While many of us have already heard of Kubernetes, or may even be leveraging it within our technology stacks, it’s still important to remember that the platform is undergoing massive adoption and evolvement. Due to its relative infancy, Kubernetes is ripe to allow for integrating new technologies, like Artificial Intelligence (AI) and Machine Learning (ML). As an open-source platform, Kubernetes orchestrates containerized applications, ensuring they run efficiently and resiliently.

We get it. FinOps can be challenging to understand, implement, and optimize, whether you’ve done it before or are just getting started. Everybody needs help from time to time. If this describes you right now, then check out the following FinOps services companies to get started. But first, a quick background.

If you’ve spent any time poking around our blog, you’ve noticed one thing: At CloudZero, we are passionate about FinOps. More than that, we’re passionate about bringing the most useful and up-to-date information on FinOps to our customers to help them better navigate their journeys toward cloud cost optimization.

This is part four of seven in our Frugal Architect blog series. Read the rest of the series here. In case you weren’t as giddy as CloudZero was at re:Invent this past year, we wanted to recount the seven laws outlined by Werner Vogels, Amazon’s CTO, which he’s bundled into a framework called “The Frugal Architect” (check out the whole framework here). What is “The Frugal Architect”?

Amazon Web Services (AWS) offers the convenience of choosing specific virtual machine combinations to meet your compute needs. Unlike traditional data centers, you can also scale your cloud resources automatically to meet fluctuating workload needs. The best part is that you can rightsize your workload requirements to specific instance types (VM types) offered by AWS. By hand, this takes a lot of time and is prone to errors. This is where AWS Compute Optimizer comes into play.

Datadog is like a Swiss Army knife for observability. Whether it’s cloud, applications, or infrastructure, Datadog can serve all your monitoring needs under one roof. This is with a level of integration that’s akin to having a universal remote for all your digital operations, from on-premises to cloud environments. The thing is, with great power comes a notable concern – cost.

Kubernetes has emerged as a pivotal force in shaping modern cloud infrastructure. Originating as a brainchild of Google, Kubernetes has evolved into an open-source platform that has revolutionized how applications are deployed, scaled, and managed across a vast network of machines. Its ability to orchestrate containerized applications efficiently makes it an indispensable tool within cloud computing. However, with great power comes great responsibility, particularly in the realm of cost management.

Cloud cost management and FinOps best practices rely on getting as much meaningful data as possible into decision-makers’ hands. Without the ability to pull data from all your various cost sources, you cannot get 100% visibility into your cloud spend — let alone make decisions to optimize that spend.

In an era where cloud computing has become the backbone of global business operations, its impact on the environment cannot be overlooked. As organizations increasingly migrate to the cloud, data centers’ energy consumption and carbon footprint have surged, highlighting a critical need for sustainable practices. One often underappreciated lever for environmental stewardship within this digital infrastructure is effective cloud cost management.

As organizations increasingly migrate to the cloud, managing expenses efficiently becomes crucial. Traditional cost management methodsoften fall short in this environment, where resource allocation and usage can fluctuate dramatically. Enter Artificial Intelligence (AI) and Machine Learning (ML). These cutting-edge technologies are revolutionizing the way businesses approach cloud cost management.