September 20, 2024
Top 10 Cloud Cost Observability Metrics to Watch
4 min read
Introduction
Balancing operational performance with cost efficiency is critical in today’s cloud-native world. As organizations shift to the cloud, cost observability becomes a critical aspect of managing and optimizing expenses and infrastructure performance. Whether you’re running applications on Kubernetes or leveraging multi-cloud environments, gaining insight into spending patterns is essential for maximizing your cloud return on investment (ROI).
Cloud cost observability platforms like Amnic provide continuous monitoring of costs, recommend areas for improvement, and ensure resource usage is efficient without compromising performance. By keeping an eye on a few key metrics, you can make informed decisions around rightsizing your cloud infrastructure, optimizing Kubernetes clusters, and ensuring greater availability while maintaining more control over budgets.
In this post, we’ll explore 10 important cloud cost observability metrics you should monitor to keep cloud infrastructure running efficiently and at a reasonable cost. This post will emphasize metrics that relate to both cost management and service performance in cloud-native environments, helping you get the most out of your cloud investments.
Compute Resource Utilization
Why It Matters
Compute resources, specifically CPU and memory utilization, are among the most critical metrics to watch when it comes to both cost and performance. In Kubernetes clusters or any cloud-native environment, these resources are allocated to run workloads and their efficiency is directly tied to cloud spending. Underutilized compute resources mean you're paying for capacity you’re not using while overprovisioned resources can lead to performance degradation.
How to Monitor
Monitoring CPU and memory utilization is straightforward with the right observability tools. Tools like Kubernetes dashboards, Prometheus, and cloud-native cost observability platforms such as Amnic can provide granular insights into how resources are being consumed across your clusters and instances. These tools help identify overprovisioned instances that can be downsized or under-provisioned resources that could be expanded, saving costs without sacrificing performance.
Best Practices
Rightsizing instances: Avoid overprovisioning or under-provisioning by allocating just the right amount of CPU and memory based on real-time data.
Horizontal Pod Autoscaling (HPA): Use HPA in Kubernetes to automatically scale the number of pods based on CPU or memory metrics, reducing waste.
Reserved instances and savings plans: For predictable workloads, leverage reserved instances or long-term cloud savings plans to lower costs.
By maintaining an optimal balance between resource usage and performance you can significantly reduce unnecessary cloud costs and ensure your workloads are rightsized and running efficiently.
Data Transfer and Storage Costs
Why It Matters
Storage is another key component that drives cloud costs, especially in data-heavy environments like Kubernetes clusters. Unused or redundant storage can quickly add up, leading to extra costs. Additionally, data transfer charges – fees associated with transferring data out of cloud storage – can significantly impact cloud spending if mismanaged.
How to Monitor
Monitoring storage utilization and data transfer is essential for understanding and controlling cloud spending. Cost observability platforms offer out-of-the-box dashboards to track storage costs, showing you exactly how much you’re spending on services like Amazon S3, Google Cloud Storage, or Azure Blob Storage. Observing data egress patterns is equally important since frequent, excessive data transfers can lead to unpredictable growth in expenses.
Best Practices
Storage tiering: Use different storage classes or tiers based on access frequency. Move data to cold storage to reduce costs when it’s not used often.
Data compression: Compressing files before storage can help minimize storage costs and improve overall efficiency.
Monitor egress costs: Use cost observability tools to track and optimize data transfer costs across regions and cloud providers.
By actively monitoring storage and data transfer costs, organizations can prevent unexpected spikes in cloud bills while simultaneously ensuring data is stored and transferred in the most cost-efficient manner.
Network Traffic and Bandwidth Usage
Why It Matters
Network traffic and bandwidth are often overlooked but can contribute significantly to cloud costs, particularly in multi-cloud environments or when using large-scale Kubernetes clusters. High levels of data transfer between regions or excessive bandwidth consumption can quickly cause your cloud bill to surge, making it crucial to monitor these metrics.
How to Monitor
Observing network traffic can provide insights into data flow patterns across your infrastructure. Most cloud providers offer detailed network usage dashboards, allowing you to track how much data is being transferred between various services and regions. Cost observability tools like Amnic can also provide real-time alerts and detailed reports around spikes in network traffic or inefficiencies that are driving unnecessary costs.
Best Practices
Optimize inter-region traffic: Monitor and minimize unnecessary data transfer between cloud regions, as cross-region traffic often incurs additional fees.
Implement bandwidth throttling: Throttle bandwidth for any non-essential services to control usage and avoid unnecessary charges.
Leverage Content Delivery Networks (CDNs): Use CDNs to reduce data transfer needs and latency for users accessing your services from different regions.
By keeping a close watch on network traffic and bandwidth usage, you can optimize your cloud infrastructure for both performance and cost-efficiency, preventing unexpected increases in your cloud bill.
