Infrastructure as Code for Kubernetes

Automate and Govern the Full Kubernetes Lifecycle Through Code

With Rafay, enterprises increase speed of deployments and improve the consistency of their Kubernetes infrastructure by leveraging Infrastructure as Code (IaC) best practices and tools like Terraform

By using Rafay’s declarative model to power our IaC initiative, we’ve eliminated application and cluster configuration drift and accelerated deployments by 4x

Vice President of Architecture

F500 Financial Services Company

Extend Terraform with Rafay’s Validated Terraform Provider

Rafay fully supports IaC driven through HashiCorp’s Terraform. Our official Terraform provider has been validated by Hasicorp and provides a seamless way to enhance Kubernetes services into your existing Terraform workflows.

https://rafay.co/wp-content/uploads/2022/10/Terrafom-Diagram.svg

Automate More Than Just Cluster Provisioning

Take More Action in Terraform with 
Ready Built Kubernetes Services from Rafay

Configure & Manage Multi-tenancy

Enterprises can implement both hard and soft tenancy providing multiple levels of isolation boundaries within their organization across different orgs/tenants, projects, workspaces, namespaces, and operating environments.

Manage Users, Groups & Access

Organizations can establish and implement clear separation of duties across functions with support for fine-grained role-based access.

Manage Cluster Blueprints & Drift

Create and easily apply cluster blueprints so it’s easy to standardize your Kubernetes clusters deployed across clouds, data centers and the edge. Get notified and even block clusters from being misconfigured.

Configure & Maintain Backups

Enable disaster recovery and migration of the Kubernetes control plane and application data.

Configure & Manage Network Policies

Centralize control of ingress and egress traffic ensuring that only legitimate traffic is allowed to and from the applications reducing the lateral attack surface – fleet-wide.

Application Configuration Templates & Addons

Ensure every new application is configured the way it is supposed to and has the correct software addons applied.

Integrate & Automate with CI/CD Pipelines

Enable continuous delivery, infrastructure orchestration, and application deployment through multi-stage, git-triggered pipelines.

Manage Security Policies & Addons

Enable policy management for clusters via the Open Policy Agent (OPA) framework for Kubernetes security and governance.

Benefits of Leveraging Rafay for your Infrastructure as Code Initiative

Standardize Clusters

Centrally define, manage and enforce cluster blueprints and workload templates across your environments. Get notified if they change in the wild

Reduce Time-to-Deploy

Leverage Rafay’s GitOps pipelines, Terraform provider, CLI or APIs to continuously provision Kubernetes clusters and for multi-cluster application deployment

Fast Track Day-2 Operations

Utilize Rafay’s turnkey Kubernetes ecosystem tool integrations to upgrade Kubernetes versions and add-ons in minutes, not days.

Self-Service Workflows with Guardrails

Enable stakeholders from engineering, ops and QA to quickly manage their own team's infrastructure with predefined templates that limit access and scope to their apps and clusters.

Blogs from the Kubernetes Current

Image for GPU PaaS Unleashed: Empowering Platform Teams to Drive Innovation

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

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

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

Image for Operationalizing AI: Solutions to Machine Learning Workflow Automation Challenges

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