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.

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.

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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.

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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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CIO

Blogs from the Kubernetes Current

Image for Optimizing AI Deployments with Training-as-a-Service Platforms

Optimizing AI Deployments with Training-as-a-Service Platforms

November 8, 2024 / by Mohan Atreya

As artificial intelligence continues to reshape industries, the demand for efficient, scalable training solutions has surged. Training-as-a-Service (TaaS) platforms are emerging as essential tools for developers, data architects, and platform engineering teams working in AI model development. By… Read More

Image for Unlocking the Potential of MLOps as a Service: Streamlining AI and ML Pipelines

Unlocking the Potential of MLOps as a Service: Streamlining AI and ML Pipelines

November 6, 2024 / by Mohan Atreya

A New Era in AI and ML Operations Managing ML models effectively is more crucial than ever in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML). From healthcare to finance and retail, industries are… Read More

Image for Training as a Service: Empowering AI Teams with Managed Model Training Solutions

Training as a Service: Empowering AI Teams with Managed Model Training Solutions

November 4, 2024 / by Mohan Atreya

Artificial intelligence is rapidly evolving, and the ability to efficiently train AI models is essential for competitive advantage. As applications scale, organizations face growing complexities around model training— from managing extensive datasets to securing infrastructure that supports continuous,… Read More