Cloud‑Native Storage Solutions for Large‑Scale Vector Data with Milvus and Zilliz

The presentation introduces Zilliz, the creator of Milvus, an open‑source vector database designed for large‑scale AI workloads, and outlines its core features: distributed architecture, high‑concurrency queries, low latency, linear scalability, and flexible node‑based expansion.

Supporting tools include Towhee, an open‑source ETL platform for extracting embeddings from unstructured data, and GPTCache, a semantic cache that stores large‑language‑model responses to reduce latency and cost.

Zilliz Cloud offers a fully managed SaaS version of Milvus with pipeline capabilities, simplifying deployment, monitoring, and scaling for AI applications such as Retrieval‑Augmented Generation (RAG).

The article explains the fundamentals of embeddings, unsupervised feature extraction, and semantic similarity measurement, then describes Approximate Nearest Neighbor (ANN) search algorithms like HNSW and inverted‑file structures used for efficient vector similarity search.

Key storage challenges are discussed, including indexing overhead, heterogeneous vector‑scalar data handling, and the need for read‑write separation, data sharding, and tiered storage across memory, NVMe, SSD, and object stores.

Design details cover sealed and growing segments, asynchronous index building, and the role of Data Node, Query Node, and Index Node services in a micro‑service architecture.

Performance metrics such as latency, throughput, recall, storage density, and compression are highlighted, with Zilliz’s VectorDB Bench benchmark showing Milvus’s advantages.

Typical use cases showcased are code‑assist (e.g., Vanna AI) and question‑answer systems (e.g., OSSChat), both leveraging vector stores for fast semantic retrieval.

Finally, the product matrix displays Milvus (open‑source and Lite), Zilliz Cloud, BYOC, and a new serverless offering, emphasizing that specialized vector databases are essential components of modern AI infrastructure.