Top 6 Cloud Cost Management Tools For 2025

Cloud cost management can be challenging, particularly for companies utilizing cloud-native technologies like Kubernetes. The complexity of cloud billing, with its extensive details, often obscures the true breakdown of costs.

That’s why so many organizations are now deploying cloud cost management tools that let them identify which products and features drive their cloud spending, including development and internal teams. 

Equipped with this type of cost intelligence, your team can make more informed engineering and business decisions in the cloud.

We prepared a guide to cloud cost management to help you out. Let’s explore cloud cost management, why it’s worth your time, and which tools you should consider. 

What is cloud cost management all about?

Cloud cost management aims to minimize costs associated with cloud services while delivering the same level of performance. The term refers to monitoring, measuring, and controlling cloud costs to maximize your investment in public cloud services. 

Traditionally, cloud cost management has focused on waste reduction efforts, such as eliminating underutilized or forgotten resources and optimizing purchasing decisions (for example, by reserving capacity upfront). 

However, as companies evolve to adopt next-generation cloud services, cloud cost management focuses on architectural optimization. With the right architecture, teams can build products utilizing cloud services that tightly align with cost and customer utilization.

Benefits of cloud cost management

Managing cloud costs brings your team a host of benefits:

• More accurate forecasting, planning, and budgeting for your cloud spend.
• Greater cost visibility empowers your engineers to see the impact of their work on your budget.
• Cloud cost management allows you to discover areas of your solutions that could use rearchitecting for increased profitability.
• It facilitates making well-informed decisions about modifying your pricing structure and decommissioning resources.
• Benefit from load balancing, autoscaling, capacity reservation, volume discounts, saving programs, and Spot Instances.  
• Identify the best services to use for each application.

Building a cloud cost management strategy

Nearly every company has some type of governance, with security and compliance being the most important. Why not add a strategy for managing cloud costs to your governance plan? That way, you will gain more control over your cloud infrastructure and the fees it incurs.

This type of strategy can include the following points:

• Provide a uniform and succinct labeling approach that everyone can use.
• Instructions for setting up monitoring and alerting tools.
• Create a method for evaluating and optimizing costs on an ongoing basis.

Top 6 cloud cost management tools for 2025

Finding the right cloud cost management tool can be challenging, as multiple vendors offer many features, cloud pricing models, and use cases. Our team reviewed cloud cost management tools that help you get on top of your cloud spend. 

All the solutions discussed below offer value to teams looking to optimize their cloud bills and streamline cloud cost monitoring and reporting processes. However, our review shows that only those solutions that go beyond cost monitoring and reporting and offer automated cloud cost optimization can make a real difference.

Cast AI

Cast AI is an automation platform for analyzing, monitoring, and optimizing Kubernetes resources. Its core strength is automation, which includes node rightsizing, instance selection, autoscaling, Spot Instance automation, and resource decommissioning.

Cast AI provides users with valuable cost information by splitting cloud costs into projects, clusters, namespaces, and deployment levels. Teams can check expenses for individual microservices and forecast a complete estimate. Cost allocation works on a per-cluster and per-node basis, which makes it easier to view costs in multi-cloud setups. Exporting cloud cost management data is easy.

Powered by automation, Cast AI chooses optimal resources for application requirements while reducing costs. When a cluster needs extra nodes, the automation engine selects the best-performing instances at the lowest price and helps teams avoid overprovisioning. Workload optimization sets the right limits and requests for individual workloads to further reduce cloud waste.

Key takeaway: Cast AI automates cloud scaling and optimization, allowing you to achieve significant cost reductions. It continuously looks for the best resource alternatives, ensuring that your apps are always running at peak performance and optimal cost.

Pros of Cast AI

• The platform chooses optimal resources for your application’s requirements based on real-time data and machine-learning models while reducing costs through autoscaling and rightsizing features. 
• When additional nodes are required for a cluster, the automated system chooses the most efficient instances at the lowest price. Automating the creation of diverse cluster configurations and adjusting instance sizes helps prevent allocating more resources than necessary.
• Cast AI’s diverse autoscaling capabilities help users significantly reduce costs by automatically adjusting pod replica counts based on workload demands, ensuring optimal resource utilization. 
• When workloads decrease or stop, Cast AI scales down accordingly, removing unnecessary pods and fully emptying nodes to prevent paying for idle resources.
• Additionally, the solution leverages automated bin-packing techniques, maximizing savings by efficiently allocating workloads across nodes.

