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DirectoryAI & ML PlatformsVector Databases & RAGMilvus

Milvus

Open Source

About Milvus

Milvus is an open-source vector database built for GenAI applications. It can be installed with pip and used to perform high-speed searches. It scales to tens of billions of vectors with minimal performance loss. It includes features such as metadata filtering, hybrid search, and multi-vector support.

Used for

  • Performing image search.
  • Performing multimodal search.
  • Performing hybrid search combining dense and sparse vectors with filters.

Who it is for

  • Used by developers building GenAI applications.

From Milvus's own website, read 2026-10-23. How we build listings.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Milvus. It comes from our Vector Database & AI Search buyer guide.

25%
Retrieval Quality & Index Options
ANN algorithms offered (HNSW, IVF/IVF-PQ, DiskANN, GPU/CAGRA), achievable recall@k at your latency budget, quantization for memory savings, and whether the index supports real-time inserts or needs rebuilds
20%
Hybrid Search & Filtering at Scale
Native vector+keyword (BM25 / sparse) fusion, reranking, and — critically — in-graph metadata pre-filtering that holds recall under selective filters rather than degrading to brute force or empty result sets
20%
Scale, Performance & Freshness
Vector ceiling per node and horizontally, p95/p99 latency and QPS under your real filters, multi-tenancy and namespaces, multi-region, and how fast new or updated embeddings become queryable (insert-to-searchable lag)
15%
Operating Model & Lock-In
Managed SaaS vs. self-hosted vs. bring-your-own-cloud, license terms (Apache 2.0 / BSD vs. proprietary), data-export and migration path, and how much sync/ETL you take on if vectors live apart from the source data
10%
Security, Governance & Residency
RBAC and API-key scoping, encryption in transit and at rest, SOC 2 / ISO 27001 / GDPR posture, data residency and VPC/air-gapped options, and inherited governance (e.g. Unity Catalog, platform IAM) where applicable
10%
Developer Experience & Ecosystem
Client SDKs and API ergonomics, LangChain / LlamaIndex / framework integrations, built-in or hosted embedding and reranking, docs quality, and operational maturity (backups, observability, upgrades)

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Quick Facts

milvus.io
CategoryAI & ML Platforms
SubcategoryVector Databases & RAG

We publish a detail only when we can point at the page it came from. Know when Milvus was founded or where it is based? Send it to us, free.

Keep reading

Buyer Guide
Vector Database & AI Search
Job 02
Govern the AI already in use

Staff already use AI tools nobody approved, and most SaaS products now ship AI features by default.

Job 05
Modernize the estate

AI agents need clean data and systems they can call, and old systems block both.