Zilliz raises $60m for cloud vector database

Startup Zilliz has raised $60 million to boost engineering and go-to-market efforts for its cloud vector database.

In July we wrote that Zilliz, founded in 2017 with $53 million funding, had developed the Milvus open-source vector database. It’s aimed at helping AI applications turn unstructured data into intelligent, usable information for applications such as new drug discovery, computer vision, recommendation engines, and chatbots.

Charles Xie, Zilliz founder and CEO, said: “Milvus has now become the world’s most popular open-source vector database with over a thousand end-users. We will continue to serve as a primary contributor and committer to Milvus and deliver on our promise to provide a fully managed vector database service on public cloud with the security, reliability, ease of use, and affordability that enterprises require.”

We understand that vector databases are designed to index vector embeddings for search and retrieval by comparing values and finding those that are most similar to one another. A vector embedding consists of numeric values in arbitrary dimensions, hundreds or even thousands of them, that describe a complex data object.

These objects can be as simple as words, and move up the complexity scale to include sentences, multi-media text, images, video, and audio sequences. 

Machine learning processes are used to put unstructured data – so-called vector embeddings – as objects into the database. The vector database carries out basic create, read, update and delete (CRUD) operations on its object content and provides metadata filtering. It can then be searched without users needing to know specific keywords or metadata classifications for the stored objects. The search term is processed into a vector using the same machine learning system used to create the database’s contents (embedded objects). The returned results can be identical or similar (near-neighbor) to the search term. 

We might imagine a vector database of images could be asked to find all occurrences of a kitten, for example, or a film/TV program website could provide recommendations to a user based on their viewed video material.

Zilliz says modern AI algorithms use feature vectors to represent the deep semantics of unstructured data, necessitating purpose-built data infrastructure to manage and process them at scale. 

Its fully managed offering is currently in private preview for early access on Zilliz Cloud. This is available by invitation to customers for testing and feedback before becoming more broadly available. The long-term idea for Zilliz Cloud is for it to become a fully managed vector database-as-a-service (DBaaS) providing an integrated platform for vector data processing, unstructured data analytics, and enterprise AI application development.

The funding round is an extension to Zilliz’s initial $43 million Series B round, and was led by Prosperity7 Ventures, a diversified growth fund under Aramco Ventures, with participation from existing investors Temasek’s Pavilion Capital, Hillhouse Capital, 5Y Capital, and Yunqi Capital. Total Zilliz funding is now $113 million.

The $60 million cash influx follows profound growth from Zilliz. Milvus downloads crossed the one million mark, tripling from 300,000 downloads a year ago; production users grew by 300 percent; GitHub stargazers grew 200 percent to over 11,000; and the number of contributors doubled.

To apply for early access to the Zilliz Cloud preview, fill out the form here.