The Elastic Budget: Crafting Cost Efficiency in the Cloud

In the vast expanse of the cloud, the only constant is change. As we navigate through this nebulous space, traditional financial guardrails like fixed budgets can often seem like relics of a bygone era. The cloud promises flexibility, scalability, and efficiency—so why do we cling to static financial models that no longer serve us?

In this blog post, we'll embark on a journey to unravel these old standards. We're not just shifting workloads to the cloud; we're redefining the very blueprint of cost management in a digital landscape. We'll explore the inadequacies of fixed budgets in a cloud environment and introduce metrics that truly encapsulate the essence of cloud efficiency.

From debunking the myth of 'more is better' in performance to introducing the concept of 'Just In Time' computing, this post is an open invitation to IT professionals, financial planners, and cloud enthusiasts to rethink how we measure and value our cloud investments.

As we delve into the intricacies of cloud cost management, ask yourself: Are you merely floating in the cloud, or are you ready to soar?

Budget creep

The FinOps Foundation and Microsoft's Well-Architected Framework advocate for budgeting in your cloud environment as a means to reduce costs. At first glance, this advice seems sound—after all, not having a budget is like sailing without a compass. Setting that metric can shine a light on your performance and kickstart important conversations about your cloud service expenditure for specific applications.

But here's where I play devil's advocate. Let's dive into a theoretical scenario: imagine you're allocated €1,000 per month for your Azure infrastructure. Suddenly, you get a forecast that you're going to exceed this budget by 10%. A quick fix might be downsizing your virtual machines, and presto, you're back within budget. This works when you have a rigid budget, which could be suitable for some scenarios.

However, when you truly embrace cloud-native architecture, a fixed budget may not just be suboptimal—it could be antithetical to the very principles of cloud flexibility and scalability. A cloud-native approach often entails dynamic resource allocation that responds to real-time demands, which can be at odds with a static budget.

Especially with 'lift-and-shift' cloud implementations that rely heavily on virtual machines (VMs), there's a tendency to experience fixed cloud costs. This conventional approach often defies the true potential of the cloud, which is designed for flexibility and scalability. In a lift-and-shift model, legacy systems are moved to the cloud with little to no modifications, which means they don't take full advantage of cloud-native features such as auto-scaling.

Moreover, there's a behavioral risk associated with strict budget caps. Teams might see reaching the budget cap as the ultimate goal, rather than continuously striving to improve their cloud cost efficiency. Without the incentive to optimize, the cost stays static, and the motivation to innovate or improve can dwindle. This can lead to a mindset where hitting the budget cap is seen as sufficient, despite there being opportunities to further reduce costs and increase efficiency.

To mitigate this, it’s crucial for organizations to foster a culture of continuous improvement, where teams are encouraged—and rewarded—for optimizing cloud costs beyond just meeting budgetary targets. For instance, incorporating cost optimization KPIs into performance reviews could incentivize staff to seek out efficiencies even when under budget.

Cloud waste

Beyond budgeting, we need additional metrics to measure our cloud usage's efficiency. Take the manufacturing industry, for example, where efficiency is paramount. Here, waste reduction is essential; a product with high waste levels might meet value or cost expectations, but the true cost is significantly inflated due to the inefficiencies.

So, how do we quantify waste in our cloud environment? Drawing from my experience as a performance engineer, I suggest we focus on the end users. Consider a web application hosted in the cloud: you may be satisfied with the cost, but is it running efficiently? To evaluate this, we can rely on two key metrics: the maximum number of users your application can support and the actual number of users utilizing the system.

Here’s a simple formula to calculate waste:

In this formula, 'U' represents the current number of users or the number over a given time, while 'M' is the maximum number of users that the application can support at peak performance or the maximum would be able to handle over the timeframe.

For instance, if your application can support 100 users at peak performance ('M') but is only serving 50 users ('U') on average, the waste calculation would be:

Waste = 100 - (50/100 × 100) = 50%

This indicates that half of your cloud resources allocated for this application are not being utilized efficiently. By tracking this metric over time, you can pinpoint inefficiencies and adjust your cloud resources accordingly to reduce waste and optimize cost.

