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.

The FinOps Foundation sets out six FinOps principles cloud-based companies should follow to achieve and maintain optimal control over their cloud spending and cost efficiency. On the surface, they seem straightforward enough. The first principle, for example, is that teams should always collaborate in real-time to “continuously improve for efficiency and innovation” when it comes to staying on top of software development cloud costs.

Another year, another quantum leap in public cloud spending. 2022 saw organizations spend $491 billion on the public cloud; not to be outdone, 2023’s $563.6 billion marked the first year that public cloud spending exceeded half a trillion dollars. Accelerating cloud spend mixed with shaky macroeconomic conditions meant one thing: Efficiency has never been a higher priority for cloud-driven organizations.

To optimize costs, businesses must continuously assess the cost-effectiveness of their IT infrastructure. This article explores the financial implications of transitioning from traditional cloud IT infrastructure, characterized by elements like EC2, RDS, and non-containerized environments, to Kubernetes, a modern container-orchestration system. Traditional IT infrastructures have long been the backbone of many organizations, offering a certain level of predictability in cost and performance.

At CloudZero, we take special pride in helping our customers to optimize their cloud spend. We especially love it when a longtime customer has experienced so much success with our platform that they can’t help but sing their love of unit economics from the rooftops. In this interview, we sat down with Francis Pereira, Vice President of Infrastructure Engineering at CleverTap, to find out how CleverTap has evolved and grown during their years of partnership with CloudZero.

SaaS is a competitive sector, so finding ways to win on gross margin is increasingly crucial. By finding sustainable strategies that either increase your revenue or minimize your cost of goods sold (COGS), your SaaS company can gain a competitive advantage. Every SaaS brand wants to be profitable, but the definition of profitability varies greatly.

This is part three of seven in our Frugal Architect blog series. Read part one here, and part two 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”?

In traditional engineering organizations, development and operations teams are often siloed, a scenario that can lead to friction between them. For example, developers are encouraged to write and release more and better code. Operations engineers are responsible for preventing errors and bugs from affecting customer experiences. As a result, operations teams frequently serve as gatekeepers and can significantly slow deployments down – to ensure everything works first.