On January 27, 2021, Pinecone announced its vector database and a $10 million seed round led by Wing Venture Capital, launching the product as a public beta. Founded in 2019 by Edo Liberty, Pinecone packaged the kind of large-scale similarity search that big technology companies ran internally into a service any developer could call.
The product is described in the announcement as “a managed database for working with vectors.” It stores embedding vectors, the numeric representations of text, images, or other data, and answers the query that powers semantic search and recommendation: given this vector, which stored vectors are most similar? Pinecone handled indexing, querying, scaling, and operations behind an API, so teams did not have to stand up and tune their own approximate nearest-neighbor search clusters. The launch predated the ChatGPT-era surge in demand, but positioned the company well when retrieval-augmented generation made vector search a default ingredient in AI applications.
The launch marked the start of the managed vector-database category as a distinct product. Within a few years it was joined by competitors and by extensions to existing databases such as pgvector for PostgreSQL, reflecting how central embedding retrieval had become to building with language models.