Zilliz, the startup behind the Milvus open-source vector database for AI applications, raises $60M and relocates to SF – TechCrunch





In 2020, Chinese startup Zilliz — which builds cloud-native software to process data for AI applications and unstructured data analytics, and is the creator of Milvus, the popular open source vector database for similarity searches — raised $43 million to scale its business and prep the company to make a move into the U.S. Nearly two years on, today Zilliz is announcing further funding of $60 million as it finally makes its move West, with a new HQ in San Francisco to capitalize on the growing demand for more efficient processing techniques for an ever-expanding trove of unstructured data getting commandeered to power AI applications.

The funding is being led by Prosperity7 Ventures, a $1 billion venture fund created by Saudi oil giant Aramco (the name is a reference to the first commercial well to strike oil in the country), with Chinese backers Temasek’s Pavilion Capital, Hillhouse Capital, 5Y Capital, and Yunqi Capital — all previous investors — also participating. The company is not disclosing its valuation, but it’s worth pointing out that this latest injection is being described as an extension to that $43 million Series B rather than a new round. We’ll update this if we learn more. The total raised by the company is now $113 million.

The capital and relocation speaks not just to key moment for the company, but also for the area of machine learning and wider trends impacting Chinese-founded startups.

On the first of these, Zilliz’s breakout product, the open-source Milvus, has boomed. The company said that downloads have now passed the 1 million mark, compared to 300,000 a year ago, with production users growing by 300% in the same period, though it didn’t disclose its active user number. Customers include the likes of eBay, Tencent, Walmart, Ikea, Intuit and Compass.

As we pointed out back in 2020, Milvus has not leaned heavily on advertising and marketing spend, instead choosing to leverage word of mouth on the places where developers like to hang out for ideas and inspiration to get itself noticed, such as Github and Reddit. That strategy has worked: “Stargazers” on Github are up 200% to more than 11,000 with the number of contributors doubling. (For a point of comparison, in 2020 it had been starred some 4,400 times.)

The reason for the interest in Milvus — and subsequently Zilliz’s roadmap, which is based around creating further products, most recently a Zilliz Cloud managed service that is now in private preview; and Towhee, another open-source framework, this one for for vector data ETL — is because of the rising interest in vector databases as how they are being used in AI applications.

Put simply, while data can be (and often is) processed via more traditional databases, the complexity of and structure of activities like anomaly detection, recommendation, rating and other AI-driven tasks lends itself more naturally and efficiently to vector databases designed to work with how AI data is represented. (Zilliz notes that its vector database “is both cloud native and capable of processing billion-scale vector data in milliseconds.”)

“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, in a statement. “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.”

There are others (competitors) like Pinecone and Weaviate also building solutions to address this; and big cloud providers like AWS also have their own solutions. All of that points to a market opportunity that Zilliz is focused to tackle.

It’s notable that back in 2020, Zilliz already said that more than half of the users of Milvus were outside of China: that speaks to how the company has long positioned itself and where it saw its growth longer-term. These days, a number of startups in the country are looking to move elsewhere to have more freedom in terms of how they grow their businesses, and to work with a wider set of customers, and Zilliz is an example of that in action.

Prosperity7 has been playing a role in facilitating this migration. The fund only entered China last year and has been actively hunting down startups with a global ambition, which could tap the firm’s vast global network. We recently covered two such investments, Jaka, a Beijing- and Shanghai-based collaborative robotics startup, and Insilico, an AI drug platform from Hong Kong.

Prosperity7’s investment in Zilliz seems to fit nicely into the investor’s mandate. It’s not uncommon to see Chinese SaaS companies going global these days. Many are started by Chinese entrepreneurs with an international background. They might have spent a few years testing the market at home and raising from VCs who are increasingly keen on B2B projects as the B2C space becomes saturated. But many find it hard to monetize in China, where small and medium enterprise owners are still reluctant to pay for software subscriptions compared to their Western corporate counterparts.

“With its leadership on Milvus, Zilliz is a global leader in vector similarity search on massive amounts of unstructured data,” said Aysar Tayeb, executive MD of Prosperity7 Ventures, in a statement. “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.”




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