The cloud is today one of the most expensive resources for any modern organization, second only to employee salaries and overhead. According to recent research by Gartner, end-user spending on public cloud services will reach $396 billion in 2021 and grow 21.7% to reach $482 billion in 2022. By 2026, Gartner predicts public cloud spending will exceed 45% of all enterprise IT spending, up from less than 17% in 2021. Companies are moving to the cloud to maintain their competitive edge, accelerate innovation, and transform interactions with customers, employees, and partners. 

But keeping cloud costs under control is notoriously hard. Cloud assets are fragmented across multiple teams, cloud vendors, and containerized and non-containerized environments. Cloud spend is vulnerable to large fluctuations, making it difficult to forecast and keep under control. 

That’s why, according to Flexera’s 2020 State of the Cloud Report, optimizing the existing use of the cloud for cost savings is the top priority for 73% of cloud decision-makers. The report came on the back of news that the average company wastes 35% of its cloud computing budget. In 2017, Flexera showed that percentage translated to $10 billion in wasted public cloud spend — and that most companies also underestimate how much they wasted by 15%. In an even bolder statement, Gartner analysts Brandon Medford and Craig Lowery estimate that as much as 70% of cloud costs are wasted. These stats make it easy to see why cloud cost management and optimization are a growing priority for engineering, operations, and FinOps teams. 

 

What is cloud cost optimization?

Cloud optimization is the process of eliminating cloud resource waste by selecting, provisioning, and right-sizing the resources the company spends on specific cloud features. Optimizing the cloud is an ongoing endeavor that consists of determining the most efficient way to allocate cloud resources among different use cases, with the goal of increasing cloud performance while reducing waste. 

On paper, this sounds like a straightforward premise. But in reality, cloud cost optimization involves multiple challenges. These are the main ones:

 

Visibility and business context

Visibility into cloud spending is the key to surfacing cost optimization opportunities. Most teams lack true visibility into their cloud spend, and find it difficult to read and interpret billing data from multiple cloud providers and allocate costs accordingly. The sheer number of billing items (SKUs) and secondary charges such as storage, data transfer, and networking make it nearly impossible to relate costs to business value and measure unit economics such as cost per customer, product, or feature. The result is that decisions can’t be made regarding the efficient allocation of resources, and companies are in the dark regarding questions such as whether an increase in spend results from business growth or from sheer inefficiencies.

 

Complexity and scale

When cloud environments scale, so do the opportunities to waste resources — you pay for what you provision, regardless of utilization. Cloud billing is complex and dynamic, making it easy for waste to accumulate and costs to increase unnecessarily. For every organization, there are many opportunities to eliminate wasted cloud resources. But this first involves identifying inefficiencies, such as over-provisioned, idle, or unused resources, as well as cost spikes in real-time.

 

Budgeting and forecasting 

The migration from on-premises to the public cloud requires that organizations shift away from a known static CAPEX model to a highly dynamic usage-based OPEX model that is notoriously hard to control and forecast. The cloud makes it easy to spin up new resources — which has allowed organizations to innovate faster but also makes it easy to rack up huge bills. This complicates budgeting and forecasting, which is essential for a business resource that is often on the top three items of a company’s COGS.

 

Kubernetes cost allocation

Optimizing cloud costs is a challenge in and of itself, but the introduction of Kubernetes adds another layer of virtualization to manage. How can you manage costs if stakeholders lack an accurate view of which resources are being used and in what context? Reaching inside each container cluster to understand who is driving resource consumption and fairly allocating the resulting costs is a very complex problem. Organizations often find themselves struggling with complex, manual work in spreadsheets to split up and allocate Kubernetes costs by delivery teams, customers, products, or features. This results in poor financial management of containerized environments and makes it difficult to understand the detailed costs of operating containerized services that are required for accurate unit economics reporting.

