How Organizations Can Take FinOps to The Next Level with Automation

As the realm of public cloud services expanded and companies transitioned their applications, monitoring and reporting on cloud expenses swiftly became an integral task for every team. However, alongside the benefits of saving data center costs, this migration introduced an array of new financial hurdles for many organizations. However, a common thread emerges—many cloud migrations, with their lift-and-shift approach, prioritize immediate flexibility and scalability over long-term cost considerations. Consequently, discussions around Cloud ROI have gained prominence as organizations grapple with the ramifications of an unfolding economic downturn. So, how can businesses reclaim a robust Cloud ROI? One established avenue involves the implementation of FinOps practices for cost monitoring and effective management. This blog will help you understand how companies can leverage FinOps with automation to achieve better business outcomes.

First, let’s explore the common issues associated with FinOps in today’s world.

Challenges Associated with FinOps

At the onset of a FinOps journey lies the pivotal step of attaining cost visibility—an essential element in maintaining control over cloud expenditures. This visibility streamlines the attribution of expenses to specific teams or projects, facilitating swift investigation of cost anomalies or sudden spikes.

To rein in cloud costs, organizations often turn to diverse cost monitoring, reporting, and allocation tools like CloudHealth by VMware or Apptio’s Cloudability. However, this approach typically hinges on manual tasks involving resource tagging and configuration, demanding consistent human intervention. Over time, these solutions prove labor-intensive and struggle to scale efficiently. Furthermore, successful FinOps implementation relies on fostering a culture of cloud cost awareness among teams—a process that demands substantial training in allocation, tagging, monitoring, and reporting of cloud expenses.

Ultimately, the goal isn’t to scrutinize every cent of the cloud budget. Organizations seek to curtail hefty cloud bills and identify savings vital for weathering challenging economic landscapes ahead. Distinguished FinOps leaders shift their focus from mere visibility to tangible cost reduction strategies, aiming not just to see the costs but to actively mitigate them.

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Significance of Cloud Cost Reduction

While cost visibility is a crucial milestone, it isn’t the ultimate objective—cost reduction holds that distinction. The substantial effort invested in attaining cloud cost visibility can sometimes negate any potential savings realized in the process. However, there exists an approach that has consistently yielded significant results across diverse cloud domains: automation.

Automation solutions are pervasive throughout the entirety of the software development lifecycle in cloud-native applications. While primarily associated with technical domains, certain automation tools wield a direct influence on curtailing cloud expenses.

Automated Standardization

Following cloud migrations, teams often undergo methodological shifts, embracing agile development practices prevalent in cloud environments. These teams prioritize rapid iteration, frequent application deployments, and the assurance of robust infrastructure reliability. A critical requisite for modern cloud-native development involves the seamless integration of infrastructure and application code within a unified environment.

In response to these demands, teams leverage automation for deployments and adopt Infrastructure-as-Code (IaC) methodologies. Open-source solutions such as Terraform by HashiCorp facilitate the integration of infrastructure code into projects, storing it within repositories and subjecting it to version control akin to other code components. This approach enables swift execution by every team member, facilitating the deployment of similar environments for expedited development processes.

Automation In Development Pipelines

Automated deployment represents just a segment of the broader narrative. Another domain witnessing profound transformation through automation is the development pipeline—a pivotal process guiding software development from inception through building, testing, and deployment. This industry-standard practice is encapsulated by Continuous Integration and Continuous Development (CI/CD).

CI/CD aims to mitigate human error while ensuring a uniform release process, thereby amplifying team efficiency. Leveraging automation-driven pipelines constructed with tools like Jenkins, GitLab, or CircleCI empowers organizations to confidently construct and deploy applications into production environments. This approach catalyzes heightened business agility and pace, facilitating swift adaptation and progress.

Autoscaling Resources for Optimal Utilization

This practice directly influences cloud expenses by aligning with engineers’ efforts to ensure uninterrupted application availability during peak usage, catering to their customer base.

Teams have long understood that cloud environments seldom operate at maximum capacity round the clock. Autoscaling, employing two primary approaches:

• Horizontal autoscaling: adjusting instances of a resource, either scaling out or in.
• Vertical autoscaling: modifying resource capacity within its limits.

Integrating a continual capacity management strategy through autoscaling mandates a comprehensive understanding of demands and their forecasting. When executed adeptly, autoscaling enhances both availability and cost management. The growing interest in autoscaling is evident even among cloud providers—AWS introduced Karpenter, an open-source autoscaling tool that enhances the native Kubernetes Cluster Autoscaler.

Automated Resource Rightsizing

Rightsizing entails the strategic calibration of cloud resources to achieve an optimal blend that minimizes waste while delicately balancing risk and cost considerations.

In the realm of scalable and on-demand cloud resources, accurate sizing facilitates teams in aligning workload demands precisely, ensuring an optimal customer experience. This facet of cloud cost optimization is swiftly evolving, witnessing a surge in tailored solutions catering to specific technologies, such as Kubernetes.

Automation Opens the Door to FinOps 2.0.

Engineers harness the array of methods outlined above not only to ensure seamless application operations aligned with business needs but also to wield control over costs.

Given the substantial impact of automation tools in resolving industry challenges, it begs the question: why not employ them for cloud cost management? Venturing down this path can usher in FinOps 2.0—an evolution beyond mere monitoring and reporting of cloud costs. Automation emerges as the catalyst, empowering teams to significantly curtail the time and resources dedicated to managing cloud expenses across the entire application lifecycle.

Final Thoughts

The journey towards effective cloud cost optimization begins with achieving cost visibility but extends far beyond, culminating in tangible cost reduction. This progression, however, encounters challenges, as the effort to attain visibility sometimes negates potential savings. Automation emerges as the transformative force reshaping this landscape, permeating various cloud domains. From streamlining deployments and integrating infrastructure-as-code methodologies to fortifying development pipelines and optimizing resource utilization through autoscaling and rightsizing, automation revolutionizes cloud management. This evolution transcends traditional monitoring and reporting, heralding the era of FinOps 2.0—a paradigm empowered by automation. Engineers leverage these tools not only for seamless operations but also to exert control over costs, freeing resources for strategic initiatives. Automation becomes the linchpin liberating teams from laborious tasks, steering them towards a future of optimized cloud expenditures and heightened operational agility.