Pinecone is a vector database provider founded in 2019 by the former Research Director at AWS Edo Liberty, and is headquartered in San Francisco with additional offices in New York City and Tel Aviv. Pinecone aims to be a leading provider of vector databases, an important component of the increasingly standard Retrieval Augmented Generation (RAG) architecture for generative AI applications. As one of the first, and highest profile movers in this space, Pinecone is already showing many promising growth signs and indications that it is on track to become a household name in the AI industry.
With the sudden explosion of generative AI applications hitting the market, a new approach to data management is needed that complements how these models work. More conventional databases, which use scalar values to index data, struggle to interoperate with large language model (LLM) applications that involve more open-ended, complex and often vague natural language requests. While possible to integrate LLMs with more conventional scalar databases, to fully leverage the technology, vector databases are favored for their ability to represent unstructured data and to search for items on the basis of the item's meaning and similarity to others.
Furthermore, when dealing with potentially sensitive factual data, it is imperative that LLMs working with that data do not play “fast and loose” when interpreting and presenting it. Vector DBs help with the problem of hallucination by furnishing the LLM with an external knowledge base. Vector databases more naturally align with the workings of neural network models, providing what is effectively a factual “long term memory” store for them.
Pinecone is for developers working on AI applications and companies looking to provide AI with factual knowledge bases and data specific to their platforms or services.
Having now explained what Pinecone does and why it’s doing it, we will now define and explain two key technical concepts at the heart of Pinecone’s product: vector databases and retrieval augmented generation.
A vector database is a type of database that processes data differently than more conventional scalar, tabular, databases. Unlike more traditional databases, vector DBs index data using a mathematical structure called a vector embedding rather than a scalar primary key or strict unique identifier. Everything a vector DB contains is mapped to what is called a vector or semantic, space, which differs significantly from the grid or tables used in more traditional scalar databases. Items in vector dbs are retrieved based on how closely a request’s vector comes to a corresponding similar item in the vector space. What this means is that vector DBs are better suited to unstructured data retrieval based on similarity or “semantic search”. This makes vector DBs a natural fit for NLP applications such as chatbots that need to access and retrieve privately held data in addition to answering queries on the basis on its general knowledge acquired during model training.
Because user requests for such chatbot applications are often delivered in less precise natural language or images, rather than say, an SQL statement, the AI benefits from the flexibility of vector embeddings to translate that verbal query into a closely matching result by applying the approximate nearest neighbor (ANN) algorithm over the vector space.
Vector DBs are designed to work best with unstructured data, meaning data that isn’t readily formatted to the cells of a grid or table. Because all data, regardless of format, is stored as vectors, vector DBs have the advantage of considerable flexibility over more traditional scalar databases. Vector DB’s “type agnosticism” means it can retrieve video clips, pdfs, plain text, images, or any other type of content without requiring special rules or buckets for each data type. All data is mapped to the same vector space, regardless of its content.
RAG is a software architecture design pattern for AI applications that is rapidly growing in popularity and is arguably becoming an industry standard approach. The term derives from a 2020 paper by researchers at Facebook AI Research, University College London, and New York University. ^1. RAG addresses the problem of pairing AI programs, often NLP chatbots, with factual, domain specific data in addition to the model’s more generic, domain general “parameterized” learnings. For example, if one were to ask vanilla ChatGPT for one’s bosses’ email address, ChatGPT would not know the answer. An internal HR AI assistant at the company, however, might be built with RAG, and so can retrieve such domain-specific data from the company database that has been integrated with the chatbot functionality.
The RAG architecture also permits NLP apps to associate its statements with sources to provide citations and similar factual supporting material, improving the traceability, credibility, and explainability of these model’s behavior.
The goal of RAG is to make such AI apps more factual and informed by giving it access to domain-specific data and encouraging it to rely on this information as its main “source of truth”. The RAG pattern has had much success in mitigating the problem of AI hallucinations—the tendency for models to make false claims to fill in gaps in its knowledge with its own confabulations. RAG architecture directs models to favor the provided source of truth over and above the models own speculations. ^2
Pinecone’s flagship product is its (optionally) serverless vector database. According to its website, ^3 Pinecone advertises itself as providing the means to build “knowledgable AI”. It claims to deliver results that are fast, accurate, and cheap, with 51ms query latency, 96% recall accuracy, at up to 50x lower cost. ^4. Pinecone’s vector DB solution comes equipped with SDKs for popular programming languages, a unified implementation-agnostic API, and works with any commercially available AI model. It has integrations with many popular AI service providers. Pinecone’s product is richly featured, and supports hybrid search (pairing it with more conventional keyword lookups), real time updates, and metadata filtering.
Pinecone bills itself as the most popular vector database provider on the market (ibid). According to Crunchbase ^5, Pinecone has a total of $138M in funding after 4 funding rounds. Pinecone’s customers and partners include established factors such as Microsoft, Notion, Accenture, GoDaddy, and Zapier. ^6 For such a young company at only 5 years old, these signs are encouraging. Pinecone is also one of the first companies to provide such a sophisticated "plug and play" solution, which means it is well positioned from the start for continued success and can even set the bar by which other solutions are judged.
Pinecone is by no means alone in providing a vector DB solution. Its competitors include the open source Qdrant and ElasticSearch, and solutions offered by major companies such as Amazon’s CloudSearch and Microsoft’s Azure Cognitive Search. It’s worth noting, however, that Pinecone’s for-profit status, ample funding, and specialization in the vector DB field means it can attract world-class talent dedicated to the subject and focus all its resources and attention on improving its vector DB solutions. This gives it an advantage over free and open source projects, which rely on less dependable volunteer contributions, and major tech companies, who offer a diverse array of products and services and may therefore not prioritize their vector db capabilities above everything else they offer. Pinecone has both the resources and motivation to provide the best vector DB solution on the market.
Considering Pinecone’s already proven track record and industry forecasts that predict significant growth in the AI field over the coming years ^7, it’s a safe bet that should these trends continue, Pinecone is positioned to enjoy greater success in the future. The company’s basic proposition, that vector DBs are an essential component to “knowledgable AI”, seems to be increasingly accepted among the engineering community, and is already widely implemented across a variety of popular platforms and services now in production.
It should be noted that the AI field is fast moving and unpredictable. New advances are appearing at a hectic pace. So there’s no telling if some other as of yet unforeseen technology or methodology might show up on the scene and displace vector databases and RAG as the perceived best practice. Noting how much companies have already invested in proven vector DB integrated RAG systems, however, it’s both plausible and probable that these technologies and methods have cemented their status as go-to solutions for the foreseeable future, and with it, Pinecone's fortunes.
[1]: https://arxiv.org/pdf/2005.11401.pdf [2]: https://thenewstack.io/reduce-ai-hallucinations-with-retrieval-augmented-generation/ [3]: https://www.pinecone.io/ [4]: https://www.pinecone.io/product/ [5]: https://www.crunchbase.com/organization/pinecone [6]: https://www.pinecone.io/customers/ [7]: https://www.forbes.com/advisor/business/ai-statistics/