Cloud cost management tools explained

For years, the story has been the same: The cloud is cheaper than DIY. Then the bills come. The price tags are denominated in pennies, but by the end of the month all those pennies add up. Often to sums that give the CFOs a heart attack. Cloud cost management tools are an answer.

Cloud cost management is a business discipline aimed at monitoring, analyzing, and optimizing an organization’s cloud computing spend. It gives organizations greater visibility into how cloud resources are being used and can also help identify and maybe eliminate unused or overprovisioned services. When usage is stable, they will predict future cloud costs to provide more fine-tuned forecast budgets.

Cloud cost management tools aid organizations in this practice by tracking all the bills associated with an organization’s deployments, allocating them to the various teams responsible for their accumulation. That way the group that added too many fancy features that need too much storage and server time will have to account for their profligacy. The good programmers who don’t use too much RAM and disk space can be rewarded.

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What to look for in cloud cost management tools

Smaller teams with simple configurations can probably get by with the stock cloud cost management services provided by their cloud vendor of choice. Cost containment is a big issue for many CIOs now and the cloud companies know it. They’ve started adding better accounting tools and alarms that are triggered before the bills reach the stratosphere. See , , and for the big three clouds.

Once your cloud commitment gets bigger or maybe spread between several vendors’ clouds, stand-alone cost management tools start to become attractive. They’re designed to work with multiple clouds and build reports that unify the data for easy consumption. Some even track the machines that run on premises so you can compare the cost of renting versus building out your own server room.

A big role can be allocating costs to specific projects or business units. Good tools will let you tag or label collections of machines so the bill can be split up accurately. Then the business units can defend their spending.

In many cases, cloud cost managers are part of a larger suite designed to not just watch the bottom line but also enforce other rules such as security or governance. Some are not marketed directly as cloud control tools but have grown to help solve this problem. Some tools for surveying enterprise architectures or managing software governance now track costs at the same time. They can offer the same opportunities for savings that purpose-built cloud cost tools do — and they help with other management chores as well.

Can AI make a difference?

AI is everywhere and it’s no surprise that cloud management companies are incorporating new features into their stack. Their value depends on your workflow. If the goal is to build a big table with all the machines and daily charges, there’s no need for large language models (LLMs) or any of the modern solutions.

But some vendors use the term AI to include more sophisticated algorithms for tasks such as predicting future usage. These can look at past performance and adjust hardware allocations in advance. They can also offer financial forecasts so the CFO will know what bills will look like in advance. Is that true artificial intelligence or just a clever statistical algorithm? It doesn’t matter once it’s running successfully.

A few vendors are using LLMs to enable users to ask open-ended questions about their data. These approaches may be useful for nontechnical users or teams exploring new ways to analyze their cloud costs.

Leading vendors for cloud cost management tools

Cloud cost management tools come in a wide variety, from components in broader cloud platforms to standalone tools. CIOs should check with their existing cloud providers such as Amazon Web Services, Google, and Microsoft to see what cost management capabilities they offer. But be sure to also look at independent tools because, after all, it’s not much in cloud providers’ interests for you to spend less on their platforms.

What follows is an alphabetical list of the best cloud cost tracking and analysis tools. The market is rapidly expanding as enterprise managers recognize the need to get a grip on their cloud spend. All these tools can help govern the burgeoning empire of server instances that your organization relies on for its cloud operations, which may stretch around the world.

CAST AI: DevOps teams tasked with managing Kubernetes clusters can turn to CAST AI for a collection of tools that optimize usage across multiple clouds, hybrid deployments, and on-prem architectures. The endless cycle of capacity prediction and node allocation is handled by the product so that the clusters grow and shrink as needed. Integration with other essential tools like Terraform, Grafana, Prometheus, and Jira help DevOps teams ensure that the clusters are performing optimally. Its standout features include:

• Kubernetes users get a tool aimed at delivering smooth support for containers
• Integration with other popular tools provides for a simple but powerful workflows

CloudAdmin: Dashboards created by CloudAdmin are simple and direct. The tool tracks cloud usage and offers suggestions for rightsizing your servers or converting them to reserved instances. Server instances can be allocated to teams and then tracked with a budget. If spending crosses a defined line, alerts are integrated with email or other common communication tools to notify personnel of the need for attention. Its standout features include:

• Carefully filtered data feeds extract the key details about spending to save time wading through too much information
• Automated alerts can stop runaway spending when it crosses thresholds

