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.

As of 2023, 89% of companies are using a multi-cloud approach. The race to the cloud might feel truly like that – a race – but the finish line isn’t hybridization or even a full migration. Even after you’ve made it to the cloud, there’s still more work. Namely, determining cloud costs. Microservices, containers, Kubernetes made resource costs, associated costs and more are near-impossible to sort though.

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.

If you’re like most companies running large-scale data intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps activities to ameliorate or address this waste and the cost it incurs, things such as: … and the list goes on. These are infrastructure-level optimizations.