Zilliz is a fully managed Vector Lakebase platform built by the team that created Milvus, officially positioned as a "Vector Lakebase for AI." It is not merely a vector database, but rather unifies real-time vector search, lake-scale data discovery, and AI data operations on the same data source, covering three types of workloads: real-time serving, iterative exploration, and batch analytics, and claims it can run each at an appropriate cost at data scales in the hundreds of billions.
Purpose and positioning Zilliz is aimed at applications and teams that need large-scale vector retrieval and AI data infrastructure. Its official description says its users include scenarios such as entity search and multilingual RAG systems; customer cases cited in the materials mention that it provides real-time retrieval for Exa's entity search, scaled services for a healthcare-related platform, and multilingual RAG systems, helping free up engineering resources and allowing teams to focus on model-side inference optimization rather than infrastructure management.
Main capabilities
- Full-spectrum search: supports multiple data types from vectors and text to JSON and geospatial, combining hybrid retrieval, filtering, and reranking to enable more expressive multimodal queries.
- Lake-native storage: provides unified storage for serving and analytics, built on Vortex, an open next-generation format. It is officially said to be up to 10x faster for random reads than Lance, lower cost, and offers the flexibility of a columnar format.
- Real-time serving: provides a tiered architecture that optimizes different workloads through flexible tiers, covering "performance-optimized solutions," "capacity-optimized solutions," and "tiered storage solutions," to balance ultra-high performance, balanced efficiency, and cost-effective scaling on very large datasets.
- Large-scale multi-tenancy: supports unlimited namespaces, hybrid vector, full-text, and JSON search, and supports hot and cold data serving; supports multi-region deployment, replication, and failover to provide low-latency, highly available global access, supporting AI applications in rapidly expanding globally.
- On-demand compute and on-demand search: pay per query rather than for provisioned compute, and it is officially said to significantly reduce costs at large scale compared with serverless approaches.
- Seamless backfill and schema iteration: can backfill online and evolve schemas and data models without affecting serving, designed for continuous AI iteration.
- Bring indexes to your lake: an optional access mode that operates directly on user S3 data (Iceberg, Lance, Vortex, Parquet), with data remaining in the user's own bucket, indexes built and served on Zilliz, with no copying and no ETL.
- CLI: An official command-line tool provided for management, search, and analysis, officially described as "Your Vector Lakebase, your terminal, full control."
Reliability and Scale It is officially stated to be built on a deep understanding of failure modes in large-scale vector databases, having undergone 8 years of production testing with over 10,000 enterprises. In terms of scale, it is officially stated to be designed to handle over 100 billion entities and over 10,000 QPS while maintaining consistent latency and predictable performance. In terms of cost, all data and indexes are stored in S3, combined with hot caching and on-demand compute, and it is officially stated to reduce costs by 90%.
Usage and Availability Zilliz offers it as a fully managed service on Zilliz Cloud, officially stating that "you can get started in minutes"; user data remains in their own VPC, while the rest is managed by Zilliz. In terms of availability, it is officially stated to cover global regions across AWS, Google Cloud, and Azure. The materials also mention migration from lake data, Milvus, Elasticsearch, and other vector databases, as well as the ability to run Milvus in your own environment. The materials mention the "Zilliz Vector Lakebase Public Preview" announcement, indicating that the product is in public preview. Specific installation commands, SDK examples, pricing, and billing details are not yet provided in the materials.
Use Cases and Limitations Use cases include: real-time retrieval requiring vector data at the scale of hundreds of billions, multimodal and hybrid retrieval, multi-tenant AI applications, global multi-region deployment, continuously iterating systems that require online backfilling and schema evolution, and teams that wish to build indexes directly on their own object storage to avoid data duplication and ETL. Limitations and information not yet provided: The official materials do not give specific prices and billing standards, a complete list of cloud regions, SLA details, quota and limitation descriptions, complete installation and integration commands, supported language SDKs, specific steps for data migration, or the functional boundaries during the public preview stage. The above content should be subject to the latest official documentation, and this article does not infer or supplement it.
