The benefits for innovation with Kubernetes are clear: it can allow small teams to deliver more value, more rapidly. However, cost discussions around Kubernetes — and Kubernetes cost management — can be difficult. You have disposable and replaceable compute resources constantly coming and going, on a range of types of infrastructure. Yet at the end of the month, you just get a billing line item for EKS cost and a bunch of EC2 instances.

Even though January 1 has passed, it’s not too late to commit to a New Year’s resolution. “Right–sizing” might sound like the latest fad diet that promises easy weight loss results. But you didn’t come to this particular blog because (like me) your pandemic diet sometimes consisted of nothing more than take-out and never-ending snacks.

In the reliability era, many services are migrating from in-house servers to the cloud. The cloud model allows your service to capitalize on the benefits of large hosting providers such as AWS, Microsoft Azure, or Google Cloud. These servers can be more reliable than in-house servers for reasons including: However, as with all things, cloud providers present their own risks and challenges as well. Teams will want to take advantage of the benefits while accounting for these limitations.

The Splunk Deep Learning Toolkit (DLTK) is a very powerful tool that allows you to offload compute resources to external container environments. Additionally, you can use GPU or SPARK environments. In last Splunk blog post, The Power of Deep Learning Analytics and GPU Acceleration, you can learn more about building a GPU-based environment. Splunk DLTK supports Docker as well as Kubernetes and OpenShift as container environments.

Although AWS Lambda is a blessing from the infrastructure perspective, while using it, we still have to face perhaps the least-wanted part of software development: debugging. In order to fix issues, we need to know what is causing them. In AWS Lambda that can be a curse. But we have a solution that could save you dozens of hours of time. TL;DR: Dashbird offers a shortcut to everything presented in this article.

It has been nearly a year since Amazon Web Services (AWS) first rolled out its new savings plans. AWS Savings Plans were created to help manage cloud costs; however, they are best used as part of a larger strategy focused around cloud cost intelligence. (Keep reading for more on that!)

At Google Cloud, we strive to bring Site Reliability Engineering (SRE) culture to our customers not only through training on organizational best practices, but also with the tools you need to run successful cloud services. Part and parcel of that is comprehensive observability tooling—logging, monitoring, tracing, profiling and debugging—which can help you troubleshoot production issues faster, increase release velocity and improve service reliability.

Azure IoT Edge is a Microsoft Azure service that allows you to run containerized workloads on IoT devices. With IoT Edge and Azure IoT Hub, Azure’s device-management platform, organizations across science, manufacturing, energy production, and other industries can provision their IoT devices and workloads at the edge of their cloud networks for immediate in-unit computing, a necessity when running AI algorithms or parsing large datasets directly on IoT devices.

If you’re a product on the web, you have less than a second to impress your customers. Web application performance is crucial to the success of online products. Using a Content Delivery Network (CDN) like Cloudflare can provide just the edge you need.

As companies are increasingly built in the cloud, cost management has become a major issue for many engineering teams. While cloud providers like AWS offer flexibility and easy scaling, cloud costs can be opaque and difficult to track. As a result, many companies that rely on the cloud are adopting cloud cost management strategies to understand and manage the charges associated with their cloud-based technology, and maximize cloud usage and efficiency.