The benefits of containerizing workloads are numerous and proven. But, during infrastructure transformations, organizations are experiencing common, consistent challenges that interfere with accurately forecasting the costs for hosting workloads in Kubernetes. Planning the proper reservations for CPU and memory before migrating to containers is a persistent issue Densify observes across our customers.
A lot of people think that using the cloud makes you elastic, but cloud really only gives you the potential to be elastic. If your apps aren’t elastic, and your cloud usage doesn’t flex up and down based on activity levels, then it’s really just expensive. The same is true of containers—they don’t magically make all of your hosting problems go away, and while they have the potential to make you more agile and efficient, it isn’t that simple.
For those of you who aren’t familiar with AWS CodePipeline, it’s a continuous integration and continuous delivery (CI/CD) framework that enables application development teams to deliver code updates more frequently and reliably. You may have also heard it being called a CI/CD or DevOps pipeline. These pipelines have always traditionally been used to deploy the components of a certain application whenever new code in “checked-in”.
To allocate costs or maximize savings? That’s the question many AWS customers will have to answer when formulating their FinOps strategy relating to Savings Plans. If part of your daily routine is finding ways to cut cloud costs, then you know exactly what I’m talking about. Appropriate chargeback reporting is an undeniable necessity for any business, but in this world of uncontrolled rising cloud costs, so is capitalizing on any savings opportunities!
A few years ago, we realized that spending in our AWS product test environment had jumped significantly from one month to the next. We drilled down into the issue and traced it to some RDS database instances that had been spun up to test new product features. No one realized that these expensive instances were left running after the tests were complete, and subsequently racking up charges for several months.
For many years after the inception of Compute Engine on June 28, 2012 (GA December 2, 2013), N1 was the only Google Cloud machine type family offered.
Cloud is a driving force that is reshaping organizations to adapt their people and processes in embracing this game-changing technology. Recently, Mike Fonseca of HashiCorp published a Guide to Cloud Cost Optimization with HashiCorp Terraform, in which he details how Densify can seamlessly integrate with Terraform in to help ensure your enterprise infrastructure is always cost optimized.
Toronto, Ontario – October 10, 2020 – Densify, a leader in cloud and container cost management, today announced that its product has been Red Hat Operator Certified and can be purchased in the Red Hat Marketplace. The Marketplace is a one-stop shop to find, try, buy, and manage enterprise applications across an organization’s hybrid IT infrastructure.
I’ve been writing about continuous cloud optimization for a while now, and recently, I’ve spoken with several organizations to understand any challenges they’re currently facing in their automation journey. Their insights would help us understand how we can improve our technology to better support them. I discovered two fundamental themes behind their challenges.
This year’s AWS Summits are more than a little different. Despite being virtual due to the COVID-19 pandemic, AWS is packing these events with lots of content. I’ve had a great time attending virtually, trying out hands-on labs, and sitting in on the technical sessions. As I work primarily with cost management and optimization for cloud compute services, this has been my main interest in attending the conferences. Here are my main takeaways around better managing your AWS cloud costs.
