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Istio has been rightfully praised for ushering in free observability and secure service to service communication. Other, more significant features, however, are what truly make Istio the Swiss army knife of service mesh operators; when it comes to meeting SLOs like uptime, latency and error rates, the ability to manage traffic between services is absolutely critical. When we released the Istio operator earlier this year, our goal (besides managing Istio installation and upgrades) was to provide support for these excellent traffic routing features, while making everything more usable and UX friendly.
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NOTE: This is an updated version of a blog post we wrote nearly a year ago. It’s been extremely popular, however, due to the improvements and new features we’ve added to Bank-Vaults, it’s become outdated and in needs of a fresh coat of paint. A key part of the Banzai Cloud Pipeline platform, has always been our strong focus on security. We incorporated Vault into our architecture early on in the design process, and we have developed a number of support components to be easily used with Kubernetes.
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At Banzai Cloud we work with Istio quite a bit and run a lot of Istio-based service meshes for our customers. Earlier this year we opensourced the Banzai Cloud Istio operator in order to simplify provisioning, management, upgrades, and multi-cluster scenarios. However, we still felt that this was not simple enough. A few months ago we announced Backyards, a method of simplifying, creating and managing Istio, with planned support for all Istio features - the more complicated, the better!
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One of the main features of the Banzai Cloud Pipeline platform is that it allows enterprises to run cost effective workloads by mixing spot and preemptible instances with regular ones, and without sacrificing overall reliability. First, let’s dig into some of the components that make spot instances so reliable, then we’ll provide an example of a Pipeline control plane installation, submit some workloads and simulate a spot instance termination.
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At Banzai Cloud we develop a container management platform that provisions and orchestrates hundreds of Kubernetes clusters across six different cloud providers or hybrid clouds every day. Pipeline makes it easy for users to run applications on Kubernetes, but under the hood there is an insanely complex system, so it’s mission critical for us to have realtime information about what’s happening inside the platform and have access to historical data to investigate incidents our customers may have had.
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Here at Banzai Cloud, we provision and manage Kubernetes clusters on multiple cloud providers (Alibaba, Amazon, Azure, Google, Oracle) and on-premise (bare metal or vmWare) with our container management platform, Pipeline. We support both cloud provider-managed K8s distributions (ACK, EKS, EKS, GKE, OKE) and our own lightweight, CNCF certified Kubernetes distribution, PKE. Both these approaches have their pros and cons, though that’s not what we’ll be talking about today (we’ve blogged about this several times already, see Deploying Pipeline Kubernetes Engine (PKE) on Azure).
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At Banzai Cloud we do a lot more than work on Pipeline, our container management platform, and PKE, our lightweight CNCF certified Kubernetes distribution. In fact, we’re currently developing a variety of services that run on Kubernetes. These range from operators (Istio, Vault, Kafka, Logging, HPA to name a few), webhooks, K8s and cloud controllers to more general applications that we develop and test each day. During the development phase for these projects, we usually need to experiment and rapidly iterate applications, but, when using Kubernetes, this isn’t as easy as running each application inside a container.
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A few weeks ago we discussed the way that we integrated Kubernetes federation v2 into Pipeline, and took a deep dive into how it works. This is the next post in our federation multi cloud/cluster series, in which we’ll dig into some real world use cases involving one of Kubefed’s most interesting features: Replica Scheduling Preference.

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About a year ago, we wanted to publish a central Helm chart repository. It seemed like the easiest way to do that was from a single source, so we migrated all of our Helm charts to a central Git repository. The idea was to use CircleCI to build every chart, then upload the resultant charts to S3 and serve them from there. It wasn’t a perfect solution, though, since it made coordination with application releases and tracking issues more difficult.
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One of the key features of our container management platform, Pipeline is its ability to create multi- and hybrid-cloud Kubernetes environments using cloud provider-managed K8s or our own CNCF certified Kubernetes distribution, PKE. Recently, customers have been asking for a way to bring their existing Kubernetes clusters (upstream or other distributions) under Pipeline’s management, in order to benefit from the features our platform offers. During the peer review of our new cluster import feature, we realized the potential security risk created by the common practice of sharing kubeconfig files.
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