Milvus is a cloud-native, open-source vector database built primarily in Go and C++ and optimized for high-performance similarity search on massive-scale embedding datasets. With over 43,000 GitHub stars and CNCF graduated status, it is one of the most mature and battle-tested vector databases available.

• Billion-Scale Search – Handles billion-scale vector datasets with millisecond latency
• Disaggregated Architecture – Independent scaling of storage and compute with microservices design
• Multiple Index Types – HNSW, IVF, DiskANN with Int8 compression for memory efficiency
• Hybrid Search – Combines vector similarity with scalar filtering, BM25 full-text search, and JSON path indexing
• Hot/Cold Tiering – Automatic data tiering between memory/SSD and object storage based on access patterns
• Hardware Acceleration – AVX512, Neon SIMD, quantization, and GPU support
• Multi-Tenancy – Supports up to 100K collections per cluster
• CNCF Graduated – Production-proven with enterprise-grade reliability

Milvus employs a multi-layered, microservices-based architecture separating access, coordination, worker, and storage layers:

• Access Layer – Handles client requests, validation, and routing to internal nodes
• Coordinator Service – The brain of the system; manages load balancing, data coordination, and query planning via etcd
• Streaming Node – Go-based component built on Woodpecker WAL for real-time ingestion, eliminating external message queues
• Data Node and Query Node – C++ worker nodes handling compaction, indexing on sealed segments, and query execution
• Object Storage – All data persists in S3/MinIO with a diskless architecture for simplified geo-distributed deployments

Milvus supports multiple vector indexing strategies:

• HNSW – Hierarchical Navigable Small World graphs for high-accuracy approximate nearest neighbor search; supports Int8 compression to reduce memory while preserving accuracy
• IVF – Inverted File index for partitioned search with configurable accuracy/speed tradeoffs
• DiskANN – Disk-optimized approximate nearest neighbor for datasets larger than memory
• BM25 – Full-text search index with 400% faster performance than Elasticsearch, supporting configurable drop_ratio_search

Milvus 2.6 introduced tiered hot/cold separation:

• Hot data – Frequently accessed data stays in memory/SSD for low-latency queries
• Cold data – Infrequently accessed data migrates to object storage (S3/MinIO/NetApp)
• Dynamic migration – Automatic tiering based on access patterns, reducing storage costs by 50%
• Storage v2 – Parquet/Vortex formats for Spark compatibility and reduced IOPS/memory usage

from pymilvus import MilvusClient, DataType
 
# Connect to Milvus
client = MilvusClient(uri="http://localhost:19530")
 
# Create collection with schema
client.create_collection(
    collection_name="articles",
    dimension=768,
    metric_type="COSINE",
    auto_id=True
)
 
# Insert vectors with metadata
data = [
    {"vector": [0.1] * 768, "title": "RAG Guide", "category": "ai"},
    {"vector": [0.2] * 768, "title": "Vector DBs", "category": "database"},
]
client.insert(collection_name="articles", data=data)
 
# Hybrid search: vector similarity + scalar filter
results = client.search(
    collection_name="articles",
    data=[[0.15] * 768],
    filter='category == "ai"',
    limit=10,
    output_fields=["title", "category"]
)
for hits in results:
    for hit in hits:
        print(f"{hit['entity']['title']}: {hit['distance']:.4f}")

• Milvus GitHub Repository
• Milvus Official Website
• Milvus Architecture Documentation
• Milvus 2.6 Release Blog

• Qdrant – Rust-based vector database
• ChromaDB – AI-native embedding database
• Mem0 – Memory layer using vector databases
• RAGFlow – RAG engine for document understanding
• LightRAG – Knowledge graph RAG framework