Directory AI & ML PlatformsVector Databases & RAGMilvus / Zilliz

Milvus / Zilliz

About Milvus / Zilliz

Zilliz offers a fully-managed vector database service designed for enterprise AI applications that require high performance, scalability, and reliability. Built on the open-source Milvus engine, Zilliz Cloud simplifies the deployment and scaling of vector similarity search applications by eliminating infrastructure complexity. It supports billion-scale vector search, enabling enterprises to efficiently handle large volumes of unstructured data for AI-driven insights.

The platform is tailored for enterprises needing robust vector search capabilities integrated with leading AI models and frameworks. It provides high availability with 99.95% uptime SLAs, multi-cloud deployment across AWS, Azure, and GCP, and security compliance including SOC 2 and ISO 27001. Zilliz Cloud also offers built-in embedding pipelines for converting unstructured data into searchable vectors, making it suitable for use cases like Retrieval Augmented Generation (RAG) and other AI-powered search scenarios.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Milvus / Zilliz. 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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