Directory AI & ML PlatformsVector Databases & RAGZilliz

Zilliz

About Zilliz

Zilliz offers a fully-managed vector database service built on the open-source Milvus platform, designed specifically for enterprise AI workloads requiring high performance and scalability. The Zilliz Cloud service simplifies deploying and scaling billion-scale vector similarity search applications by removing infrastructure complexity, enabling organizations to focus on business logic rather than operations. It supports multi-cloud deployment across AWS, Azure, and GCP, ensuring global availability and flexibility.

Targeted at large enterprises, Zilliz Cloud delivers blazing fast vector retrieval speeds, high availability with 99.95% uptime SLAs, and robust security compliant with SOC 2 and ISO 27001 standards. Its optimized indexing and distributed architecture allow it to handle over 100 billion items with ease, making it suitable for AI use cases such as retrieval augmented generation and unstructured data search. Integration with leading AI models and frameworks further enhances its capability to convert unstructured data into actionable insights.

How to evaluate Vector Databases & RAG

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