Pinecone team (company photo)
Pinecone Systems, an Israeli startup that develops new types of search infrastructure, hit a $750 million valuation. The valuation came after a new $100 million Series B round of investment led by Andreessen Horowitz, with the participation of ICONIQ Growth and existing investors, Menlo Ventures and Wing Venture Capital.
Founded in 2019 by former Amazon developer Israeli Edo Liberty, Pinecone offers a fully managed vector database that the company says makes it easy to add vector search to production applications. It combines state-of-the-art vector search libraries, advanced features such as filtering, and distributed infrastructure to provide high performance and reliability at any scale.
“No more hassles of benchmarking and tuning algorithms or building and maintaining infrastructure for vector search.”
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“Generative AI gave us a boost, but we were growing long before that,” said Liberty. “We already have very significant revenue and the number of paying customers is growing at a dizzying pace. We released the product 15 months ago and have 1,500 paying customers, and it’s all been organic growth. The artificial intelligence models today are very smart and understand language and know how to summarize and search, but they are not good at managing data. They are language engines, not data, and what we do is develop long-term memory for the models. When companies build language models, they need something like our product to develop memory.”
Pinecone Systems explains that Vector databases are purpose-built to handle the unique structure of vector embeddings. They index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another. A vector database indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.
Vector databases, says Pinecone Systems, are a solution for powering ranking and recommendation engines. For online retailers, they can be used to suggest items similar to past purchases or a current item the customer is researching. Streaming media services can apply a user’s song ratings to create perfectly matched recommendations tailored to the individual rather than relying on collaborative filtering or popularity lists.
