“We’d love to have you join our team,” he said. Eeks! I screamed in my head. You did it, Joanne! 

I happily accepted the role on the spot, hung up the phone, and this time really screamed.  

One week later, I started my first day as the senior content marketing manager at CloudBolt. 

Now don’t take my giddy excitement for inexperience—this is by no means my first rodeo. I’ve done content marketing at tech companies for quite some time now (10 years, to be exact) and have managed topics such as digital transformation, cloud computing, and AI across multiple industries. I can write technical articles, produce in-depth webinar series, and interview thought leaders like the best of them. 

There’s just one problem: I don’t know anything about FinOps. 

The Meaning of FinOps 

When I first heard the term “FinOps,” I naturally assumed it was short for “financial operations,” which entails managing financial transactions, budgeting, and accounting within an organization. I then considered the word “fintech” and thought perhaps it was related to financial technology innovations like digital payments or blockchain. But to my surprise, FinOps is nothing of the sort. The name is actually a portmanteau (or what I’d like to say, a mashup) of “Finance” and “DevOps.” But DevOps is a common methodology for software development—what does that have to do with finance?   

To get to the bottom of my confusion, I visited the FinOps Foundation for its official definition, which reads as follows: “FinOps is an operational framework and cultural practice which maximizes the business value of cloud, enables timely data-driven decision making, and creates financial accountability through collaboration between engineering, finance, and business teams.” The definition also accompanied a comprehensive framework including principles, personas, domains, and capabilities. Before me was an entirely untapped universe—and oh, was I diving in. 

A few days later—with multiple moments coming up for air—I think I finally understand. Rather than simply managing money, FinOps is about making money. It’s taking a strategic and agile approach to cloud spend and making real-time proactive decisions that produce the highest return on investment for the business. With this approach, the cloud functions as less of a cost center that keeps the rest of the business running and more of a profit center that directly pours more money into the business so it not only runs but grows. What do you think—am I on the right track? 

Learning from the Best at FinOps X 

Luckily for me, my education journey isn’t stopping any time soon. In fact, I’ll be getting a 2-day crash course when I make a pit stop at FinOps X, an annual conference hosted by the FinOps Foundation, in San Diego from June 19 to 22. Along with end-user practitioners, subject matter experts, and thought leaders, I’ll be soaking in the real-world FinOps stories and best practices from companies leading the way in innovation.  

Here is my list of top sessions that I want to attend: 

And of course, I’m not going to miss the panel that our chief technology officer Kyle Campos is hosting on Thursday, June 20, Adopting FinOps: Advanced Tips & Tactics. 

While I might be joining the party a little late, I’m glad to be here at a time when FinOps is evolving before our eyes. There’s still much to do in this space and learn from one another. I’ll be furiously taking notes at FinOps X, but if you see me, be sure to tap me on the shoulder and say hi! I’d love to meet you, hear your story about journeying into FinOps, and have you be part of mine.  

Imagine this: A cost anomaly is detected in your environment. You receive an automatic alert that the appropriate team has been notified to take immediate action. In just a matter of minutes, the issue is resolved, and AI and machine learning begin ingesting the data and further training the system. You sit back, take a sip of coffee, and dive into more strategic work. 

Pretty cool, right? This vision represents an ideal future of FinOps automation—where company culture, operations, and technology are so aligned that insight-to-action occurs in minutes, sometimes even seconds! 

As it stands today, however, the insight-to-action process can take weeks or months. That’s because the current state of FinOps automations is more like a series of episodic human activities than true continuous automation. FinOps practitioners still use spreadsheets or disparate tools to manually pull and aggregate data from various cloud providers, reconcile discrepancies, and analyze costs. Each step, from identifying cost-saving opportunities to implementing optimizations, is interrupted by approvals, reviews, and coordination among multiple stakeholders. This fragmented approach is extremely time-consuming and prone to inaccuracies.  

Traditional FinOps Automation 

To further illustrate this, consider the typical FinOps workflow broken down into four main steps: Acknowledge, Assign, Approval, and Action.  

Each of these steps introduces pauses and waiting periods, creating a disjointed, stop-and-go process that significantly delays the implementation of cost-saving measures. 

The Right, Shift-Left, Approach 

So how do companies overcome the roadblocks they’re facing in FinOps automation? The answer is clearly not to offload work to FinOps or DevOps teams themselves, which only adds to the unrealistic burden of managing an ever-growing complexity of cloud environments manually. Instead, it is shifting resolution left to advanced technology solutions that are designed to be the force multipliers to achieve the scale, accuracy, and automation required to truly solve advanced cloud cost problems such as waste reduction, chargeback/show back, unit economics, and optimized workload recommendations at the point of provisioning. 

CloudBolt’s Augmented FinOps solution is designed to address these challenges head-on by transforming episodic FinOps automation into a continuous, efficient, and impactful process. With our new capabilities, CloudBolt customers can leverage the following:  

Continuous Automation with Augmented FinOps 

To revisit the FinOps workflow illustration from earlier, here’s how the process looks like now with CloudBolt’s Augmented FinOps in action: 

At scale, these automated steps result in enormous savings and waste reduction, moving from simply sporadic gains to an ongoing, cumulative pile of benefits.  

While we may not yet be soaking in FinOps’s perfected state of automation, CloudBolt’s Augmented FinOps moves us one step closer to the collective vision. By drastically reducing insight-to-action lead time, eliminating manual toil, and enhancing collaboration, our new service advisor experience empowers organizations to maximize their cloud ROI and achieve seamless, efficient cloud operations.  

