloud-native vector database that separates storage and computation to enhance elasticity and flexibility. This version is fully stateless, which significantly improves its scalability and reliability. The database supports millisecond search on trillion-vector datasets and offers features such as hybrid search and metadata filtering. Additionally, Milvus can be easily deployed on various platforms, including laptops, mobile devices, Docker, and Kubernetes. For those looking to minimize operational burdens, Zilliz Cloud offers a fully managed service that ensures production-ready reliability with pay-as-you-go options. Milvus is trusted by industry leaders like NVIDIA, PayPal, and Salesforce, underscoring its reliability and performance in production workloads.
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High performance and scalability
Seamless integration with AI tools
Millisecond search on trillion vectors
Hybrid search and metadata filtering
Cloud-native with stateless architecture
Developers can manage unstructured data efficiently for AI applications.
Machine learning models benefit from robust similarity search.
High-dimensional datasets can be handled with ease and scalability.
Generative AI applications are streamlined with seamless tool integration.
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