Kubernetes Node and Pod Costs
Why It Matters
In Kubernetes clusters, the cost of running nodes and managing pods plays a significant role in overall cloud spending. Kubernetes allows for elastic scaling, meaning that nodes and pods can be dynamically created or deleted according to demand. Proper cost observability leads to well-provisioned Kubernetes clusters, helping you avoid overprovisioned nodes or misallocated pod resources that could drive cloud costs higher without corresponding performance gains.
How to Monitor
Cloud cost observability platforms offer Kubernetes-specific visibility, allowing you to track node and pod usage, resource allocation, and cost distribution across different namespaces or workloads. This level of granularity helps you identify which nodes or pods are driving costs and where optimizations can be made.
Best Practices
Rightsize Kubernetes nodes: Ensure nodes are provisioned with just enough resources to handle workloads. Avoid overprovisioning by analyzing historical data and setting dynamic limits.
Monitor pod resource requests and limits: Misconfigured resource requests and limits can lead to the inefficient use of Kubernetes nodes, increasing costs without improving performance.
Leverage spot instances: For non-critical workloads, consider using spot instances to lower Kubernetes infrastructure costs by taking advantage of lower-cost, preemptible resources.
Monitoring and optimizing Kubernetes node and pod usage ensures your cloud infrastructure is not only cost-effective but also scalable and performant.
Idle and Abandoned Resources
Why It Matters
Idle and abandoned resources are one of the biggest culprits when it comes to inflated cloud bills. These are cloud resources that are no longer in use or are underutilized, continuously incurring costs. Examples include instances left running after testing, unattached storage volumes, or unused IP addresses. If unmonitored, these idle resources can pile up and lead to significant financial ramifications.
How to Monitor
Most cloud providers offer native tools to help detect and manage idle or unused resources. Cost observability platforms like Amnic provide deeper insights by automatically identifying and flagging unused resources. For Kubernetes clusters, neglected persistent volumes or dangling services can be a common source of hidden costs which can easily be detected with the continuous tracking of cloud resource utilization and spending.
Best Practices
Automate resource cleanup: Use automation, tools, and policies to regularly identify and terminate idle or neglected resources.
Use tagging for tracking: Implement resource tagging strategies across your infrastructure to keep track of resource ownership and prevent unnecessary resources from going unnoticed.
Run periodic audits: Schedule regular audits to identify unused instances, volumes, or other resources that can be decommissioned to save costs.
By continuously monitoring and managing unused resources, you can significantly reduce waste, ensuring your cloud infrastructure is being used as efficiently as possible.
Autoscaling and Spot Instances
Why It Matters
One of the most effective ways to control cloud costs is through intelligent autoscaling and the use of spot instances. Autoscaling enables cloud environments to dynamically adjust resource consumption based on demand, ensuring you're not overprovisioned or underprovisioned. Spot instances, on the other hand, offer the ability to leverage unused capacity at a fraction of the cost, though they come with the trade-off of being preemptible.
How to Monitor
With the right cost observability tool, you can monitor the effectiveness of autoscaling policies and the utilization of spot instances. Kubernetes offers built-in autoscaling features like the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) that can be fine-tuned to dynamically scale workloads based on CPU and memory usage. On top of this, cost observability platforms can help ensure autoscaling mechanisms are optimized and alert you of any inefficiencies.
Best Practices
Fine-tune autoscaling policies: Regularly adjust autoscaling parameters based on historical usage patterns to continuously optimize resource allocation.
Leverage spot instances for non-critical workloads: Use spot instances for jobs that can handle interruption such as batch processing or stateless workloads to drastically reduce costs.
Use predictive scaling: Some cloud providers offer predictive scaling features that anticipate demand and scale resources accordingly, providing an additional layer of optimization.
By combining autoscaling with the strategic use of spot instances, you can proactively manage highly efficient cloud infrastructure that balances performance with cost savings.
Cloud Service-Specific Costs (AWS, Azure, GCP)
Why It Matters
Each cloud provider – AWS, Azure, and GCP – offers a vast array of services, each with its own pricing model. Without a granular understanding of how these services contribute to your overall cloud bill, costs can spiral out of control. For companies operating in multi-cloud environments, understanding service-specific costs and monitoring them becomes even more critical for cloud cost optimization.
How to Monitor
Dedicated cloud cost observability platforms provide a unified view of costs across multiple cloud providers. They allow you to drill down into service-specific charges and helps you track how much you're spending on particular services like AWS EC2, Azure VM, or GCP BigQuery. This kind of granularity makes it easy to optimize resource usage and ensures you're using the most cost-effective solutions possible.
Best Practices
Optimize service usage: Regularly evaluate whether you're using the most cost-efficient services available for each cloud provider. For example, you might migrate from on-demand instances to spot instances or savings plans based on workload predictability.
