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Tool Comparison

Milvus

Milvus

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

Open Source
VS
Weaviate

Weaviate

Open-source vector database with built-in embedding and hybrid search.

Open Source
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At a Glance

AttributeMilvusWeaviate
License / PricingOpen SourceOpen Source
Typeaiai
GitHub Stars
Rating4.3/54.5/5
Key Features6 listed6 listed
Integrations4 listed5 listed
Categories
Vector Database
Vector Database

Key Features

Milvus

  • 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

Weaviate

  • Built-in vectorization modules (OpenAI, Cohere, HuggingFace)
  • Hybrid search combining BM25 keyword and vector search
  • GraphQL and REST APIs
  • Multi-tenancy for SaaS applications
  • Generative search — query + LLM response in one call
  • Cloud-managed or self-hosted with Docker/Kubernetes

Real-World Use Cases

Milvus

Production-scale image similarity search

Extract image embeddings using a vision model (CLIP, ResNet)

Weaviate

Build generative search over your knowledge base

Start Weaviate with docker-compose (includes vectorizer module)

Integrations

Milvus

langchainllamaindexopenaihuggingface

Weaviate

langchainllamaindexopenaihuggingfacedspy

🏆 Which should you choose?

Choose Milvus if…

  • you're already in the Vector Database ecosystem and prefer Milvus's workflow
Full Milvus guide →

Choose Weaviate if…

  • you're already in the Vector Database ecosystem and prefer Weaviate's workflow
Full Weaviate guide →