Pinecone raises $28 million to advance vector database

Vector database startup Pinecone Systems today announced that it has raised $28 million in a series of Series A funding rounds to help grow its technology and go-to-market efforts.

The San Mateo, Calif.-based provider was founded in 2019 by Edo Liberty, who spent nearly seven years working at Yahoo on machine learning, followed by three years at Amazon Web Services where he worked on AI projects, including Amazon Sagemaker.

Liberty holds a doctorate in computer science from Yale University, where her thesis focused on random projection, a machine learning technique that has a direct impact on vector databases.

With a vector database, rather than just using keywords to determine relevance, content is converted into mathematical tables. AI algorithms in the database help determine the proximity of the vectors to determine the relevance of the query.

In this Q&A, Freedomwho is CEO of Pine cone, talks about the vendor’s vector database technology.

Why are you now raising a Series A to develop Pinecone vector database technology?

Edo Freedom: We raised a very big $10 million fundraising round a little over a year ago, and we had a lot of lead left. So we hadn’t planned to raise at all. We launched our commercial product in October 2021 and traction has exploded.

The commercial product is a fully managed cloud service. Users query the data through their applications. They don’t have to worry about machines, availability, backups, etc. We take care of everything.

The $28 million that we raised did not come from the mountains. It was based on our planning to help support the rapid growth we have and the need for engineers, product managers, customer success, and developer relations staff. We charted it for the next 18-24 months and the number was around 28.

How is a vector database different from a graph database?

Freedom: Different databases specialize in different types of data.

Graph databases mainly care about relationships between objects. A social network is a standard example of a graph. In a social network, the relationships you are interested in are pairwise relationships between objects in your data. A vector database cares about vectors, which are just numeric arrays. This is how deep learning networks represent data. This data can be text, images or anything else.

The vector is a semantically rich representation of the objects and it is what is saved in your data. So you query this vector to work with the data. Deep learning models and machine learning models in general are digital objects. If you want to apply math to anything, you have to use numbers.

How can Pinecone Vector Database work with traditional data structure for analytics and business intelligence?

Freedom: AI and vector search capabilities are inherently soft and fuzzy and hard to pin down for traditional data engineers doing BI and data analytics.

One of the hardest things about building something like Pinecone is combining traditional data structure and traditional database capabilities with this advanced AI-based vector search, and we did that.

Editor’s note: This interview has been edited for clarity and conciseness.


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