One of the many things enterprises consider when developing commercially viable GenAI applications, is to make as much proprietary data available on-demand to the application as possible. The most efficient approach to enhancing the quality of GenAI applications and minimizing hallucinations, while maintaining complete control over the accessible data for GenAI models, is Retrieval-Augmented Generation (RAG).

The next big breakthrough in application quality comes from simply adding data but even with a traditional vector database, the cost to maintain the data can be prohibitive. When you look at legacy databases, they are not built to handle vector data at large scales and regardless of the database, developers still need to set up, provision, manage and maintain the infrastructure while being cognizant of spend.

Picture this: Developers can now input nearly unlimited knowledge into GenAI applications at up to 50x lower cost compared to legacy databases. This completely re-invented vector database is truly a serverless experience without the burden, manpower and expense required to provision, manage and maintain clusters on the backend. Developers will experience a significantly more efficient path to reliable, effective, and impactful GenAI applications regardless of the company size or GenAI maturity.

Serverless Design Principles:

• Separate read, write, and storage
• Load the least amount of data required to save on memory
• Focus on the cluster that matters the most.

Serverless Benefits:

• “Unlimited” index capacity via cloud object storage (ex. S3, GCS)
• Decreased cost to serve - only pay for what you use
• Lower cost for high availability

Introducing Pinecone serverless is just part of the equation. Pinecone’s objective is to provide a platform for developers that is the easiest to use. The best way to deliver that is to integrate with other best-in-class GenAI solutions. We’ve partnered with leaders like AWS, Anyscale, Cohere, Confluent, Langchain, Pulumi and Vercel on integrations that empower users to capitalize on the power of GenAI.

Learn more about Pinecone serverless integrations with these partners below:

When building RAG applications, embeddings are a critical component in powering vector databases used for search and retrieval. Read how Anyscale and Pinecone handle data ingestion and embeddings computations at scale, while still allowing for flexibility in the preprocessing step and embeddings model.

Pinecone serverless in combination with Cohere’s Embed Jobs simplifies the process of working with embeddings and is a powerful toolkit for deploying and scaling enterprise semantic search and RAG applications.

Confluent’s Pinecone Sink Connector allows organizations to easily access high-value data and harness the full potential of Confluent for any GenAI use case.

Pinecone is one of Langchain’s most popular vector database integrations. See how Pinecone serverless along with LangServe and LangSmith can address some of the challenges associated in deploying RAG applications.

The Pinecone Provider for Pulumi makes it easy for platform engineers and developers to maintain, manage, and reproduce infrastructure as code for their AI applications

See how RAG chatbots use Pinecone serverless and Vercel's AI SDK to demonstrate a URL crawl, data chunking and embedding, and semantic questioning.