CloudZero: The platform from CloudZero was designed from the beginning to make it easy to compare cloud costs to business metrics. That is not just tabulating the costs per machine, but connecting those costs to the business operations by computing values such as cost per customer or cost per feature. Providing granular insights enables business decisions about feature sets or services rendered to be made mathematically instead of emotionally. Its standout features include:

• Anomaly detection for moments when key metrics diverge
• Automatic cost allocation and normalization to simplify comparisons

Datadog: Watching over cloud machines, networks, serverless platforms, and other applications is the first job for Datadog’s collection of tools. Tracking cloud costs is just one part of the workload that also includes network monitoring and security. Its telemetry gathers data about performance and cost, and Datadog builds this into a sophisticated dashboard to help organizations understand both application cost and performance. Its standout features include:

• Broad suite for infrastructure monitoring across multiple clouds
• Monitoring of not just spending but all tasks for responsible cloud curation

Densify: Densify wants to help DevOps teams manage Kubernetes clusters juggling containers and VMware instances. The best way to run your clusters, according to Densify, is to keep precise, meticulous records of load and then use this data to scale up and down quickly. Densify’s optimizers focus on containers, pods, deployments, ReplicaSets, nodes, node groups up to full clusters. Careful attention to deployment improves scaling by 50% or more. Densify’s FinOps tool generates extensive reports to help keep application developers and bean counters happy. Its standout features include:

• A focus on container-level deployment provides direct control over the resource usage that leads to cost savings
• Reporting identifies cost problems and gamifies savings identification

Finout: The center of the Finout experience is one feature called “Megabill,” a single dashboard that tabulates all the costs, past and future, to produce a bottom-line number. The tool consolidates costs across the major clouds and offers virtual tagging to track operational subsets. The tool’s waste detection is said to be “codeless,” and it watches over the deployments and generates Jira tickets when overspending appears. Its standout features include:

• Costguard tracks spending and identifies opportunities for saving
• Integrations with major platforms such as Databricks, Datadog, Snowflake, and soon Confluent provide long-term record keeping

Flexera One: The Flexera One cloud management suite tackles many cloud management tasks, such as tracking assets, managing licenses, and organizing governance to orchestrate control. An important section of the suite is devoted to controlling the budget. The tool offers multicloud accounting for tracking spending with elaborate reporting broken down by team and project. Flexera One also offers suggestions for optimizing consumption by targeting wasteful allocations, and it provides automated systems to put these observations into practice. The tool is not just focused on cloud hardware but also license costs and audit defense. Its standout features include:

• Integrates cost tracking across hardware and services to support all levels of management
• Supports larger management roles like audit defense

Flexera Spot: Teams working on planning resources for AI workflow can turn to Flexera’s Spot to optimize usage of often costly GPU hardware. At the core of the Spot stack is a set of tools like CloudCheckr, a product that focuses on controlling cloud costs and security and Spot Eco, a tool that will optimize deployment. The stack will constantly balance usage demands with the price for both reserved instances and spot machines. Its RI Marketplace, for instance, will constantly look for extra machines available at bargain prices. Its standout features include:

• Monitor compliance with privacy regulations by tracking security configuration
• Right-size reserved instances by tracking baseline consumption

Harness: The AI-managed platform orchestrates deployment across multiple clouds and Kubernetes clusters by tracking usage and executing on the plans to minimize unneeded consumption. The long-term commitment manager balances the cost-savings of reserved instances with the ability to terminate unneeded spot instances. Harness’s cost management features watch for anomalies compared to historic spending, generating alerts for teams. The platform also manages governance and access policies to maximize security. The tight integration with the CI/CD pipelines makes development teams full partners in long-term management. Its standout features include:

• Deep integration with the development pipeline to make cost savings part of the software creation process
• New features such as Database DevOps and an Artifact registry prompt long-term integration with development workflows

IBM Cloudability: Big Blue’s Apptio subsidiary makes a large collection of tools for managing IT shops, and Cloudability is its tool for handling cloud costs. The tool breaks down the various cloud instances in use, allocating them to your teams for accounting purposes. Ideally, teams can control their own costs and predict future usage with the reports and dashboards. Cloudability wants to nurture collaborative response by opening data to managers, accountants, and developers. Integration with other tools such as IBM Kubecost help ensure the clusters minimize their budget. Integration with many other tools such as Jira and PagerDuty help speed response to spending anomalies. Its standout features include:

