flagger VS gloo-mesh

Flagger

Where appropriate, canary releases are an excellent option, as the percentage of traffic exposed to the canary is highly controlled. The trade-off is that the system must have good monitoring in place to be able to quickly identify an issue and roll back if necessary (which can be automated). This guide shows you how to use Apache APISIX and Flagger to quickly implement a canary release solution.

What is Flagger? Flagger is a Progressive Delivery tool that automates the release process for applications running on Kubernetes. Under the hood, both tools are built on top of a modular GitOps toolkit. It is the main reason why Flagger compliments Flux.

flagger: Automated canary using istio/linkerd

I would suggest trying a-b deployment in this case using flagger. It will spin up a new instance and gradually rotate out old ones. Maybe that can be helpful in this weird situation :)

ArgoRollouts offers Canary and BlueGreen deployment strategies for Kubernetes Pods. It's a drop-in replacement for the v1.Deployment object. Flagger is similar what it offers, extending Kubernetes to support Canary and BlueGreen deployment strategies.

Check https://argoproj.github.io/argo-rollouts/ and https://flagger.app/

I'm only a K8s amateur and I've never used it in Prod, but I've used Gloo Mesh with Istio and EKS Anywhere to make a unified control plane between on prem and AWS-EKS. It was a pretty small scale deployment, but if you have low enough latency, it felt like I should scale pretty well.

Gloo Mesh is one of those products. It provides a good example of how to reduce the complexity of managing the entire application networking in your infrastructure to a minimum. As can be understood, this implies multi-cluster architectures.