Cons of Cast AI

• Cast AI is primarily designed for cloud-native Kubernetes environments. Organizations relying heavily on legacy systems or non-containerized workloads may not fully benefit from Cast AI’s capabilities.

Conclusion: Cast AI is perfect for teams looking to reduce their cloud costs 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.

Spot by NetApp

Spot by NetApp is a cloud optimization platform that automates resource provisioning and management, optimizes performance, and reduces infrastructure costs by intelligently using Spot Instances and reserved capacity.

The platform breaks down the costs associated with deploying clusters and provides details on each layer. You can break down expenses into namespaces and individual workloads within every namespace and filter them further. 

You get compute and storage costs for each workload. You can use this data to analyze your application costs, perform chargebacks without a lot of resource tagging, and estimate future cloud expenses. 

Spot by NetApp comes with a rightsizing recommendation mechanism that monitors workload utilization in real-time. This mechanism offers teams recommendations for manually adjusting resource requirements, allowing for high-level visualization and faster implementation. 

The platform offers a cloud cost optimization solution that reduces cloud spending by focusing on Spot Instances rather than other opportunities. Spot by NetApp triggers a scale-down to drain and terminate the instance by identifying workloads that can be distributed across a cluster. It allows you to run clusters on Spot Instances without provisioning or scaling instances. 

Pros of Spot by NetApp

• It provides a detailed cost breakdown by namespace and individual workloads, including compute and storage expenses, with granular filtering capabilities.
• The tool offers real-time workload monitoring and proactive recommendations to rightsize resources at the container and workload levels, enabling easy visualization and rapid, disruption-free adjustments.
• Automatically identifies unschedulable pods and intelligently scales infrastructure, ensuring consistent application performance and availability.

Cons of Spot by NetApp

• The tool reduces costs by focusing on running workloads on Spot Instances. However, this may mean that you miss out on other cost-saving opportunities. Spot by NetApp, for example, offers limited rightsizing options for managing the size of VMs.

Key takeaway: Spot by NetApp reduces cloud costs thanks to its focus on Spot Instances. The tool suggests mostly running workloads on more Spot Instances, potentially missing out on other cloud cost optimization opportunities that may generate even more savings.

Harness

Harness cloud cost management is a continuous delivery and integration module focusing on improving cost transparency, optimization, and governance.

Harness gives you in-depth visibility into your Kubernetes clusters and cloud spending by displaying the utilized, idle, and unallocated resources per workload and cluster. It shows cost information by projects, teams, business units, departments, etc. You can also create periodic reports on your crucial cost and usage metrics. 

Harness makes governing cloud usage easier thanks to its custom budgeting, forecasts, and accounts for cost showbacks and chargebacks. It also provides in-depth reporting and practical cloud optimization suggestions. These may involve cluster utilization, rightsizing, autoscaling, and cleaning underutilized or orphaned resources. 

Before implementing the recommendations, Harness lets users run a what-if analysis to see what impact they would have on their costs. The platform allows you to set budgets, keep track of expense variations, and continuously monitor your usage so that alerts are sent when usage diverges from expected levels.  

Harness offers various automated cloud cost management and optimization features. For 

instance, AutoStopping can automatically turn off non-production resources whenever they’re not in use. 

Pros of Harness

• 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, etc. 
• Harness lets you create periodic reports on the most important cost and usage metrics. Thanks to custom budgeting, forecasts, and accounts for cost throwbacks and chargebacks, governing cloud costs becomes easier with Harness. 
• The platform offers custom, data-driven dashboards that help track resources across every public cloud provider your team uses. 
• Harness offers automated cloud cost management and optimization features. For example, AutoStopping turns off non-production resources whenever they’re not used. 

Cons of Harness

• As valuable as Harness’s cost insights are, you still need to implement them manually since the platform doesn’t support fully automated cost optimization. It also lacks important cloud optimization and automation features such as autoscaling and Spot Instance.
• The platform offers two different sets of recommendations: performance-optimized and cost-optimized. The first can increase your expenses, while the latter may lead to system performance issues – so it may be a tough choice. 

Key takeaway: Harness offers cost insights into your apps, services, and environments without human tagging, reducing the effort teams would need for this task. However, as valuable as this information is, you still need to implement them manually, as the platform doesn’t support full multi-cloud cost optimization. The platform also misses important cloud optimization and automation features such as autoscaling, rightsizing, and Spot Instance.

Cloudcheckr

Note: CloudCheckr has been acquired by NetApp.

