AI Suite

MLOps & LLMOps Your Way,
In Any Cloud

Operate AI and Gen AI workflows in public, private or sovereign clouds.

What is the AI Suite?

Rafay’s product suite for AI extends our PaaS offering to help platform teams provide developers and data scientists with a standards-based pipeline for MLOps and LLMOps. They can easily build an integrated, consistent experience that accelerates the deployment of AI applications and GPU workloads.

This permits them to use the most strategic technologies available, wherever they reside, without needing to learn the intricacies of how to make them work.

Accelerate AI/ML development with a turnkey MLOps solution

Deliver an enhanced developer experience with an all-in-one MLOps platform, complete with GPU support, a company-wide model registry and integrations with Jupyter Notebooks and VSCode IDEs. Support for Kubeflow, MLflow, and Ray Train & Serve facilitate rapid model development.

Provide a curated and scalable LLMOps playground for GenAI experimentation

Help developers experiment with GenAI by rapidly training, tuning, and testing GenAI apps with approved models, vector databases, inference servers, cloud LLMs and more.

Centrally manage LLM providers and prompts

Built-in prompt compliance, cost controls on public LLM use such as OpenAI and Anthropic ensure developers consistently comply with internal policies.

Manage AI data source integrations and governance

Leverage pre-configured integrations with enterprise data sources such as Databricks and Snowflake, while controlling usage for AI application development and deployments.

Enable on-demand consumption of accelerated computing infrastructure

Let developers and data scientists vend preconfigured GPU workspaces on demand, sized by the number of GPUs, cores, and amount of memory, and apply dynamic scheduling and matchmaking for optimum performance.

Deliver a sovereign GPU cloud in weeks

Deliver sovereign GPU clouds faster, by integrating provisioning, orchestration, and management with policies and controls. This streamlines access to accelerated computing and private data, while adhering to local data protection requirements.

What do MLOps platform teams get with the AI Suite?

Built on a proven enterprise-grade platform for Kubernetes operations, Rafay helps platform teams build a standards-based pipeline for MLOps and LLMOps that provides their developers and data scientists with an integrated, consistent experience. This permits them to use the most strategic technologies available, wherever they reside, without needing to learn the intricacies of how to make them work.

Harness the Power of AI Faster

Developers and data scientists shouldn’t have complex processes preventing them from building, training and tuning their AI-based applications. A turnkey MLOps and LLMOps platform with self-service capabilities allows them to be more productive without worrying about infrastructure details.

Reduce the Cost of AI Development

By utilizing GPU resources more efficiently with capabilities such as GPU virtualization and time-slicing, enterprises reduce the overall infrastructure cost of AI development, testing and serving in production.

Avoid Tool or Cloud Lock-In

Platform teams need an infrastructure-agnostic AI PaaS solution that supports de facto standards and open source options, so they can provide the same, self-service experience for data scientists and ML engineers working across clouds - public or private.

Private and Sovereign GPU Cloud Usage in Days, Not Years

A PaaS for private and sovereign GPU clouds enables businesses and their customers to leverage high-performance computing capabilities sooner, while ensuring data sovereignty and compliance requirements are met.

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Scale AI/ML Adoption

Delve into best practices for successfully leveraging Kubernetes and cloud operations to accelerate AI/ML projects.

Download White Paper

Scale AI/ML Adoption

See for yourself how Rafay delivers the automation developers and operations want with the right level of standardization, control and governance platform teams need.

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