Cloud Service Providers (CSPs) offer an ever-expanding array of instance types, ensuring that for any given workload there exists the perfect hosting option that matches the exact needs of that app or business service. But with this expansion comes an ever-increasing challenge to match the workloads to the offerings – there are many things to consider.
So, you’re looking for the right instance type for your public cloud workload, but how do you decide? Major cloud providers such as Amazon Web Services (AWS), Microsoft Azure Cloud and Google Cloud Platform (GCP) now offer such a large catalog of IaaS instances that it can become difficult to make sense of it all.
Implementing change in an organization always has its challenges, and for it to be successful it requires support from executive management. For one of our large Managed Service Provider (MSP) clients, their new CIO was focused on making their customer’s cloud consumption more efficient, with the outcome being cost savings.
On May 11th, at Intel Vision, Densify won Intel Partner of the year for Software Innovation. This award recognizes the impact of our jointly developed offering: Intel Cloud Optimizer (ICO). <="" a=""> Working with Intel we launched a program for enterprise customers that combines Densify analytics software and Intel expert guidance to help match customer workloads with the cloud instances that enable the best performance at the lowest possible cost.
The global chip shortage, which began in 2020, continues as demand for semiconductor chips continues to far outpace production. Intel CEO Pat Gelsinger recently forecast shortages to be sustained through at least the remainder of 2022. As a result, IT operations teams at almost every company we’ve talked with have felt the crunch in the form of skyrocketing prices and delays of up to a year for procurement of physical servers.
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.
