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.