Kubernetes Operations for AI/ML Applications

Accelerate your Adoption of AI/ML Apps

The world of ChatGPT, OpenAI, and LLMs in AI is moving fast and it’s imperative that your company leverage the benefits before your competition. Building AI-powered applications is one thing, but the infrastructure setup and maintenance of these AI applications across your infrastructure is another (that’s why OpenAI runs Kubernetes). Rafay makes this easy with unified provisioning, lifecycle management, and monitoring of AI applications no matter where they reside.

With Rafay for AI/ML Applications, you can:

Provide a Self-Service Experience for Engineers and Data Scientists

Deploy, view, manage, and upgrade all of your Amazon EKS (& EKS-A) clusters in any AWS region using Rafay’s self-service workflows

Deliver World-Class Security and Governance

As AI/ML goes mainstream, Platform teams find themselves having to demonstrate that they are operating with world-class security and governance. With Rafay, enterprises enforce standards, RBAC, and have an end-to-end audit trail of all actions performed on Kubernetes clusters running LLM-based applications, for example.

Single Pane of Glass Management Across Public Clouds, Data Centers & Edge

Manage your entire fleet of AI/ML applications from a single pane of glass - across AWS, Azure, GCP (and others), in your on-premises data centers, and at the edge. Leverage a single, consistent GPU-specific dashboard to deploy, view and manage clusters and workloads across all your clusters.

Accelerate Your Migration to Artificial Intelligence (AI) Applications

Do you have a deadline by which you need to deploy AI/ML applications? With Rafay, your AI/ML clusters and LLM workloads will be up and running in days and your apps will be deployed in even less time.

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Key Kubernetes Challenges for AI/ML in the Enterprise

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Key Features for Kubernetes Operations for AI/ML Applications

With Rafay, you have one console to manage the operations of all your AI/ML applications (including LLMs) without having to install custom software, operational processes or dashboards.

Integrated GPU and Kubernetes Metrics

Rafay automatically captures and aggregates both Kubernetes and GPU metrics at the controller in a multi-tenant time series database. These metrics are then made available to users when they log in, governed by RBAC.

Unified Management of AI/MLApps

Organizations require a unified, central management platform for all AI/ML clusters in use spanning both data center, cloud-based and edge environments. Rafay acts as a single pane of glass to manage the deployment and lifecycle of all your AI and LLM applications.

Secure Remote Access

Users with very different roles and responsibilities (i.e. data scientists, operations, FinOps, security, contractor, 3rd party ISVs) need access and visibility into the health metrics for the underlying compute, storage infrastructure, GPUs, and their applications.

Cluster and Workflow Standardization

Rafay’s Cluster Blueprints creates and manages version-controlled standards fleet-wide for core components and software add-ons that are deployed on AI/ML clusters.

Multitenancy for AI/ML Apps

It is incredibly common for enterprises to have different teams share clusters – perhaps with specific LLM resources – in an effort to save costs. Rafay’s multi-modal multi-tenancy capabilities can easily support multiple AI/ML teams on the same Kubernetes cluster.

"The big draw was that you could centralize the lifecycle management & operations."

Beth Cohen

Cloud Technology Strategist, Verizon Business

"Rafay’s thought leadership and white glove support has been fantastic."

Kumud Kalia

CIO

"Rafay’s unified view for Kubernetes Operations & deep DevOps expertise has allowed us to significantly increase development velocity."

Alec Rooney

CTO

"Rafay stood out from the crowd with their deep integration with Amazon EKS."

Jayant Thakre

VP Products

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