CloudCheckr is a cloud management tool that focuses on reporting, making recommendations for cost optimization, and creating policy-based automation. The platform started as a cloud security tool but was later expanded to include cost management, tracking, optimization, and resource inventory solutions. 

The platform offers a detailed view of cloud cost allocation data, enabling you to see expenses across resources from all major cloud service providers. Its reports display cloud costs over time in a monthly format, letting your team interact with the data and improve billing accuracy. Alerts combined with cloud governance will give your team more control and help avoid costly surprises. 

Pros of CloudCheckr

• CloudCheckr uses predictive analytics to create resource purchasing recommendations. By identifying wasted resources and providing resizing recommendations, the platform helps reduce costs.
• The solution performs hundreds of checks for idle resources, unused instances, mismatches in reserved instances, and more. However, it generates recommendations only for rightsizing and snapshot cleanups. 
• Although CloudCheckr focuses on policy-based cloud management, it still delivers some interesting automation features, such as automatically re-allocating, resizing, and modifying reserved instances. The tool also stores historical data on RI inventory, supporting teams in making future purchases. 
• CloudCheckr automatically enforces tag-or-terminate policies for better infrastructure control. It also supports users with automated Spot Instance selection and replacement strategies that can deliver outstanding savings over time.

Cons of CloudCheckr

• Limited automation features that require further engineering effort.

Key takeaway: CloudCheckr is optimal for teams looking to improve their cloud spend and visibility. The platform helps uncover trends and provide savings recommendations across the organization, teams, or projects. However, these changes will not happen overnight due to limited automation features that require further engineering work to implement fully.

Kubecost

Kubecost is a cloud cost management tool for Kubernetes clusters. The platform delivers insights into Kubernetes cost allocation, monitoring, and alerts, providing flexible and customizable cost breakdown features. For instance, you can divide costs by namespace, deployment, service, and more indicators across all three major cloud service providers. 

Kubescost’s comprehensive resource allocation allows for generating more accurate showbacks and chargebacks, streamlining ongoing cost monitoring. Users can allocate costs to teams, individual applications, products, projects, departments, or environments. 

The cloud cost management platform allows linking real-time in-cluster costs (CPU, memory, storage, network, etc.) with out-of-cluster expenses from the cloud services such as tagged RDS instances, BigQuery warehouses, or S3 buckets.

Users get context-aware reports that help them achieve an optimal balance between cost and performance while matching their service requirements around the cloud infrastructure. 

Pros of Kubecost

• Kubecost offers flexible and customizable cost breakdown features, including dividing costs by namespace, deployment, service, and more indicators across the 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 near real-time in-cluster costs (CPU, memory, storage, network) with out-of-cluster expenses from 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 that matches their service requirements.

Cons of Kubecost

• Kubecost’s in-depth insights and cost reporting capabilities make it a great tool for teams looking to manage their multi-cloud infrastructure. However, as it doesn’t include cloud optimization features, you’ll need to implement relevant changes manually, which entails extra charges that don’t automatically guarantee savings. 

Key takeaway: Since Kubecost doesn’t include cloud optimization features, you’ll need to implement relevant changes manually. This will incur extra costs and doesn’t automatically guarantee savings.

Zesty

Originally built as a storage optimization tool, Zesty has grown to a reserved cloud capacity buy-back solution with an added element of rightsizing.  

Pros of Zesty

• Zesty scans your cloud infrastructure needs in real-time and adjusts your RI commitments accordingly.
• The tool collects Amazon EC2 usage patterns data to train its prediction model. It simultaneously analyzes the prices of RIs on the marketplace to pick the most suitable deals for your workload’s needs.
• The tool provides metrics related to your usage of RIs, including on-demand compute cost, effective EC2 cost, daily compute cost, and more. It also lets you view how your expenses change over time and future projections.

Cons of Zesty

• The tool lacks a more comprehensive approach to managing your cloud cost visibility and control. 

Conclusion: While Zesty helps to reduce bills for AWS users, it lacks a more comprehensive approach to managing and reducing your cloud expenses. Therefore, it primarily addresses the symptoms rather than addressing the underlying issue permanently.

Wrap up

Scaling cloud resources is easy – so easy that many teams lose control over their cloud spend. A missed bug or architecture oversight can quickly snowball into a massive bill at the end of the month. 

That’s why you need a cloud cost monitoring and optimization toolkit that provides detailed visibility, exhaustive reporting, and – ideally – automated optimization capable of handling cloud-native applications’ fast-changing requirements to generate serious cost savings.

Kubernetes cost optimization

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