Cloud Cost Management Software: Top 6 Solutions for 2022

Tracking the cost and usage of cloud-based services can be a tough nut to crack even if you’re using cloud cost management software. This is especially true for teams running microservices, containers, and Kubernetes. Let’s take a look at the top 6 cloud cost management software solutions in the market.

Cloud native tech is gaining ground, but it often makes it difficult to figure out exactly where your money is going and why. 

Cloud providers usually present you with bills that often bury key information behind rows of text. That's why every company using the cloud needs an effective platform that allows identifying which products and features drive the cloud spend or which customers are causing expenses to grow. 

Equipped with this cost intelligence, you can make informed engineering and business decisions - from pricing a product to designing cost-optimized software.

By using best industry practices encoded into cloud cost management tools, you can: 

• forecast, plan, and budget your cloud spend, 
• empower your engineers to see the cost impact of their work,
• discover areas of your software that could be rearchitected for increased profitability,
• identify your least profitable technologies, customers, and projects within the cloud,
• decide whether to adjust your pricing structure, 
• decommission features to cut operational costs, 
• calculate the effectiveness of cloud cost optimization mechanisms.

Cloud Cost Management Software - 2022 Comparison

Check out how the 6 top tools on the market compare in terms of feature coverage.

Top 6 Cloud Cost Management Software Solutions

Cloud cost management software helps companies control their cloud expenses by monitoring a company's resource usage and computing demands. These tools are typically paired with infrastructure as a service (IaaS) software offering to minimize the costs of the pay-as-you-go model.

Cloud cost management tools also help companies reduce waste by alerting users of lowered demand or automatically scaling usage to optimal rates. Furthermore, some solutions provide reporting features to outline waste and redundancies.

CAST AI – Fully Automated Kubernetes Cost Optimization and Management

CAST AI is a comprehensive cloud automation platform for optimizing Kubernetes environments. Teams running clusters on EKS, Kops, AKS, and GKE use it to save 50% to 90% on their cloud bills. Its automation features include spot instances, autoscaling, instance type selection, and rightsizing.

Pros of CAST AI

• CAST AI is a cloud-native platform for automatically analyzing, monitoring, and optimizing cloud environments, saving money and time. 
• It offers a free reporting module that splits cloud costs into project, cluster, namespace, and deployment levels. A team can track expenses down to individual microservices and then produce a full estimate of cluster costs. 
• Cost allocation works on a per cluster and per node basis. The solution also makes it easier for teams to view and allocate costs in multi-cloud setups. 
• Powered by AI, CAST AI chooses optimal resources for your application's requirements while reducing costs through autoscaling and rightsizing features. 
• When a cluster needs extra nodes, the automation engine selects the best-performing instances at the lowest cost. Automating multi-shape cluster formation and rightsizing features helps avoid overprovisioning.
• CAST's multiple autoscaling features allow users to achieve cost reductions. It generates an optimal number of required pod instances and scales the replica count of your pods up or down. It removes all pods if there is no work to be done. 
• CAST AI can also empty nodes to help you avoid paying for resources you don't use. The solution employs automated bin packing to maximize savings. 

Cons of CAST AI

• CAST AI's monitoring and reporting feature currently focuses on compute costs. However, the team is planning to add more coverage in the near future.
• Works only with Kubernetes (EKS, Kops, AKS, GKE).

Conclusion: CAST AI is a perfect choice for teams looking to reduce their cloud costs significantly without adding any engineer workload. Another good use case for CAST AI is among teams that want to automate the most time-consuming cost optimization tasks such as instance selection, rightsizing, or autoscaling.

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Kubecost – Kubernetes Cost Monitoring and Management For Improved Visibility

Kubecost is a robust cost reporting solution that helps Kubernetes users get insights into cost allocation, monitoring, and alerts for their clusters. It started as an open-source tool that provided developers with more visibility into their costs.

Pros of Kubecost

• Kubecost offers flexible and customizable cost breakdown features, including the ability to divide costs by namespace, deployment, service, and more indicators across all three major cloud service providers. 
• Kubecost's comprehensive resource allocation allows users to generate more accurate showbacks and chargebacks to help users manage ongoing costs. 
• Kubecost shows costs across multiple clusters and multi-cloud environments in a single view or through a single API endpoint. 
• The solution enables linking real-time in-cluster costs (CPU, memory, storage, network) with out-of-cluster expenses from the cloud services – for instance, tagged RDS instances, BigQuery warehouses, or S3 buckets. Users receive context-aware cluster-level reports to find an optimal balance between cost and performance matching their service requirements.
• Kubecost is deployed inside your infrastructure, so there is no need to egress any data to a remote service. You retain, and control access to your cloud spend data at all times.

