Zilliz

Zilliz provides a fully managed Vector Lakebase built on the open-source vector database Milvus. This platform is designed to handle enterprise-scale AI workloads by unifying real-time vector search with lake-scale data discovery and batch analytics. Unlike traditional vector databases that focus solely on low-latency nearest neighbor search, Zilliz’s Vector Lakebase treats vector data as a first-class citizen within a data lake architecture. It supports three core operational modes: real-time serving for sub-millisecond query responses, iterative discovery for exploratory analysis and model training data curation, and batch analytics for large-scale offline processing. Users can ingest, index, and query billions of vectors using a single source of truth, while independently scaling compute and storage resources to optimize cost. The platform integrates with object storage systems like Amazon S3, where data is stored in a columnar format with hot caching for frequently accessed vectors. On-demand compute allows users to spin up ephemeral clusters only when needed, reducing idle costs. Zilliz also offers a CLI tool for programmatic interaction, enabling developers to deploy and manage vector pipelines directly from the terminal. A built-in Zilliz Agent assists with troubleshooting, performance tuning, and schema design. Enterprises can use Zilliz for a wide range of AI applications including semantic search, recommendation systems, anomaly detection, image and video similarity, natural language processing embeddings, and retrieval-augmented generation (RAG). The platform supports multiple programming languages and frameworks, with native SDKs for Python, Java, Go, and REST APIs. It also provides hybrid search capabilities that combine vector similarity with scalar filter