This metric allows us to recognize that many applications are full with waste, particularly during off-peak times when usage drops to zero. In the 'Four Pillars Framework' that Michiel Hamers and I developed, we address this issue with the concept of 'Zero User Costs.' This metric is vital for reducing waste and encourages organizations to adopt more cloud-native features that scale according to user demand.

For a more in-depth exploration of 'Zero User Costs,' I invite you to read my previous blog post, which can be found at Zero User Costs Blog .

To minimize waste, one approach I advocate is what I like to call 'JIT Computing,' inspired by the 'Just In Time' manufacturing methodology pioneered by Toyota. This method emphasizes the timely provision of resources—delivering them exactly when needed and not before, thereby avoiding inefficiency.

In cloud computing, JIT translates to scaling resources dynamically, provisioning them in response to real-time demand. For example, using cloud services that automatically scale down during periods of low or zero usage can significantly reduce costs, aligning with the JIT principle of not carrying excess inventory—in this case, unused compute resources.

By applying JIT principles to cloud computing, organizations can ensure they're not paying for idle resources, thereby maximizing efficiency and aligning costs with actual usage.

Flexibility Over Rigidity

By integrating both usage and costs, we can devise a more appropriate and cloud-friendly budget cap. Such a budget would be inherently flexible, designed to adapt to the usage patterns, which is a core principle of Azure's cloud service offerings. Instead of adhering to a rigid monthly, weekly, or daily budget, we would establish one based on actual usage.

This concept particularly benefits commercial organizations. For SAAS providers, whose business models revolve around usage and users, setting a budget cap per user based on application and environment could be transformative.

Let’s illustrate this with an example: Imagine your application incurs costs of €100 per hour. If there are 100 users in that given period, that equates to €1 per user. This method allows you to discern precisely how much each user costs you, providing a direct correlation between service delivery and expenditure.

Moreover, while auto-scaling is a popular method for cost reduction in the cloud, a general rule of thumb is that utilizing smaller instances is more cost-effective. Here’s why: Suppose you have a single app service instance that supports 1000 users per hour. When the 1001st user logs on, you need to scale up and add a new instance. At this instant, your efficiency is halved, and the cost per user effectively doubles. By using smaller instances that can be scaled more granularly, you maintain higher efficiency and better control over the cost per user.

While this approach can maximize efficiency, it also requires careful planning and monitoring to ensure that performance remains consistent. Furthermore, one must consider the complexity of accurately predicting user load and the potential for rapid changes in demand.

In this already complex cloud environment, let's introduce another variable to consider: necessity. While the aim is to provide the best for your end users, it's worth questioning whether the 'best' is always necessary. Research supports that faster user experiences can lead to more sales and user satisfaction. This is theoretically sound, but there is a tipping point. Beyond a certain speed, the return on investment diminishes, and maintaining ultra-fast performance becomes less about user satisfaction and more about diminishing financial returns.

At such a juncture, it might be wise to resist scaling up. Allowing for a slight increase in response time could be a strategic choice, keeping waste minimal and cost per user at an all-time low. However, navigating this decision requires a nuanced approach: it’s a delicate balance that demands extensive insight, continuous testing, and an innovative application of technologies.

For instance, leveraging analytics tools to monitor user engagement and satisfaction metrics can help identify that tipping point. Performance testing can simulate increased loads to determine the impact on user experience. And adopting emerging technologies like edge computing can enhance performance cost-effectively.

In the end, the goal is to strike a balance that maintains user satisfaction without incurring unnecessary costs—finding that sweet spot where performance meets efficiency is the key.

Keep improving and strive for the optimal solutions

This blog post is an invitation to dismantle the old paradigms we've brought into the cloud. There's a myriad of factors to consider, and while it might seem daunting, the aim is to inspire a shift in perspective—away from fixed budgets and towards a more nuanced approach that embraces the fluidity and flexibility of cloud computing.

Are you ready to move beyond a fixed budget and explore metrics that better suit the cloud's capabilities? I'm eager to hear your views on this topic. Whether you've found these insights useful, or you have experiences and strategies of your own to share, your input can enrich the conversation for all of us navigating these changes.

What are your thoughts? Do you agree with the approach outlined here, or do you have a different perspective? Let’s continue the discussion in the comments below.