To learn more about CloudBolt’s Augmented FinOps, talk to us at FinOps X or visit cloudbolt.io/demo today. 

Since Google released it to the open-source community ten years ago, Kubernetes has quickly become a cornerstone technology for orchestrating and managing software containers and microservices. According to a Cloud Native Computing Foundation (CNCF) survey, Kubernetes is used in 96% of global businesses, and its adoption rate is not slowing down. 

Despite its widespread adoption and undeniable benefits, Kubernetes poses significant challenges in resource and cost management. The inability to monitor organizational usage or optimize a cluster’s resource utilization and allocation at scale leads to a staggering amount of cloud waste—about 47% of companies’ cloud budgets, as revealed by a StormForge survey. These challenges are not just hurdles, but pressing issues that demand immediate attention.

CloudBolt solutions are used by FinOps teams worldwide that strive to maximize the value of their cloud investments. Through our extensive interactions with these teams, we’ve witnessed the intricate complexities and critical challenges of managing costs within Kubernetes environments—challenges that conventional tools struggle to address effectively.

Recognizing the need for a transformative solution, we formed a technical partnership with StormForge earlier this year. By combining StormForge’s intelligent machine learning capabilities with CloudBolt’s Augmented FinOps offerings, this collaboration offers a powerful solution that enables users to manage Kubernetes resources with precision and autonomy. 

Understanding Traditional Kubernetes Resource Management

At its core, Kubernetes utilizes a set of mechanisms to control how a cluster allocates and consumes resources. A big part of this system is the concepts of CPU and memory ‘requests’ and ‘limits,’ which allow users to isolate container resources. Requests guarantee that a container gets a certain amount of resources, helping Kubernetes schedule pods effectively across nodes. Limits prevent a container from consuming more than its fair share of a resource, which can affect other containers running on the same node.

Despite its potential, Kubernetes often falls short in efficiency largely due to the complexity of managing resource requests and limits. Here, we explore three common approaches to Kubernetes resource management, each with its own set of drawbacks, highlighting the need for a more intelligent, automated solution.

  1. Non-specification of requests

While most developers know that CPU and memory requests exist in Kubernetes, many are unaware of how to set them carefully or why it is crucial. Instead, they usually focus on simply getting their applications running. This approach leads to an entire host of performance and reliability problems such as unstable environments where applications do not receive the resources they need, causing crashes or slowdowns during peak loads.

  1. One-size-fits-all approach

Organizations that experience the problems resulting from the first approach will then adopt a one-size-fits-all approach, which simplifies management in the short term but causes significant inefficiencies in the long term. Such an approach fails to consider the unique requirements of different applications or workloads. Developers, whose primary concern is the performance of their applications, tend to request more compute and memory than necessary which can quickly lead to significant overspending.

  1. Manual tuning of workloads

Finally, organizations that understand the need to set appropriate values for each cluster will tune their Kubernetes workloads manually. Doing so, however, requires a large amount of engineering time and resources to individually assess each application or service and meticulously adjust its resource needs. This process is particularly problematic at scale and must happen continuously, as resource needs change over time. It cannot be a set-it-and-forget-it solution, which makes it both time-consuming and prone to inefficiencies.

Machine Learning for Kubernetes Resource Management

StormForge and CloudBolt’s joint solution offers a new transformative approach to automating and optimizing Kubernetes resource management in real time. With its advanced machine learning, StormForge analyzes observability data and makes recommendations for container CPU and memory settings to optimize resource consumption and ensure cost efficiency and application performance.

How it works

StormForge’s solution integrates seamlessly into existing Kubernetes environments using a straightforward one-click installation process. Upon installation, a comprehensive analysis of observability data from your Kubernetes clusters begins. Here’s a step-by-step breakdown:

  1. Data Ingestion: StormForge ingests metrics from the Kubernetes cluster (kube-state-metrics and cadivsor). This includes CPU usage, memory demands, and other critical performance metrics.

  2. Analysis and Recommendations:
  1. Automatic Deployment:
apiVersion: apps/v1
kind: Deployment
Metadata:
  name: nginx-deployment
Spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:

name: nginx

        image: nginx:1.14.2
        resources:
          requests:
            cpu: 100m  # Adjusted from initial higher value
            memory: 200Mi  # Optimized based on usage analysis
          limits: 
            cpu: 200m
            memory: 400Mi
  1. Control and Customization:

Outcomes

The StormForge solution integrated into CloudBolt’s Augmented FinOps platform automates Kubernetes resource management, offering a scalable, machine learning-based approach that outperforms traditional methods in every aspect. It’s not just easy to use and continuously automatic, but also highly accurate. Customers can expect to save an average of 50% with StormForge, a significant amount considering the potential cost of Kubernetes for medium to large enterprises. As Mark Piersak, U.S. Bank vice president of container platform solutions, attests, the value of StormForge is undeniable: “The overall net savings on capacity has given us tremendous cost savings.”

Conclusion

As Kubernetes’ use continues to rise, clear cost visibility and accurate allocation control are more important than ever. StormForge and CloudBolt’s partnership ensures that Kubernetes clusters, along with the rest of the cloud, are not only clearly factored into cloud spending but optimized for maximum cloud ROI. Sign up for a demo to see the solution in action!

StormForge and CloudBolt will be at the FinOps X Conference in San Diego on June 19 to 22. Visit us at booth G8 to learn more about our solution!