Milvus
Milvus is an open-source vector database designed for AI applications and similarity searches. It stores, indexes, and manages billions of embedding vectors generated by deep neural networks and other machine learning models. Milvus enables real-time similarity search, making it ideal for applications like recommendation systems, image recognition, and natural language processing.
Core Information
#core_informationName, description, vendor website, logo, technology type and tags.
Overview & Role
Milvus is an open-source vector database designed for AI applications and similarity searches. It stores, indexes, and manages billions of embedding vectors generated by deep neural networks and other machine learning models. Milvus enables real-time similarity search, making it ideal for applications like recommendation systems, image recognition, and natural language processing.
Domain Classification & Tags
Performance Profile
#performance_profileTypical read and write throughput, payload size and processing latency.
Read Performance Profile
Throughput: Thousands to millions of QPS (queries per second) depending on hardware, index type, and dataset size
Payload Size: Vector embeddings (typically 128 to 1536 dimensions, float32), plus optional scalar filtering data
Processing Latency: Tens to hundreds of milliseconds for typical similarity searches; sub-10ms for highly optimized scenarios or small datasets
Write Performance Profile
Throughput: Thousands to hundreds of thousands of vectors per second per node, scalable horizontally
Payload Size: Vector embeddings (typically 128 to 1536 dimensions, float32), plus optional scalar field data
Processing Latency: Tens to hundreds of milliseconds for ingestion and indexing, with eventual consistency for search availability
Architecture Diagram
#architecture_diagramReference diagram of the technology's internal architecture.
Features
#featuresCatalogued product capabilities and what each one does.
Empirical data for features is currently being compiled in the global catalog.
Typical Use Cases
#typical_use_casesScenarios the technology is commonly chosen for.
Empirical data for typical use cases is currently being compiled in the global catalog.
Known Customers
#known_customersPublicly referenced organisations using the technology.
Empirical data for known customers is currently being compiled in the global catalog.
Known Integrations
#known_integrationsOther products and services it is documented to work with.
Empirical data for known integrations is currently being compiled in the global catalog.
Connectors
#connectorsDirectional data connections to other technologies, with direction and maturity.
Empirical data for connectors is currently being compiled in the global catalog.
Reference Architectures
#reference_architecturesPublished architectures where the technology is used or mentioned.
No published reference architecture blueprints currently link to Milvus.
Security Features
#security_featuresBuilt-in security and access-control capabilities.
Empirical data for security features is currently being compiled in the global catalog.
Known Issues
#known_issuesDocumented limitations, defects and operational pitfalls.
Empirical data for known issues is currently being compiled in the global catalog.
Guidelines
#guidelinesRecommended practices for adopting and operating the technology.
Empirical data for guidelines is currently being compiled in the global catalog.
Standards & Compliance
#standards_and_complianceStandards, certifications and control requirements it maps to.
Empirical data for standards & compliance is currently being compiled in the global catalog.
Sources
#sourcesDocumentation and research references behind the recorded information.
Empirical data for sources is currently being compiled in the global catalog.
Expert Validation
#expert_validationWhether domain experts reviewed and confirmed the content.
Empirical data for expert validation is currently being compiled in the global catalog.