FinOps Essentials: Understanding Cloud Usage Optimization

Enterprises are constantly expanding their cloud footprints. Over half of enterprises now spend an average of $1.2 million annually on AWS alone, and that’s just the beginning. A FinOps strategy aims to comprehensively answer the question of exactly where all that money is going and that, in turn, starts with understanding your cloud usage. Once you have visibility into your cloud usage, you can optimize it for significant cost reductions and other benefits.

It's already well known that cloud data sprawl leads to increased operational and cybersecurity risk, but the financial risk that comes with rapidly growing cloud footprints often doesn’t get the attention it deserves. Furthermore, as your cloud estate grows, it becomes more challenging to understand where your budget is going and what’s driving up the costs.

As enterprises shift towards multi-cloud environments and expand their service offerings, it gets even harder to keep usage and costs under control. As the bills soar, the costs can start undermining the benefits of migrating to the cloud in the first place.

It’s time for every tech leader to know about modern cloud cost optimization. It’s also essential that they look beyond the relatively limited scope of the tools provided by major cloud vendors themselves. Without a robust cloud usage optimization strategy, it’s just a matter of time before costs spiral out of control and end up being detrimental to innovation and growth.

What is Cloud Usage Optimization?

Cloud usage optimization is the process of eliminating resource waste by correctly selecting, right-sizing, and provisioning the resources required for a given workload. There are several major sources of resource waste in cloud computing:

• Overprovisioning is perhaps the most common source of cloud waste, and one of the most difficult to address. Enterprises often overprovision to ensure their services have sufficient resources on peak load and to increase resilience in case part of the system goes down. To optimize your cloud usage, you should provision with minimal resources and leverage right-sizing to automatically scale resources with workload demand – and scale down again when the workload disappears.
• Purchasing resources for longer time periods than they’re actually needed is another common source of cloud waste. For example, a service might online need to be online and accessible for 12 hours per day on weekdays, but if it’s kept on around the clock every day of the week, you’ll be wasting 65% of your spend. Reserved instances in platforms like AWS can reduce unit costs, but should only be used for very predictable workloads.
• Duplicate purchases for similar types of cloud services are a common problem in siloed organizations, in which a lack of sufficient collaboration between teams leads to rogue IT. For example, one employee might decide to open an instance to host an internal app that serves exactly the same role as that of another app used by someone in a similar role. Duplicate resources might also appear when an employee leaves the company, and the original resource is not migrated or retired.
• Individual transactions and service features can also be a root cause of cloud resource waste if they are not designed correctly. For example, if a cloud transaction process is unnecessarily convoluted, spanning several different systems and architectures more than it strictly needs to, then it’s probably going to have a negative impact on cloud usage and, therefore, costs. The same applies to individual app and service features. All features require resources to function, but if those features are poorly implemented or rarely used, they’ll become a drain on your cloud resources and budget. 

On average, enterprises waste about 30% of their cloud spend. If we’re going by the average annual AWS spend in large enterprises, that translates into $360,000 per year being whittled away on nothing.

Cloud usage optimization aims to target and eliminate these sources of waste by aligning the resources used with the actual workload demand, along with other requirements. For example, reserved instances are suitable for steady and predictable usage, even if they are they aren’t needed around the clock. Furthermore, reserved instances can actually save money in certain use cases, because cloud vendors provide discounts in exchange for your commitment to pay for all the hours over a given term. A cloud optimization strategy should also inform you as to which types of instances and other resources are best suited to each workload type.

Integrated tools provided by cloud vendors play an important role in usage optimization. For example, AWS users can get cost and usage information from the AWS Cost Explorer and AWS Cost & Usage Report. These tools provide some granular visibility into the hourly, daily, and monthly levels of your usage of EC2 and other AWS services. 

What are the Limitations of Mainstream Solutions?

You can’t optimize what you don’t know about, which is why the first step towards optimizing your cloud usage and costs is to gain complete visibility over your environment. For this, you need the right set of tools to provide the real-time insights needed to make informed decisions concerning how you allocate your cloud resources.

Most current cloud usage visibility solutions on the market, including those offered by the cloud vendors themselves, are fairly limited. Unsurprisingly, the tools provided by cloud vendors only support their own platforms, in which case enterprises with complex multi-cloud environments will lack the single pane of glass they need to monitor their cloud usage across the board. For example, you can’t use AWS’s tools to monitor your GCP costs, and neither will they give you a granular view of your usage associated with platforms like Kubernetes or Snowflake. This translates into a greater burden on teams seeking to gain visibility and control over their cloud usage and spend. For a complete view of spending and usage, you’re going to need a more holistic solution.

How FinOps Drives Cloud Usage Optimization

Cloud usage optimization aims to streamline cloud services and, in doing so, reduce the costs without hindering service delivery. While some of the responsibility inevitably falls to the cloud providers themselves to deliver more cost-effective infrastructure, there’s only so much they can do, or are willing to do, on their end. As such, there inevitably ends up being a gap between expectation and reality.

FinOps aims to close that gap by fostering a culture of accountability in which everyone is responsible for their cloud usage. Although FinOps isn’t a technology, third-party optimization tools and services can help drive a FinOps culture by providing relevant insights into how and where your cloud budget is being spent. For example, sales teams can leverage cloud usage insights when formulating quotations for potential clients, while development teams will know which service features are bringing a satisfactory return on investment and which ones aren’t.

Cloud Usage Optimization is one of the six domains defined by the FinOps Foundation. Within this domain, the enterprise identifies and takes steps to match their cloud resources with the workloads at any given time. This involves the predictive rightsizing of resources, optimizing workloads to ensure they align with the correct scaling of those resources, and deactivating resources when they’re not in use, among other techniques.

FinOps defines four capabilities in the Cloud Usage Optimization domain:

• Data analysis and showback: This refers to the ability to use data and metadata about cloud resources and their hierarchies to create a near real-time reporting mechanism. This should cover the total costs of a desired business entity, opportunities to avoid or reduce costs, and financial KPIs. To achieve this ability, you ideally need a centralized repository of normalized, tagged, and queryable data – in other words, a single pane of glass for analysis, reporting, and visualization of cloud costs and usage. 
• Onboarding workloads: This involves establishing standardized onboarding processes for existing applications and services, as well as those that are in development. This process must incorporate financial and technological viability. In other words, it serves to help teams determine how to best prepare a given workload for optimal cloud usage, or whether or not to even implement it at all. 
• Resource utilization and efficiency: Compute and storage resources must be analyzed to determine whether they provide sufficient business value in return for any costs they incur. For example, some resources need to be available at all times, while others can be wound down during off-peak hours. A more advanced approach involves applying tailored storage classes or tweaking instances to accommodate specific requirements, such as low latency or maximum processing power.
• Workload management and automation: This capability focuses on running resources only when they’re needed and creating mechanisms to automatically adjust and right-size resources at any given time. This process relies heavily on the correct tagging of instances and other resources to make it possible for the automation engine to identify, group, and manage them. 

FinOps has now become the ultimate strategic approach to cloud usage and cost optimization. However, although FinOps is a strategic framework rather than a subset of technology, it relies heavily on data-driven insights. To gain the insights needed to make informed decisions about cloud usage, you need a solution that grants full visibility into your cloud estate, automates scaling and rightsizing, and continuously tracks and evaluates your cloud operations against business objectives and metrics. That way, you can enhance cost efficiency, boost resiliency, and accelerate fluidity in your service delivery.

Finout gives enterprises the means to monitor, manage, and optimize their cloud spend in just minutes, regardless of the complexity of their environments. Get started today to find out how it works.