---
title: "GitOps"
description: "3 posts about GitOps, summarised, each linking to the original."
---

# GitOps
> 3 posts about GitOps, summarised, each linking to the original.

## Articles

### [Declarative clusters: GitOps deployment of hosted control planes on Red Hat OpenShift Virtualization](https://yomu.fyi/post/declarative-clusters-gitops-deployment-of-hosted-control-planes-on-red.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Serena Flocco
- Published: Oct 6, 2026
- Type: Tutorial

Hosted control planes for Red Hat OpenShift, based on the HyperShift project, decouple the cluster control plane from worker nodes by running control-plane components as pods inside a central management cluster. On bare-metal infrastructure, OpenShift Virtualization provisions worker nodes as virtual machines, enabling live migration and reducing hardware requirements. The article presents a declarative GitOps workflow using Helm templates and an Argo CD ApplicationSet that dynamically generates deployments from tenant-specific YAML configuration files in Git. Prerequisites include a bare-metal management cluster with OpenShift Virtualization, Red Hat Advanced Cluster Management, and OpenShift GitOps, plus networking configured with NMState, Multus CNI, and MetalLB. The approach achieves a “Cluster-as-a-Service” model where provisioning a production-grade cluster requires only a Git push.


### [Scaling GitOps: The "pull" architecture for global fleets](https://yomu.fyi/post/scaling-gitops-the-pull-architecture-for-global-fleets.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Jann Fischer, Pradeep Surisetty, Sho Weimer, Jan Hutař, Alex Krzos, Mike Ng
- Published: Sep 24, 2026
- Type: Benchmark

Red Hat conducted a performance and scale test of the argocd-agent pull architecture for GitOps, using 42 physical machines, 500 spoke clusters, and software including OpenShift 4.21 and OpenShift GitOps 1.20. The pull model decentralizes reconciliation to lightweight agents on each managed cluster, which pull configurations from a central hub, improving security by eliminating inbound administrative ports and reducing cross-cluster network traffic by up to 80%. In the bootstrap scenario, generating and syncing 31,200 applications across 480 clusters completed in 13 minutes 3 seconds, while the sync-to-commit scenario, which includes a polling delay, took 13 minutes. The test revealed bottlenecks including Kubernetes API limits on the hub, informer processing slowdowns causing watch restarts, event writer retry issues under load, and ApplicationSet controller RAM consumption. The team also deleted all 31,200 applications in 4 minutes 53 seconds using cascading deletion, and the insights are informing future OpenShift GitOps releases.


### [MongoDB Atlas Joins Google Cloud Foundation Fabric (FAST): Secure Private Data Infrastructure in Minutes](https://yomu.fyi/post/mongodb-atlas-joins-google-cloud-foundation-fabric-fast-secure-private.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 26, 2026
- Type: Announcement

Many organizations lack cloud fundamentals, making foundational setup complex and time-consuming; Google Cloud Foundation Fabric (FAST) is a GitOps-driven landing zone toolkit that accelerates establishing a production-ready Google Cloud organization. A new MongoDB Atlas module within FAST enables provisioning MongoDB Atlas clusters with Private Service Connect (PSC) for secure, private connectivity. PSC avoids VPC peering issues like CIDR conflicts and transitive routing, using a unidirectional consumer-producer model that respects project boundaries and routes traffic over Google’s private fiber network. The module automates cluster creation, PSC endpoint registration with Service Directory, and internal DNS resolution, integrating with Google Cloud KMS for customer-managed encryption keys. This integration reduces foundational setup from weeks to minutes, providing a secure data foundation that connects to BigQuery for real-time analytics and Vertex AI for GenAI workloads.