Weaviate vs Milvus

Side-by-side comparison to help you choose the best tool.

Weaviate

freemium

Data & Analytics

4.5 / 5.0

Weaviate is an open-source vector database that combines vector search with structured filtering, making it ideal for building production AI applications. It natively supports text, image, and multimodal embeddings, integrates directly with popular embedding models from OpenAI, Cohere, and Hugging Face, and offers both cloud-managed and self-hosted deployment options - giving teams maximum flexibility for RAG and semantic search systems.

Best for: AI engineers who want an open-source vector database with multimodal support and the flexibility to self-host or use managed cloud

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Milvus

freemium

Data & Analytics

4.4 / 5.0

Milvus is a cloud-native, open-source vector database built to handle billions of vectors at enterprise scale. Originally developed at Zilliz and donated to the LF AI & Data Foundation, it powers semantic search, recommendation systems, and AI applications at companies like Walmart and Shopee. Milvus supports multiple index types, GPU acceleration, and a distributed architecture - making it the most scalable open-source vector database available.

Best for: Enterprise engineering teams building billion-scale vector search systems for recommendation engines, semantic search, and AI applications

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Feature Comparison

Feature	Weaviate	Milvus
Pricing	freemium	freemium
Category	Data & Analytics	Data & Analytics
Rating	★★★★½ 4.5	★★★★☆ 4.4
Best For	AI engineers who want an open-source vector database with multimodal support and the flexibility to self-host or use managed cloud	Enterprise engineering teams building billion-scale vector search systems for recommendation engines, semantic search, and AI applications
Views	67	76

Pros & Cons — Weaviate

Pros

Open-source with self-hosting option
Native support for multimodal data
Strong hybrid search capabilities

Cons

More setup required than fully managed alternatives
Documentation can be complex for beginners

Pros & Cons — Milvus

Pros

Handles the largest vector datasets of any open-source option
GPU acceleration for ultra-fast indexing
Strong enterprise adoption and LF AI foundation backing

Cons

Complex to operate at full distributed scale
Heavier infrastructure requirements than lighter alternatives

Key Features — Weaviate

Open-source vector database
Native multimodal embedding support
Hybrid search (vector + keyword)
Built-in embedding model integrations
Self-hosted or managed cloud

Key Features — Milvus

Billion-scale vector search
Multiple index types (HNSW, IVF, DiskANN)
GPU acceleration support
Distributed cloud-native architecture
Python, Java & Go SDKs

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