Cons of Kubecost

• Kubecost's in-depth insights and cost reporting capabilities make it a great tool for teams looking for ways to manage their multi-cloud infrastructure. As it doesn't include cloud optimization features, however, you'll need to implement relevant changes manually—an extra charge that doesn't automatically guarantee savings. 

Conclusion: Kubecost's rich cloud cost reporting and monitoring features will work well for teams looking for in-depth insights and improvement recommendations. 

Spot.io – Optimization Based on Spot Instances

NetApp's Spot.io is a cloud cost management solution that helps teams optimize cloud infrastructure costs with spot instance automation. Using machine learning and analytics algorithms, the solution automatically gets spot capacity for workloads, cutting costs and ensuring high availability.

Pros of Spot.io

• Spot.io is a cloud cost management tool that optimizes cloud costs thanks to spot instance automation. Spot.io divides the infrastructure costs of clusters and offers insights into each layer of infrastructure. 
• The tool allows you to break down expenses into namespaces and individual workloads within every namespace, and you can further filter them by layer (compute or storage). For each workload, users get both compute and storage costs. You can use Spot.io data to analyze your application costs, perform chargebacks without extensive resource tagging, estimate future cloud spend, and adjust resources on the fly with recommendations from Spot.io's cloud cost management platform. 
• The tool monitors workload utilization in real-time, offering recommendations for manually adjusting the resource requirements for both per container and workload. This opens the door to easier high-level visualization and quicker implementation without disrupting applications.
• Spot.io checks for unschedulable pods and scales infrastructure up to ensure that all your pods have a place to run. 
• Moreover, Spot.io prioritizes downscaling the least utilized nodes and removes them if it it's possible to move all pods running on them. 

Cons of Spot.io

• The tool reduces costs by focusing on running workloads on spot instances. This, however, may mean that you miss out on other cost-saving opportunities. Spot.io, for example, does not currently support rightsizing – a process for managing the size of virtual machines in the cloud.

Conclusion: Spot.io is a good choice for teams that want to use spot instances more efficiently. However, they may soon discover that other optimization methods deliver better results in some situations. That's why they're likely better off with a platform that can automatically analyze workload requirements and match them with optimal cloud resources.

Harness – Automated Cloud Cost Management for Transparency and Governance

Harness provides continuous integration and delivery for DevOps teams. The platform includes a cost management module that supports cost savings in Kubernetes and offers business intelligence tools for analyzing cloud costs.

Pros of Harness

• Harness is a cloud cost management platform with a continuous delivery and integration module, which features Business Intelligence tools to improve transparency, optimization, and governance over all your cloud expenses. 
• The product brings in-depth Kubernetes visibility by displaying the utilized, idle, and unallocated resources per workload and cluster. It shows cost information by projects, teams, business units, departments, and more. 
• Furthermore, Harness lets you to create periodic reports on the most important cost and usage metrics. Governing cloud costs becomes easier with Harness thanks to the custom budgeting, forecasts, and accounts for cost showbacks and chargebacks. 
• Harness helps you manage costs by setting budgets and measuring variance from those budgets. It continuously monitors your usage and alerts you when your consumption diverges from the expected levels, allowing you to stay on top of cost and usage across your multiple-cloud resources. 
• The platform offers custom, data-driven dashboards that help track resources across every public cloud provider your team uses. 
• It uses the power of spot instances. Harness users can run workloads on orchestrated spot instances without worrying about interruptions; the platform handles this part smoothly.
• Finally, Harness offers automated cloud cost management and optimization features. For example, AutoStopping turns off non-production resources whenever they're not in use. 
• Harness shares cost information about apps, services, and environments without the need for human tagging, reducing both the effort teams would invest into this task. 

Cons of Harness

• As valuable as Harness's cost insights are, you still need to implement them manually since the platform doesn't support full cost optimization. It also lacks important cloud optimization and automation features such as autoscaling and spot instance use.

Conclusion: Harness provides in-depth recommendations about cloud use and expenses across multiple clouds, but the platform has limited automation capabilities and requires users to manually optimize their infrastructure.

