Accelerate Cloud-native Adoption

Go (Cloud) Native
In The Cloud Or
On-Prem

Leverage Rafay to standardize and centralize landing zones and
Kubernetes environments, deliver self-service workflows, and keep infrastructure costs low

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Are your Kubernetes investments growing YoY with no end in sight?

As enterprises modernize their applications, they quickly realize the significant increase in the cost and resources required to manage Kubernetes clusters. Rafay’s SaaS-first approach enables companies to gain efficiencies from Kubernetes- almost immediately, speeding digital transformation initiatives while keeping operating costs low as cloud usage grows.

Standardize and centralize Kubernetes management

Rafay enables platform teams to standardize every aspect of a cloud environment’s configuration – cluster & environment configs, resource allocation, approved addons, access controls, security policies, and much more – in one place.

Put EKS/AKS/GKE lifecycle management on autopilot

Rafay allows a small, central team to easily manage the lifecycle of all Kubernetes clusters and cloud resources being used by multiple BUs and hundreds of app teams, hosted across public clouds (AWS, Microsoft Azure, Google Cloud, Oracle Cloud and more). This enables end-to-end lifecycle management automations that eliminate the need for specialized teams.

Operate an EKS-like service in your data center

Why not have the same, great, public cloud-like managed Kubernetes experience but in your own data center?

Rafay provides turnkey management of Kubernetes resources hosted in private, remote, or edge clouds (including bare metal, VMware vSphere, local VMs, and support for their networking and storage needs). Now, your team can focus on higher-value work rather than the menial tasks of keeping Kubernetes operational.

Rightsize Kubernetes clusters and apps proactively

Optimize cloud cost by detecting and fixing resource allocation issues through intelligent policy-driven controls for workload management. This ensures Kubernetes and cloud resources are rightsized for better application utilization, performance and cost efficiency.

Focus on innovation, not on Kubernetes automation

Rafay provides a centrally managed, easy-to-operate platform
to manage Kubernetes and cloud environments, across
all private and public clouds to help companies realize the
following benefits:

Increase Team Productivity

Rafay customers operate more efficiently and with much smaller teams compared to DIYers or companies using competitive products.

Fill the K8s
Skills Gap

Maintaining platforms, scripts, access controls, or extensive IaC configs across different clouds costs time, and the skills needed to do so are difficult to acquire. Rafay’s automation eliminates the need for multiple teams with special skills.

Reduce the Cost of Kubernetes

Rafay helps platform and FinOps teams proactively reduce costs rather than just reporting them. It continuously analyzes spending patterns and automatically adjusts resources, eliminating repetitive manual intervention.

Download the White Paper

Scale AI/ML Adoption

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

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Most Recent Blogs

Optimizing AI Workloads for Multi-Cloud Environments with Rafay and GPU PaaS

November 27, 2024 / by Mohan Atreya

Rafay’s platform enables you build a GPU PaaS for AI workloads so you can confidently operate machine learning models, generative AI, and neural networks at scale. It orchestrates your hybrid and multi-cloud computing resources, improves operational flexibility, and… Read More

Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

November 15, 2024 / by Mohan Atreya

Machine learning (ML) has emerged as a transformative force, enabling organizations to derive critical insights, enhance customer experiences, and make data-driven predictions. However, operationalizing machine learning workflows presents significant challenges, especially for enterprises with complex, cloud-based infrastructures. Machine… Read More

Achieving Optimal AI Performance with Tuning-as-a-Service

November 12, 2024 / by Mohan Atreya

Tuning-as-a-Service (another TaaS but not to be confused with Training-as-a-service) is a cloud-based solution that optimizes AI models by automating the adjustment of hyperparameters to enhance model accuracy, efficiency, and overall performance. By leveraging advanced algorithms and scalable… Read More