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Milvus

Open-source vector database for billion-scale AI — Kubernetes-native and distributed.

0Open Source
Vector Database
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Overview

Milvus is an open-source vector database built for scalable similarity search, designed to handle billions of vectors with a distributed architecture and Kubernetes-native deployment.

Key Features

  • Billion-scale vector search with distributed architecture
  • Multiple index types: HNSW, IVF, DiskANN
  • Milvus Lite — embedded mode for local development
  • Time Travel — query data at any historical timestamp
  • Dynamic schema for schema-on-write flexibility
  • Zilliz Cloud managed offering

Real-World Workflows

Production-scale image similarity search

  1. 1Extract image embeddings using a vision model (CLIP, ResNet)
  2. 2Load billions of embeddings into Milvus with IVF_FLAT index
  3. 3Deploy Milvus cluster on Kubernetes for horizontal scaling
  4. 4Query with a new image embedding to find visually similar products

Getting Started

# Start with Milvus Lite (embedded)
pip install pymilvus

from pymilvus import MilvusClient
client = MilvusClient('milvus.db')
client.create_collection(collection_name='demo', dimension=128)
client.insert('demo', [{'id': 1, 'vector': [0.1]*128}])

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