 

Cloud cost management and optimization best practices

Cloud optimization is a continuous improvement process. It relies heavily on FinOps — a cross-functional team spanning IT, finance, and engineering with the goal of increasing an organization’s ability to understand and optimize cloud costs — and on cloud cost management tools that monitor cloud KPIs including utilization, cost and performance. Understanding what is happening in your cloud environment is key to a working cloud optimization strategy. Here are some of the best practices for managing your cloud environment:

 

1. Achieve a unified view with granular billing visualization

Seeing the big cloud picture — and having the ability to drill in — is a preliminary step for gaining control over cloud costs. Cloud teams need to rely on visualization and reporting tools that create complete, end-to-end visibility into the entire multi-cloud infrastructure and related billing costs from a single platform. By creating transparency into cloud KPIs, these tools enable teams to understand the cost of each resource, service, project or team; track spend and usage across projects; create customized dashboards and custom reports per customer/projects, and drill down to the resource level. Successful cloud financial management is dependent upon the ability to visualize your cloud cost and usage information.

 

2. Know and track your cloud unit costs

Unit costs are the average costs directly associated with a specific unit delivered by an organization, such as customer, product, feature, or delivery team. An increase in cloud costs might be a result of business growth – but not always. Understanding whether a cost increase is proportionately tied to revenue growth requires that you know and track your unit costs over time.

 

3. Monitor your cloud costs in real time 

Cloud cost monitoring is essential for both cloud cost management and optimization. Monitoring cloud spend is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time. Cloud activity that isn’t tracked in real-time opens the door to potentially preventable runaway costs. In addition, it is critical that cloud teams understand the business context of their cloud performance and utilization. An AI-based monitoring solution can automatically identify deviations from expected usage and cost patterns and alert the relevant teams, in real-time.

 

4. Continuously optimize your infrastructure 

Automate your cloud optimization strategy with AI-based automated insights and forecasting. Advanced cloud cost optimization solutions help you continuously optimize your cloud spend with real-time recommendations that are tailored to your environment and are quick to deploy. Additionally, they can take in every single cloud-based metric – even in multi-cloud environments – learn its normal behavior on its own, and create cost forecasts which allow for more effective budget planning and resource allocation.

 

5. Analyze your Kubernetes with unit economics

The shift to Kubernetes deployments requires organizations to extend their FinOps capabilities to containerized environments, so they can understand the exact container costs breakdown and how they roll up to products, features, and teams along with their other non-containerized services. Unit economics can be a powerful tool for understanding realized business value and tracking the efficiency of your Kubernetes investments.

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Anodot for Cloud Costs Management 

Anodot for Cloud Costs is built to offer cloud teams a contextual understanding of cloud costs and the impact of business decisions on cloud spend, helping companies achieve unit economics and understand how specific units and/or customers impact cloud metrics including cost, utilization and performance. From a single platform, Anodot provides complete, end-to-end visibility into your entire cloud infrastructure and related billing costs. By monitoring your cloud metrics together with your revenue and business metrics, Anodot enables cloud teams to understand the true cost of their SaaS customers and features. 

Anodot automatically learns each service usage pattern and alerts relevant teams to irregular cloud spend and usage anomalies, providing the full context of what is happening for the fastest time to resolution. The platform leverages proprietary ML-based algorithms to offer deep root-cause analysis and recommended remediation. With continuous monitoring and deep visibility, you gain the power to align FinOps, DevOps, and Finance teams and cut your cloud bill.  

Anodot provides granular insights about your Kubernetes deployment that no other cloud optimization platform offers. Easily track your spending and usage across your clusters with detailed reports and dashboards. Anodot for Cloud Costs’ powerful algorithms and multi-dimensional filters enable you to deep dive into your performance and identify under-utilization at the node level. 

Anodot’s AI-powered forecasting leverages deep learning to automatically optimize cloud cost forecasts, enabling businesses to anticipate changing conditions and usage and get a better read on related costs. 

Try Anodot for Cloud Costs with a 30-day free trial. Instantly get an overview of your cloud usage, costs, and expected annual savings.

Written by Anodot

Anodot is the leader in Autonomous Business Monitoring. Data-driven companies use Anodot's machine learning platform to detect business incidents in real time, helping slash time to detection by as much as 80 percent and reduce alert noise by as much as 95 percent. Thus far, Anodot has helped customers reclaim millions in time and revenue.

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