6 Best Cloud Cost Management and Cloud Optimization Solutions for Azure, AWS, and Google Cloud – April 2024 Comparison

CAST AI splits cloud costs into project, cluster, namespace, and deployment levels. Your team can track expenses down to individual microservices and then produce a full estimate of your cluster costs. Its cost allocation works on a per cluster and per node basis. CAST AI also makes it easier to view and allocate costs in multi-cloud setups.

Powered by automation, CAST AI chooses optimal resources for your application’s requirements while reducing costs. When a cluster needs extra nodes, the platform’s automation engine selects the best performing instances at the lowest cost. By automating multi-shape cluster formation and rightsizing, it helps to make the right choices and avoid overprovisioning.

Users also benefit from CAST’s multiple autoscaling features. For instance, Horizontal Pod Autoscaler generates an optimal number of required pod instances. It then scales the replica count of your pods up and down and removes all pods if there’s no work to be done. CAST AI can also automatically pause and resume clusters so that you avoid paying for resources you don’t use. It also employs automated bin packing to maximize savings.

CAST AI allows you to achieve significant cost reductions with spot instance automation. You don’t have to worry about running out of capacity, as the platform continuously looks for the best alternatives.

Finally, the platform offers a variety of multi-cloud features. For instance, its Active-Active Multi-Cloud automatically distributes apps and replicates data over several cloud services to ensure business continuity in case of any failures. It also distributes traffic across all clouds your team uses, always selecting up and healthy endpoints.

Spot.io breaks down the infrastructure costs of clusters and offers insights into each layer. You can break down expenses into namespaces and individual workloads within every namespace, and you can further filter them.

For each workload, you get both compute and storage costs. You can use this data to analyze your application costs, perform chargebacks without extensive resource tagging, and estimate future cloud spend.

Spot.io offers a handy rightsizing recommendation mechanism. The tool monitors workload utilization in real-time, providing you with recommendations for manually adjusting the resource requirements, both per container and the entire workload. This allows for easier high-level visualization and quicker implementation.

Spot.io continuously checks for unschedulable pods. If it finds one, it scales your infrastructure up to ensure that all your pods have a place to run. In addition, Spot.io prioritizes downscaling the least utilized nodes and automatically removes them if it detects that it’s possible to move all pods running on them. In the case of scale-down or instance replacement, Spot.io terminates all pods and nodes.

Spot.io generates even more savings thanks to bin-packing algorithms. When it identifies an instance with workloads that can be distributed across the cluster, it triggers a scale-down to drain and terminate the instance. It lets you run clusters on spot instances without having to provision or scale instances.

Harness brings in-depth Kubernetes visibility by displaying the utilized, idle, and unallocated resources per workload and cluster. It visualizes cost information by projects, teams, business units, departments, and more. Additionally, Harness allows you to create periodic reports on your crucial cost and usage metrics. Governing cloud usage becomes easier with Harness thanks to its custom budgeting, forecasts, and accounts for cost showbacks and chargebacks.

Harness offers in-depth reporting and practical cloud optimization suggestions. These may involve cluster utilization, rightsizing, autoscaling, cleaning underutilized or orphaned resources, but also picking the optimal cloud service tier based on your usage trends. Before implementing the recommendations, Harness lets you run a what-if analysis.

The platform enables you to set budgets and keep track of expense variations. In addition, Harness continuously monitors your usage and alerts you when your consumption diverges from the expected levels, which is possible thanks to analyzing your historical spend trends.

Harness allows you to stay on top of cost and usage across your multiple-cloud resources. The tool displays insights on custom, data-driven dashboards to help track resources across every public cloud provider your team uses.

The platform embraces the power of spot instances. Harness users can run workloads on fully orchestrated spot instances without having to worry about interruptions. The platform handles this part smoothly.

Finally, Harness offers a range of automated cloud cost management and optimization features. For example, AutoStopping can automatically turn off non-production resources whenever they’re not in use. Harness provides cost information about your apps, services, and environments without the need for human tagging, reducing both the time and effort teams would need for this task.

Cloudability detects and monitors cloud resource consumption, helping teams balance risk and cost and reduce waste. The tool collects your usage 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 various products, departments, or roles within your organization. The solution’s True Cost Explorer enables visual exploration of cloud costs and used data. You can easily connect your use and expenses to relevant data dimensions such as business units, apps, and teams.

The tool helps you quantify the ROI of spot instances and offers other cloud optimization recommendations. As a result, your team can create a more consistent budget and a baseline for forecasting in the future.

Budgeting for multi-cloud configurations is challenging, but Cloudability makes it a lot easier. Users can display multi-cloud data together in a single pane of glass. All data is continuously refreshed with each new update from service providers.

Another benefit of Cloudability is its use of automation in several areas. First, you can set up and schedule daily cleanups of detached EBS volumes. The tool can automatically shut down operations during periods of low usage. It also lets users schedule the scaling of ASGs or the stopping/starting of EC2 and RDS instances, checking how many resources this will affect.

CloudCheckr offers a detailed view of cloud cost allocation data, granting you instant visibility into expenses across resources from all top cloud service providers. Its reports display cloud costs over time in a monthly format, allowing your team to interact with the data and improve your billing accuracy. Alerts paired with cloud governance will give your team more control and help to avoid costly surprises.

CloudCheckr generates resource purchasing recommendations thanks to predictive analytics. By identifying wasted resources and providing resource resizing recommendations, the platform can help you reduce costs. The solution performs hundreds of checks for idle resources, unused instances, mismatches in reserved instances, and more. However, the engine generates recommendations only for rightsizing and snapshot cleanups.

Teams relying on reserved instances should pay attention to CloudCheckr’s Savings Plan Recommendations report. It helps to check which services they deployed could be covered by their Savings Plans and create or customize purchase recommendations.

Although CloudCheckr focuses on policy-based cloud management, it still delivers some interesting automation features. The platform automatically re-allocates, resizes and modifies reserved instances. Thanks to storing historical data on RI inventory, it supports teams in making future purchases. CloudCheckr automatically enforces tag-or-terminate policies for better infrastructure control.

Kubecost provides flexible and customizable cost breakdown features. For example, you can divide costs by namespace, deployment, service, and more indicators across all the three major cloud service providers. 

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

Kubecost displays costs across multiple clusters and multi-cloud environments in a single view or through a single API endpoint. It enables linking real-time in-cluster costs (CPU, memory, storage, network, etc.) with out-of-cluster expenses from the cloud services – for example, tagged RDS instances, BigQuery warehouses, or S3 buckets. Users get context-aware, cluster-level reports to find an optimal balance between cost and performance matching their service requirements.

Kubecost doesn’t expose private data. Since you deploy it in your infrastructure, there’s no need to egress any data to a remote service. You retain and control access to sensitive cloud spend data at all times.