When it comes to choosing the right AWS container optimization solution, building a DIY suite of free optimizer tools may seem like a great idea, but will it provide everything you need? The hidden, total cost of ownership (TCO) for open-source tools can quickly build up, making it a less manageable option when compared to a third-party optimizer. Our helpful list of what you need to consider will help you decide which AWS container optimization solution is right for you: a DIY toolset or a managed suite.

Recently we announced the availability of a new version of Ocean for Azure Kubernetes Service (AKS). I decided to migrate some of our production services. While the service was already running on native AKS, we decided to leverage Spot’s Ocean for AKS to manage it more effectively, and, as they say, drink our own champagne. The outcome has been quite impressive. I managed to reduce our spending for this AKS cluster by a staggering 85%. You can see the results in the screen capture provided below.

Uncertainty continues to plague global economies, prompting many CFOs to optimize their bottom lines by re-examining large line-item costs, payrolls, and unit economics. Many IT leaders have been directed by their C-Suite to reduce or halt cloud spending, which represents roughly 30% of IT budgets.

The increased usage of containerization and virtual machines (VMs) in cloud infrastructure comes with its operational toll. Teams are preoccupied with complex and tedious tasks that prevent them from focusing on applications and architecture and driving the business value they can deliver. Organizations are seeking efficient solutions to optimize their cloud operations and enhance scalability.

If you are familiar with Spot Ocean, you may already know how quickly you start realizing the value it generates. Ocean offers automation and optimization of container workloads, while providing enterprise-grade service. Today we are glad to share that you can start realizing the value even faster, with Ocean’s new creation wizard. Ocean’s creation wizard is designed to simplify and accelerate setting up Elastic Kubernetes Service (EKS) clusters with Spot Ocean.

One contributor to successful research is the ability to process and analyze enormous amounts of data. The speed of accessing information and performing computations is crucial to research institutions and organizations looking to innovate and lead in their domain. Yet to process and analyze all that data, researchers need high-performance computing resources that can handle these workloads.

Amazon EC2 Spot Instances can help you save significantly on your compute costs. However, you should also be aware that Amazon can take them back with a two-minute notice if the demand for the instance type goes up. Fortunately, AWS EventBridge, along with Spot by NetApp, can help you automate the process of detecting and reacting faster to these interruptions.

You’ve deployed your Azure Kubernetes Services (AKS) cluster into production. Now what? Deploying AKS clusters is cause for celebration, but don’t rest on your laurels for too long. You are now in the Day 2 Kubernetes management phase and the operational challenges are on the rise. The Kubernetes application lifecycle is broken into three main phases. They are often referred to as Days, but realistically, they take much longer than 24 hours!

We are thrilled to announce the launch of our Azure CLI (Command Line Interface) Onboarding tool! This innovative addition significantly streamlines the process of integrating Azure accounts into the Spot platform. Until now, the process was manual, requiring several steps in the Azure portal that were time-consuming and, quite frankly, a tad cumbersome.

One of the most exciting implementations to come out of Apache Spark™ on Kubernetes is the dynamic creation, mounting, and remounting of a PersistentVolumeClaim (PVC) within a Spark application. We’ve spoken previously about how PVCs can be used to recover shuffle data and prevent application failure after a spot kill has occurred, negating one of the biggest drawbacks of spot instance usage in Spark workloads.