Milvus vs Weaviate: Comparison for Vector Databases
Compare Milvus and Weaviate on scale, hybrid search, and AI features. Discover which vector database best fits your RAG and AI projects.
Milvus
An open-source vector database for similarity search and AI applications. Milvus is built for scale — billions of vectors — with support for multiple index types. Part of the LF AI & Data Foundation.
Weaviate
An open-source vector database with built-in ML models and GraphQL. Weaviate supports vector + keyword hybrid search and has modules for embeddings, OpenAI, and more. Good fit for RAG with minimal configuration.
Comparison table
| Feature | Milvus | Weaviate |
|---|---|---|
| Architecture | Distributed, cloud-native | Single node or cluster |
| Hybrid search | Via separate components | Native vector + BM25 keyword |
| Embeddings | External — bring your own | Built-in modules (OpenAI, Cohere, local) |
| Query language | Python/Go SDK, REST | GraphQL, REST, Python/JS SDK |
| Scale | Very large — billions of vectors | Millions to tens of millions |
| Managed | Zilliz Cloud (Milvus) | Weaviate Cloud |
Verdict
Milvus focuses on maximum scale and performance. Weaviate wins on developer experience and integrated RAG. For most production RAG Weaviate is simpler. For very large datasets and custom pipelines choose Milvus.
Our recommendation
AVARC Solutions uses Weaviate for RAG projects with hybrid search. For clients with millions+ documents and strict latency requirements we evaluate Milvus/Zilliz. Weaviate's built-in modules significantly speed development.
Frequently asked questions
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