Cloudcheckr – Cloud Cost Management and Recommendations

CloudCheckr is a cloud management tool that reports and generates recommendations for optimizing cloud costs. It started as a cloud security platform but has since expanded into cost management, spanning cost tracking, optimization, and resource inventory.

Pros of CloudCheckr

• CloudCheckr offers a solution that provides cloud cost allocation data, giving teams instant visibility into costs across resources from all the major cloud service providers. The platform displays cost over time in monthly formats, allowing your team to interact with the data and improve billing accuracy. 
• Alerts paired with cloud governance give your team more control and help avoid costly surprises. 
• The solution carries out hundreds of checks for idle resources, unused instances, mismatches in reserved instances, and more. It generates rightsizing recommendations only for snapshot cleanups and other resource resizing recommendations.
• Teams that have deployed reserved instances should use CloudCheckr's Savings Plan Recommendations report to check whether those services can be covered by a savings plan. 
• The platform helps users make future purchases by automatically re-allocating, resizing, and modifying their reserved instances. In addition to its policy-based cloud management features, CloudCheckr stores historical data on RI inventory and enforces tag-or-terminate policies for better infrastructure control.

Cons of CloudCheckr

• One of the biggest drawbacks of CloudCheckr is that it does not support automated spot instance selection and replacement. This cloud optimization tactic can bring outstanding savings, but this platform won't let you reap its benefits. But CloudCheckr comes with recommendations on relevant savings plans, which may not be optimal in the long run for many reasons. 

Conclusion: While the platform comes with a report presenting expenses on cloud services from various providers, implementing such changes will require further engineering work due to limited automation features.

Apptio Cloudability – Insights into Cloud Cost Optimization and Multi-Cloud Automation

Cloudability offers cloud financial management tools for monitoring, analyzing, and optimizing public cloud costs. The software enables companies to track their public cloud spend and plan better-informed budgets for cloud services.

Pros of Cloudability

• Cloudability monitors cloud resource usage, enabling you to reduce waste, balance risk, and cut costs. The tool collects data from the last 10 to 30 days and uses its algorithms to create rightsizing suggestions for your resources. 
• Cloudability lets you create custom dashboards for different products, departments, or roles within your organization. The solution's True Cost Explorer allows visual exploration of cloud costs and used data. You can link your use and expenses to relevant data dimensions such as business units, apps, and teams. As a result, your team can generate a more consistent budget and a baseline for forecasting the future.
• Cloudability simplifies multi-cloud budgeting. Users can view all of their cloud data on a single pane of glass, and the tool provides continuous updates with each new provider update. 
• Cloudability also lets you set up automated daily cleanups for detached EBS volumes, automatically shutting down operations during low-usage periods, and it allows you to schedule scaling of ASGs or stopping/starting of EC2 and RDS instances to see how many resources will be affected.

Cons of Cloudability

• While good at cloud cost optimization and data-rich dashboards, the solution lags behind in optimization and automation capabilities. It may be too narrow to guarantee the expected cost savings and streamline work with Kubernetes. 

Conclusion: Cloudability is a good option for smaller companies looking for a solution that tracks and analyzes cloud costs, provides insights, and automates their implementation. Larger organizations with more complex processes and infrastructure may require more automation features.

Recap: Top 6 Cloud Cost Management Software Solutions 

No time to read? We've got you covered. In 2022, the top cloud cost management and optimization tools are: 

1. CAST AI – a complete cloud optimization solution that delivers insightful cost reporting and automated optimization features. 
2. Spot.io – a solution that delivers savings through automating spot instances and reservations. 
3. Harness – a CI/CD solution that includes cloud management and BI tools for cost transparency and governance with limited automation.
4. Apptio Cloudability – financial management solution for monitoring, allocating, and analyzing cloud costs supporting multi-cloud. 
5. Cloudcheckr – tool that delivers cloud cost reporting, allocation, and optimization recommendations for manual implementation. 
6. Kubecost – a comprehensive cost management solution for Kubernetes environments that gives teams visibility into their spending, allocation options, and real-time alerts.

Summary: Pick the Right Cloud Cost Management Software for Your Business

Choosing the right cloud cost management software for your business isn't easy, but once you make that choice, you're on the right track, potentially saving even millions of dollars. 

Check out how others have saved big on their cloud bills and eliminated hours of engineer time that would otherwise be lost on managing cloud expenses manually.