Milvus : Open-source vector database for high-performance AI search
Milvus: in summary
Milvus is an open-source vector database designed for high-speed similarity search and retrieval of large-scale embedding data. Developed by Zilliz and built for AI-native workloads, it’s widely used in scenarios such as semantic search, image retrieval, recommendation engines, and video analysis.
Milvus is suitable for data scientists, AI/ML engineers, and backend developers working in industries like e-commerce, finance, security, and autonomous systems. It supports billions of vectors, integrates with major ML frameworks, and offers advanced indexing options for optimized search performance.
Key benefits include:
High-throughput vector search at low latency
Multiple indexing and distance metric options
Scalable, modular, and cloud-native architecture
What are the main features of Milvus?
High-performance vector indexing
Milvus supports multiple indexing algorithms tailored for different use cases and dataset sizes.
Index types include IVF, HNSW, ANNOY, and Flat
Supports cosine, Euclidean (L2), and inner product metrics
Dynamic indexing with update and delete capabilities
Horizontal scalability
Milvus is designed to handle massive datasets with ease, scaling out across nodes as needed.
Supports distributed architecture with separation of compute and storage
Efficient resource management using Milvus’ custom query node design
Easily scales to billions of vectors
Flexible deployment options
Milvus can be deployed in various environments to suit different infrastructure requirements.
Self-hosted via Docker or Kubernetes
Fully managed via Zilliz Cloud
Integrates with object storage systems (e.g., S3, MinIO)
Integration with machine learning tools
Milvus fits seamlessly into ML pipelines and supports vector ingestion from popular frameworks.
Works with TensorFlow, PyTorch, Hugging Face, and OpenAI embeddings
APIs and SDKs available in Python, Go, Java, C++, and Node.js
Compatible with common data science tools and workflows
Rich query capabilities
Milvus provides versatile search and filtering mechanisms for more targeted results.
Vector search by similarity with top-k results
Boolean filtering on vector metadata
Range and term queries for hybrid search scenarios
Why choose Milvus?
Optimized for AI search workloads: Designed from the ground up for handling embeddings from modern deep learning models.
Highly scalable and distributed: Efficient architecture enables scaling across billions of vectors and multiple nodes.
Flexible and pluggable indexing: Choose the indexing strategy best suited to your latency and accuracy requirements.
Broad integration with ML ecosystems: Compatible with the most widely used AI/ML frameworks and tools.
Mature open-source project: Backed by Zilliz and a growing global community,
Milvus: its rates
Standard
Rate
On demand
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