Milvus
Distributed open source vector database with dense, sparse and full-text search, from laptop to Kubernetes.
About
Built for vector search at large scale, Milvus keeps embeddings next to scalar fields such as strings, numbers and JSON, and answers nearest-neighbor queries with metadata filters, range search, grouping and hybrid dense-plus-sparse retrieval, including built-in BM25 full-text search. Its engine, implemented in C++ and Go, offers HNSW, IVF, FLAT, SCANN and DiskANN indexes with quantized variants, and can build GPU indexes through NVIDIA CAGRA. There are three ways to run it: Milvus Lite inside a Python process backed by a local file, a single-node standalone server in Docker, or a distributed Kubernetes deployment that separates query, data and coordinator roles so each can scale independently. SDKs exist for Python, Node.js, Java and Go, alongside a REST API, the Attu admin GUI, and connectors for LangChain, LlamaIndex, Spark and Kafka. Logins, TLS and role-scoped permissions are included. The project sits under the LF AI and Data Foundation with an Apache 2.0 license, and Zilliz, its main contributor, sells a managed version. Fits teams building RAG or semantic search who expect their data to outgrow one machine.
Should you use Milvus?
Pick it when
Pick Milvus when you are heading for hundreds of millions to billions of vectors with streaming updates and high query rates, have Kubernetes skills on hand, and want an open-source engine with GPU acceleration available.
Look elsewhere when
Skip it if your corpus fits on one machine: Milvus Lite and standalone exist, but the distributed mode is its main strength and real operational work. Qdrant or Chroma are simpler, and Pinecone removes operations.
Alternatives to Milvus
- Qdrant
Rust engine with strong payload filtering that is simpler to operate for single-node and modest cluster workloads, with a managed cloud too.
- Pinecone
Fully managed serverless scaling with no capacity planning, trading away self-hosting and source access for zero operations.
- Weaviate
Built-in modules create embeddings at import and one query mixes vector, keyword, and RAG, so less glue code, while Milvus leans on scale-out and GPU acceleration.
- Vespa
Combines vector search with full ranking and ML inference at large scale, more powerful for complex ranking but steeper to learn.
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Details
- Category
- Vector Databases & Embeddings
- Price
- Freemium
- Platform
- Hybrid
- Difficulty
- Advanced (4/5)
- License
- Apache-2.0
- Added
- Jan 29, 2026