Features
- Billion-scale vector search with distributed architecture
- Multiple index types (HNSW, IVF, DiskANN)
- Hybrid search with scalar and vector fields
- GPU-accelerated indexing and search
Pros
- Designed for true billion-scale vector datasets
- Most index type options of any vector database
- GPU acceleration for high-throughput workloads
Cons
- Complex deployment with multiple components
- Heavy resource requirements for distributed mode
- Steeper learning curve than simpler alternatives
Overview
Milvus is an open-source, cloud-native vector database designed for billion-scale similarity search. Built with a distributed architecture from the ground up, it separates compute, storage, and coordination, allowing each layer to scale independently.
Milvus supports the widest range of index types among vector databases, including HNSW, IVF variants, DiskANN for disk-based search, and GPU indexes for high-throughput scenarios. This flexibility lets you optimize for your specific performance, memory, and accuracy tradeoffs.
The database supports hybrid search that combines vector similarity with scalar field filtering, and provides a SQL-like query language for complex operations. Zilliz Cloud offers a fully managed version for teams that do not want to manage the distributed infrastructure.
When to Use
Choose Milvus for large-scale applications with billions of vectors that need distributed, high-throughput vector search. For smaller workloads, simpler options like Qdrant, ChromaDB, or pgvector may be more practical.
Getting Started
pip install pymilvusfrom pymilvus import MilvusClient
client = MilvusClient("milvus_demo.db")
client.create_collection(collection_name="demo", dimension=768)
client.insert(collection_name="demo", data=[
{"id": 1, "vector": [0.1] * 768, "text": "hello"}
])
results = client.search(collection_name="demo", data=[[0.1] * 768], limit=3)