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August 25, 2022

Vector Database Company Zilliz Raises $60M to Enhance Cloud Offering

Vector database company Zilliz announced a $60 million extension of its initial $43 million Series B round, bringing its total funding to $113 million. Originally based in China, the company has established a new headquarters in San Francisco.

Established in 2017, Zilliz develops vector database management systems for AI applications, including the management and processing of feature vectors for AI algorithms used to represent the deep semantics of unstructured data. “Unstructured data is the most important data type of our time,” the company wrote in a blog post. “The world needs purpose-built data infrastructure to manage and process feature vectors at scale and in real-time.”

The company is also the creator of and main contributor to Milvus, the open source vector database project started in 2018 that Zilliz touts as cloud native, highly scalable, and capable of processing billion-scale vector data in milliseconds. The company recently announced Zilliz Cloud, its fully managed vector database service built around Milvus.

“Zilliz’s journey to this point started with the creation of Milvus, an open-source vector database that eventually joined the LF AI & Data Foundation as a top-level project,” said Charles Xie, founder and CEO of Zilliz. “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.”

The fully managed cloud offering is currently in private preview and is available by invitation to current customers for testing and feedback. The company says its long-term vision for Zilliz Cloud is to be a DBaaS that provides an integrated platform for vector data processing, unstructured data analytics, and enterprise AI application development. Its features include pre-loaded Milvus for a fully managed experience, vector similarity search with low latency vector data retrieval on trillion-scale datasets with support for various index types and AI algorithms, elastic deployment capabilities including configurable auto-scaling, and SOC 2 compliant, full data encryption with role-based access control.

This graphic shows where Zilliz Cloud fits into enterprise-level AI development. Source: Zilliz

The company noted its growth in the past year, including how Milvus has now been downloaded over a million times, has seen its contributors double, and has been starred by Github users over 11,000 times for a 200% increase. Zilliz also created Towhee, an open source ETL framework for vector data. In addition to research recognition at the ACM SIGMOD 2021 and VLDB 2022 events, the company also won the BigANN challenge at NeurIPS 2021, a global competition focused on developing new and innovative approaches to billion-scale vector search.

The extended Series B round was led by Prosperity7 Ventures with participation from existing investors Temasek’s Pavilion Capital, Hillhouse Capital, 5Y Capital, and Yunqi Capital. Zilliz plans to use the funds to expand its engineering and marketing teams, as well as to further enhance its managed cloud offering.

“With its leadership on Milvus, Zilliz is a global leader in vector similarity search on massive amounts of unstructured data,” said Aysar Tayeb, Executive Managing Director of Prosperity7 Ventures. “We believe that the company is in a strong position to build a cloud platform around Milvus that will unleash new and powerful business insights and outcomes for its customers, just as data analytics platforms like Databricks and Snowflake have done with structured data. There is already over 4x more unstructured data than structured data, a gap that will continue to grow as AI, robotics, IoT, and other technologies meld the digital and physical realms.”

Related Items:

Zilliz Announces Key Contributions to Milvus 2.1

Taming the ‘White Whale’ of Unstructured Data

Are Neural Nets the Next Big Thing in Search?

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