Top 5 Strategies for Managing Cloud Costs

Cloud computing has changed the way businesses operate, offering unparalleled flexibility, scalability, and cost efficiency. However, with these benefits come the challenges of managing and optimizing costs in highly-dynamic environments like AWS, Azure, Google Cloud (GCP), and Kubernetes. 

This post will talk about five actionable strategies and tools that DevOps engineers and FinOps practitioners can focus on today to improve the efficiency of their cloud infrastructure without compromising the end-user experience.

Understanding Cloud Cost Observability

Cloud cost observability is the practice of monitoring, analyzing, debugging, and controlling cloud costs over time to ensure optimal resource utilization – both technical and personnel-wise. Cost observability is crucial for finding opportunities to optimize your cloud, creating more accurate financial forecasts and budgets, and driving more informed business decisions based on unit economics. 

Compute costs are a significant part of cloud spending. For most DevOps and FinOps teams, compute costs are a key area to start monitoring and actioning in order to drive significant cloud savings. Tools like Amnic or native cloud provider tools like AWS Cost Explorer, Azure Cost Management, and GCP Cost Management provide insights into spending patterns and help organizations pinpoint areas for improvement.

Cloud cost observability is essential for visualizing and understanding exactly how cloud investments are returning value. Cost observability tools at the most basic level provide dashboards and reports that highlight cost drivers and usage patterns. On top of these tools, organizations can set budgets, track spending against cloud resource performance and usage, recommend optimization actions, and even receive alert notifications when anomalous costs pop up. Cloud cost observability drives a more proactive approach, helping you identify and address cost surges before they become larger issues.

The Top 5 Strategies for Managing Cloud Computing Costs

1. Rightsizing Compute Resources

Rightsizing involves adjusting the size of compute resources to match the actual needs of applications and workloads. This practice prevents over-provisioning and under-provisioning, leading to substantial cost savings. For example, a cloud-native organization can leverage tools like Recommendations to analyze usage patterns and recommend optimal instance types and sizes.

• AWS Rightsizing Tools and Techniques

AWS provides services such as AWS Compute Optimizer and AWS Trusted Advisor that can provide recommendations for optimal AWS instance types and sizes based on historical usage patterns. These tools analyze historical data to suggest resizing (larger or smaller) or to change instance types if a different instance would be a better match for a given workload.

• Azure Rightsizing Tools and Techniques

Azure provides something similar with Azure Advisor, which can deliver tips and tricks on Azure best practices. These tips can include rightsizing recommendations or users can leverage Azure Cost Management to gain insight into resource utilization alongside recommendations for cost savings.

• GCP Rightsizing Tools and Techniques

As you might guess, GCP's Recommender service offers insights into resource usage and suggests rightsizing options to help reduce costs. GCP also offers a Cost Management tool that can provide simple reports on resource capacity and usage rates, helping users identify commonly over-provisioned resources.

• Kubernetes Rightsizing Tools and Techniques

Kubernetes users can utilize tools like Kubernetes Resource Recommender or Kube-State-Metrics to analyze resource usage and adjust pod sizes to the right fit. Kubernetes can also dynamically adjust resources through Vertical Pod Autoscaler (VPA) or via the Karpenter auto-scaler, helping ensure applications have just the right amount of CPU and memory resources. Amnic combines recommendations for all cloud providers into one platform so developers can find actionable optimization opportunities at all levels for all clouds.

2. Utilizing Reserved Instances and Savings Plans

Reserved instances and savings plans offer significant cost savings by committing to a certain level of usage for an agreed-upon period of time. These options sacrifice flexibility in order to give you discounted rates compared to on-demand pricing. For instance, AWS's Reserved Instances can help you save up to 75% over on-demand instances.

• AWS Reserved Instances and Savings Plans

AWS Reserved Instances and Savings Plans allow users to commit to a certain level of usage. If you continuously fall short of that usage mark or have a product that requires lots of dynamic scaling, this may not be right for you. But, these options offer substantial discounts when compared to on-demand pricing. These options are somewhat flexible, allowing you to modify reservations to match changing workloads if necessary

• Azure Reserved Instances

Azure provides Reserved VM Instances that offer cost savings for customers who commit to one-year or three-year terms. Azure's Hybrid Benefit program also allows customers to use existing Windows Server and SQL Server licenses on Azure, driving additional savings when these services are applicable to your business.

• GCP Committed Use Contracts

Similarly to Azure, GCP's Committed Use Contracts provide discounts for resources you’ve committed to using for one or three years, of course leading to significant savings over on-demand prices. These contracts cover a wide range of GCP services which can be a nice benefit over some of the alternatives, allowing users to save not only on compute but on storage, network costs, and more.

3. Implementing Auto-Scaling Strategies

Auto-scaling continuously adjusts the number of compute resources you’re using based on real-time demand, ensuring optimal resource usage and cost efficiency. For example, AWS Auto-Scaling can automatically increase or decrease instance counts based on predefined conditions, helping you automatically manage costs and reduce surges in unnecessary spending.