Leverage reserved instances: Take advantage of reserved instances or commitment-based plans to reduce costs for predictable workloads across different cloud providers.
Use cost allocation: Tag and organize resources by team, project, or department to track and allocate spending more effectively across services and providers.
By knowing your cloud’s service-specific costs you’ll gain more control of overall cloud spend, especially in multi-cloud environments.
Data Observability for Cost Management
Why It Matters
Data observability is essential for nearly every organization today. Without insights into data pipelines, storage, and real-time analytics, it's easy to overlook the costs associated with data-heavy operations. Full-stack cost observability helps teams not only optimize performance but also see how data workloads are impacting cloud expenses.
How to Monitor
Cost observability platforms like Amnic allow teams to monitor usage across storage, processing, and data transfer services, giving a clear picture of how different data workloads contribute to costs. For Kubernetes clusters, tools like Prometheus and Grafana can help you track data-related metrics in real-time, ensuring your applications run efficiently without unnecessary costs.
Best Practices
Optimize data storage and processing: Continuously evaluate your data storage and processing solutions to avoid overpaying for unused or redundant resources. Use cloud-native storage optimization techniques such as compression or deduplication.
Monitor data egress: Pay close attention to data transfer charges, especially in multi-cloud environments where moving data between regions or providers can rack up quickly.
Leverage cloud cost observability: Implement cost observability tools and techniques to track how each stage of your data pipeline is contributing to cloud costs, from ingestion to processing and storage.
By implementing robust observability practices, you’ll create transparency around how data-centric workloads impact cloud budgets so you can make the necessary cost and performance optimization adjustments.
Anomaly Detection in Cloud Billing
Why It Matters
Anomaly detection with cloud costs helps you catch unexpected spikes or deviations in typical cloud spending. Without proactive anomaly detection, unexpected usage – whether caused by a configuration error or a security breach – can result in sudden and significant cloud expenses. Identifying anomalies in real-time allows you to quickly respond before your cloud costs spiral out of control.
How to Monitor
Anomaly detection tools is a core component of any cost observability platform. It will continuously monitor cloud usage patterns and costs, alerting you to deviations that exceed normal thresholds. By setting up cost alerts based on historical spending data, these tools can flag any sudden spikes in resource consumption, allowing for quick investigation and resolution.
Best Practices
Set automated alerts: Use automated cost alerts alongside anomaly detection to flag unusual spikes in usage or billing, especially for high-usage resources like compute and storage.
Perform root cause analysis: Once an anomaly is detected, conduct root cause analyses to determine what happened and take action to avoid any future incidents.
Implement spending caps: To prevent runaway costs, especially in cloud-native environments like Kubernetes, implement spending caps or use budget controls provided by your cloud provider.
Anomaly detection must be integrated into your cloud cost observability strategy to protect your organization from unexpected cost surges and ensure cloud spending remains under control.
Budgeting and Forecasting
Why It Matters
Effective budgeting and forecasting help you maintain control over cloud spending for the long term. Without accurate financial forecasts, you risk exceeding cloud budgets or under-allocating resources which can lead to either excessive spending or performance issues. Cloud cost observability tools can help by providing accurate predictions based on historical usage patterns, allowing teams to proactively manage costs.
How to Monitor
Cloud cost observability platforms help you easily manage budgets and create forecasts based on current and past usage trends. These tools can help teams set realistic budgets for cloud resources, monitor actual usage against set budgets, and adjust spending goals in real-time to avoid overages.
Best Practices
Set clear budgets: Define clear cloud budgets at the project, team, or department level, and use real-time cost tracking to ensure spending stays within those limits.
Use predictive analytics: Leverage forecasts that analyze historical cloud usage patterns to predict future costs and mitigate surprise bills.
Monitor in real-time: Continuously monitor actual cloud spend against forecasts, making real-time adjustments to resource allocation when necessary.
By adopting strong budgeting and forecasting practices, you’ll ensure cloud infrastructure is not only cost-efficient but also aligned with long-term financial goals for the company.
Conclusion
Cloud cost observability is not just about tracking your expenses – it's about optimizing your entire cloud infrastructure for both performance and cost. By continuously monitoring these top 10 cloud cost observability metrics, you can make informed decisions that keep your cloud environment lean and effective.
Cloud cost observability platforms like Amnic help you pull granular insights that are needed to rightsize resources, manage a cloud’s service-specific costs, and detect anomalies before they become costly. With proper observability in place, you can confidently scale and optimize cloud infrastructure and maintain predictable cloud budgets, all while ensuring high performance.
Unlock full cloud cost observability and start optimizing Kubernetes clusters and cloud environments with Amnic. Sign up for a 30-day free trial today or request a personalized demo to see how easy it is to manage costs and rightsize your infrastructure.
