Zilliz
By Zilliz, Inc.
Zilliz is the company behind Milvus, the widely used open-source vector database, and it offers Zilliz Cloud, a managed vector database service built on the same technology. Vector databases store high-dimensional embeddings produced by…
Definition
Zilliz is the company behind Milvus, the widely used open-source vector database, and it offers Zilliz Cloud, a managed vector database service built on the same technology. Vector databases store high-dimensional embeddings produced by machine learning models and support similarity search over them, which underpins applications like semantic search, recommendation systems, and retrieval-augmented generation for large language models. Zilliz packages this capability as hosted infrastructure so teams do not need to operate a Milvus cluster themselves.
Overview
Zilliz was founded to solve a problem that emerged as embedding-based machine learning became common: once data is represented as dense vectors, finding the nearest or most similar vectors to a query at scale requires specialized indexing that conventional relational or document databases handle poorly. The company built Milvus as an open-source answer to that gap and later launched Zilliz Cloud so organizations could get the same capability without running distributed infrastructure themselves, positioning Zilliz as both an open-source project steward and a commercial cloud vendor. Mechanically, a vector database ingests embeddings — numeric arrays produced by models such as sentence encoders or image encoders — and builds approximate nearest neighbor indexes (methods like HNSW or IVF) over them so that similarity search returns results in milliseconds even across billions of vectors, rather than requiring an exhaustive comparison against every stored vector. Milvus and Zilliz Cloud additionally support hybrid search that combines vector similarity with traditional metadata filtering, partitioning for multi-tenant isolation, and horizontal scaling across nodes for large collections. Zilliz sits alongside other vector database offerings such as Pinecone, Weaviate, and Qdrant, all of which solve the same core similarity-search problem but differ in deployment model, indexing internals, and how much operational control they expose. Compared to adding a vector extension to an existing relational database like pgvector for Postgres, Zilliz and Milvus are purpose-built for vector workloads at large scale, trading the simplicity of an extension for dedicated performance and scaling headroom. It differs from a general analytics platform in that its core operation is similarity search, not aggregation or reporting. In practice, Zilliz is used to power retrieval-augmented generation pipelines that fetch relevant document chunks for a language model, semantic search over unstructured content such as text or images, recommendation engines that match users to similar items, and deduplication or anomaly detection systems that rely on embedding similarity. Teams building LLM applications are a particularly large share of current adopters, since RAG systems depend on fast, accurate vector retrieval to ground model outputs. The trade-offs mirror those of any specialized data store: introducing a vector database means managing another system alongside existing relational and document stores, and approximate nearest neighbor search sacrifices some retrieval precision for speed, which matters for applications with strict accuracy requirements. For small-scale similarity search needs, a lightweight in-process library or a vector extension on an existing database may be simpler than adopting a dedicated Zilliz or Milvus deployment; the case for it strengthens as vector volume, query throughput, or multi-tenant isolation needs grow.
Key Features
- Managed cloud service (Zilliz Cloud) built on open-source Milvus
- Approximate nearest neighbor indexing for fast similarity search at scale
- Hybrid search combining vector similarity with metadata filtering
- Horizontal scaling across nodes for billions of vectors
- Multi-tenant partitioning for isolating customer or workload data
- Support for multiple index types including HNSW and IVF
- Integrations with embedding models and LLM application frameworks
- Open-source Milvus core with commercial hosted tier