Zilliz

Zilliz is purpose-built for AI infrastructure workloads that require high-throughput, low-latency vector retrieval at scale:

• RAG (Retrieval-Augmented Generation): Serve as the vector store in LLM pipelines, enabling semantic document retrieval with hybrid dense + sparse search and JSON metadata filtering.
• Billion-scale ANN search: Run approximate nearest-neighbor queries across billions of embeddings with production-grade 99.95% uptime SLA.
• Multimodal search: Index and retrieve across text, image, audio, and other embedding types within a single collection.
• Batch analytics + real-time serving: The Vector Lakebase architecture supports both OLAP-style batch analytics and real-time query serving from the same S3-backed storage layer.
• Regulated industry deployments: BYOC and self-hosted Milvus options satisfy data residency and compliance requirements in healthcare and finance.
• Data lake integration: Native support for Iceberg, Lance, and Parquet enables vector search directly over existing lake assets without ETL duplication.