• AWS Auto-Scaling Strategies

AWS Auto-Scaling allows users to set scaling policies that automatically adjust resource capacity based on demand. AWS Auto-Scaling can be configured to use dynamic or even predictive scaling policies to ensure that resources are scaled appropriately and that costs are limited to a number you’re comfortable with.

• Azure Auto-Scaling Strategies

Azure provides Auto-Scale settings that enable resources to scale automatically based on metrics like CPU usage and memory, optimizing costs. Azure's built-in autoscaling for virtual machines and App Services ensures that applications can handle variable loads efficiently. At off-peak hours, you’ll spend less but you still don’t need to worry about an increase in demand or any kind of variable event that could cause an anomaly in your typical cloud spend.

• GCP Auto-Scaling Strategies

GCP's Autoscaler adjusts the number of VM instances based on load metrics, continuously ensuring efficient resource usage and cost management. GCP also offers autoscaling for managed instance groups, allowing applications to scale seamlessly as traffic ebbs and flows.

• Kubernetes Auto-Scaling Strategies

Kubernetes offers Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler. You could also leverage a tool such as Karpenter to help auto-scale and provision Kubernetes resources appropriately to reduce costs. These auto-scalers can adjust the number of pods and nodes based on real-time resource usage in order to maintain cost efficiency. HPA scales pods based on observed CPU utilization or other select metrics, while Cluster Autoscaler adjusts the size of the Kubernetes cluster based on the needs of the pods. Amnic also tracks capacity, requested CPU or memory, and actual usage at the node and workload level in one single place for Kubernetes across all cloud providers. 

4. Leveraging Spot Instances

Spot instances and preemptible VMs help you take advantage of unused cloud capacity at significantly reduced rates. These options are ideal for workloads that are flexible in terms of start and stop times. For example, AWS Spot Instances can save as much as 90% of your spending when compared to on-demand instances.

• AWS Spot Instances

AWS Spot Instances enable users to bid on unused EC2 capacity, providing significant cost savings for fault-tolerant and flexible applications. Spot Instances are suitable for workloads that can handle interruptions such as batch processing, big data queues, and CI/CD pipelines.

• Azure Spot VMs

Azure Spot VMs offer the ability to purchase unused capacity at a discounted rate which is ideal for interruptible workloads. Azure Spot VMs can be used for workloads like testing and development, batch processing, and large-scale simulations that can tolerate disruptions without affecting customers.

• GCP Preemptible VMs

GCP Preemptible VMs provide substantial savings for compute-intensive tasks that can tolerate interruptions, offering discounts of up to 80% compared to regular instances. These VMs are ideal for fault-tolerant applications like big data processing, machine learning model training, and media rendering. Amnic looks at all cloud providers and Kubernetes to understand when spot instances might make sense and will recommend using them when they can drive savings in your cloud environments.

5. Monitoring and Optimizing Based on Usage

Continuous monitoring and optimization based on typical usage patterns is crucial for managing cloud costs effectively. By analyzing usage data over time, you can identify inefficiencies and ensure resources are allocated appropriately. For instance, with Amnic, you can monitor costs alongside resource usage to help with identifying underutilized instances.

• AWS Monitoring Tools

AWS offers CloudWatch and AWS Cost Explorer to monitor resource usage and show where cost-saving measures can be taken. CloudWatch provides metrics and logs to help you understand resource usage and performance while AWS Cost Explorer offers cost reports.

• Azure Monitoring Tools

Azure provides Azure Monitor and Azure Cost Management to track resource usage and infrastructure performance metrics next to their related expenses. Azure Monitor offers comprehensive general infrastructure monitoring capabilities while Azure Cost Management helps you track and control cloud spending.

• GCP Monitoring Tools

As with AWS and Azure, GCP's Stackdriver and Cost Management tools are broken out to help monitor infrastructure and track associated costs respectively. Stackdriver provides logging, monitoring, and diagnostics for GCP resources while GCP Cost Management ties these metrics to cost reports and budgets.

• Kubernetes Monitoring Tools

Kubernetes users often leverage third-party tools like Prometheus and Grafana to monitor cluster performance and resource usage, aiding in cost optimization. Prometheus collects metrics from Kubernetes clusters and Grafana visualizes these metrics. Amnic also monitors Kubernetes workload and node capacity, requests, and usage to help users identify and address cloud inefficiencies.

Cloud Cost Observability to Reduce Compute Costs

Optimizing cloud computing costs requires a complete approach – including the above five actionable opportunities for reducing costs. By adopting these practices, organizations can significantly reduce their cloud expenses while maintaining high performance and reliability. A collaborative culture of FinOps dedicated to cloud cost observability also helps address the human component of reducing cloud costs – connecting the dots between some of the technical concerns listed above and the personnel-related processes. 

Ready to implement cloud cost observability today? Sign up for a 30-day free trial with Amnic or request a demo to get started. Let us help you achieve cost-efficient cloud computing today.