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Generative AI

Infrastructure Templates for Generative AI on AWS

We constantly hear from our customers about wanting their developers to experiment with Generative AI. No organization wants to be left behind and they are all trying to find ways to empower their developers and application teams to be able to experiment with use cases powered especially by Generative AI.

According to recent Gartner research, >80% of enterprises will have used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026.

We have been listening to our customers and are happy to announce Rafay's Templates for AI & Generative AI. Platform teams can now provide their developers with a self service experience for Gen AI infrastructure enabling developers to experiment with new and innovative Generative AI use cases.

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Announcing Rafay's Templates for AI and Generative AI

We constantly hear from our customers about wanting their developers to experiment with Generative AI. No organization wants to be left behind and they are all trying to find ways to empower their developers and application teams to be able to experiment with use cases powered especially by Generative AI.

According to recent Gartner research, >80% of enterprises will have used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026.

We have been listening to our customers and are happy to announce Rafay's Templates for AI & Generative AI. Platform teams can now provide their developers with a self service experience for infrastructure so that developers can experiment with new and innovative AI and Generative AI use cases.

Gen AI Logo

Vector Databases for Generative AI on Kubernetes

Many of our customers use Kubernetes extensively for AI/ML use cases. This is one of the reasons why we have turnkey support for Nvidia GPUs on EKS, AKS, Upstream Kubernetes in on-prem data centers. Recently, we have had several customers look at adding support for Generative AI to their applications. Doing so will require looking at a slightly different technology stack.

Traditional relational databases are adept at handling structured data. They do this by storing data in tables. However, AI use cases are focused on handling unstructured data (e.g. images, audio, and text). Data like this is not well suited for storage and retrieval in a tabular format. This critical technology gap with relational databases has opened the door for a new type of database called a Vector Database that can natively store and process vector embeddings. The rapid rise of large-scale generative AI models has further propelled the demand for vector databases.

In this blog, we will review why vector databases are well suited and critical for AI and Generative AI. We will then look at how you can deploy and operate vector databases on Kubernetes using the Rafay Kubernetes Operations Platform in just one step.