When Pinecone launched last year, the company’s idea was to create a serverless vector database designed specifically for the needs of data scientists. While this database is at the core of the company, it is evolving into a more sophisticated use case for this database for AI-based search, helping data scientists find the proverbial needle in a haystack.
When we spoke to Pinecone founder and CEO Edo Liberty during their $10 million seed round last year, his company just groped its way and built a database. He came from Amazon where he helped build the SageMaker database service. He says he has come a long way since then.
Liberty said: “A lot has changed since we announced our launch so we first launched our true paid production service in October and it has grown rapidly in terms of both adoption and revenue since then so things are going very Okay”.
He described the rationale behind setting up a dedicated database for data scientists during seed funding as follows:
“The data that the machine learning model expects is not a JSON record, it is a higher dimensional vector that is either a list of features or a so-called nesting that is a numeric representation of objects or objects in the world. [format] Much more semantically rich and suitable for machine learning,” he explained.
He says the semantically rich approach is what drives customers to use Pinecone today.” Mostly vector databases are used for searching and searching in the broadest sense of the word. It’s a document search, but search in general can be thought of as information retrieval, search, recommendation, anomaly detection, etc.,” he said.
The system is organized in the form of modules, which are a set of resources designed to process data in the Pinecone database. The company is offering one module for free to help customers get familiar with the product and run a simple proof of concept. After that, they start paying depending on the number of pods.
He is convinced that the company has designed the system in such a way that it can be scaled to billions of elements. “You can scale your software as much as you can, and you can really organize it. We designed the system in such a way that there is no well-defined limit on the amount of data that you can index and use,” he said.
Because it’s a serverless database, the customer doesn’t have to worry about provisioning, but Pinecone has to tell him how much he’s willing to spend each month based on the amount of data he needs to process.
“They’re pushing the envelope to see if the X Pods will be good enough for what we’re using in terms of the data and performance they’ll give me, and that’s it.” After that, a person simply logs in, and with a few clicks in the console and an API call to create an index, he is ready to go.
Liberty is reluctant to share growth figures or workforce numbers, but says it expects to double its workforce (whatever that means) next year. It is noteworthy that at the time of the announcement of the startup, 10 employees worked in the startup.
Regarding diversity, he said last year: “We have instructed our employers to be proactive. [in finding more diverse applicants]to make sure they don’t miss out on great candidates and bring in a wide variety of candidates for us.” In practice, he says, this has resulted in 50% of new tech workers (as opposed to the entire workforce) being women this year.
The company today announced a $28 million Series A led by Menlo Ventures with new investor Tiger Global as well as previous investors including Wing Venture Capital, which led the company’s seed funding. The company has now raised $38 million.