Blueprints & Drift Detection

Standardize, Curate and Enforce Cluster Configs Across Your Fleet

Centralize configurations for cluster standardization encompassing software add-ons and governance policies while ensuring continuous reconciliation with drift detection across dev, QA, and production environments.

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Standardize Deployment of Clusters and Kubernetes Software Addons Required by Your Enterprise

With Rafay, platform teams can ensure that clusters are in always in compliance with internal policies and regulatory requirements

Ensure that clusters are configured with a set of baseline components and policies across environments and clouds

Create, share and mandate pre-configured templates that enable consistency and ease of deployment

Implement drift detection and reconciliation based on version controlled Blueprints to eliminate out of band changes

Standardize Cluster Configurations

Ensure consistency of clusters by allowing your developers to select from a set of preapproved cluster configurations. Once a custom Cluster Blueprint has been created, it can be used during initial provisioning or applied to existing clusters in a declarative way using GitOps. Blueprints can be created centrally and shared across projects ensuring the entire enterprise—across departments and across the globe—is using standardized clusters.

Golden Blueprints

Eliminate repetitive configuration changes by sharing a baseline of addons and policies that allow for customizations by internal teams without compromising security or governance.

Prevent Drift from Desired State

Implement drift detection and automate reconciliation to block unintended configuration changes without relying on having to implement bespoke monitoring systems.

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The Definitive Guide to Kubernetes Automation and Governance for Platform Teams

Leverage this Guide to Maximize your Kubernetes Investments

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SVP Chief Product Officer, Verizon Business

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VP Products

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CIO

Blogs from the Kubernetes Current

GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

December 18, 2024 / by Mohan Atreya

GPUs underpin cutting-edge AI, machine learning, and big data workloads. They also provide critical acceleration for simulation, video rendering, and streaming tasks. With modern enterprises likely to be investing in some or all of these fields, easy access… Read More

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