• Planning future purchasing of reserved instances to lock in savings for the constant demand
• Focus on cost management balances labor with product feature set

IBM Kubecost: Teams that rely on Kubernetes to deploy pods of containers can install Kubecost to track spending. It will work across all major (and minor) clouds as well as pods hosted on premises. Costs are tracked as Kubernetes adjusts to handle loads and are presented in a unified set of reports. Large jumps or unexpected deployments can trigger alerts for human intervention. Its standout features include:

• Optimized for tracking how Kubernetes deployments affect costs
• Dynamic recommendations track opportunities for lowering spending

IBM Turbonomic: IBM uses Turbonomic to deliver an AI-powered solution for managing deployment to match application demand with infrastructure. The tool will automatically start, stop, and move applications in response to demand. The data driving these decisions is stored in a warehouse to train the AI that will be making future decisions. All these changes and predictions for future usage are reported on dashboards for all stakeholders. Its standout features include:

• Full-stack integrated graphics to understand demand and cost across an application
• Designed to automate resource allocation to save engineering teams from the chore

nOps: The nOps platform has three major roles (inform, operate, optimize) and mainly works with AWS and its forms of resource allocation. Granular data about usage is collected and displayed as dashboards or reports by the features under the “inform” umbrella. Details about day-to-day use such as data of business unit economics or anomaly detection are gathered under the “operate” umbrella. Finally, the real value for the platform comes when the “optimize” features balance the long-term commitment for reserved instances with the savings that can come from the spot market. The automated commitment algorithms can ensure that there’s little waste from locking in a commitment. Its standout features include:

• A focus on AWS ensures that the algorithms are tuned for that cloud’s idiosyncrasies
• ML-driven optimization routines ensure close to complete utilization

Nutanix: Organizations with large multicloud deployments can use Nutanix as an abstraction layer to deploy apps. While much of the layer handles chores such as storage or resource management, one part called Cost Governance (formerly Beam) tracks costs across a range of installations, including private cloud machines hosted on premises. The tool can be customized to generate accurate cost estimates of private installations by accounting for heating and cooling costs, hardware, and data center rent. This makes it easier to make accurate decisions about allocating workloads to the lowest-cost deployment. The process can be automated to optimize and rightsize hardware to simplify management. Its standout features include:

• Metering of private clouds builds direct insight into the costs of on-premises hardware
• Budget alerting and dynamic optimization help rightsize consumption to minimize costs

ServiceNow: Teams running extensive collections of microservices rely on ServiceNow to manage some or even all the stack through its constellation of products such as AppEngine or Integration Hub. Many of the tools are customer-facing solutions like IT automation, but there are more backend tools for optimizing IT operations by intelligently managing performance. It offers several dashboards that offer a high-level view of costs and a detailed breakdown. The tracks past performance and offers forecasts for future budgets. Its standout features include:

• Broad selection of tools for tracking and optimizing IT assets
• Risk management well integrated with governance tools

Splunk AppDynamics: Tracking and reining in containers across a multicloud, hybrid, and on-prem environment is the goal for AppDynamics. It was formerly known as Replex and is now part of the Splunk Observability Portfolio. The cost management job is just one part of a larger system that tracks performance and log files looking for anomalies and making predictions for the future. Splunk’s AIOps, for instance, can watch for security violations and overspending. A policy control layer offers granular restrictions to ensure teams have access to what they need but are locked out of what they don’t. Its standout features include:

• Integrates cost management with general application monitoring
• Integrated with Open Telemetry as part of a goal of unifying data across local, on-prem machines with a multicloud environment

Umbrella (formerly Anodot): The first job for Umbrella’s collection of cloud monitoring tools is to track the flow of data through the various services and applications. If there’s an anomaly or hiccup that will affect users, it will raise a flag. Umbrella deploys a machine learning algorithm that compares the current spending to the past so that unusual spending spikes stand out. Tracking the cost of instances and pods across your multiple clouds is part of this larger job. The dashboard produces a collection of infographics that make it possible to study each microservice or API and determine just how much it costs to keep it running in times of high demand and low. This granular detail gives you the ability to spot the expensive workloads and find a way to prune them. Its standout features include:

• Integrated multicloud analytics provides normalized and actionable data for both detecting unusual spending while also providing accurate planning for regular deployments
• A generative AI called COST GPT offers an opportunity to dive into spending data and explore it with a natural language interface.

VMware Aria CloudHealth: VMware, the Broadcom subsidiary, built Tanzu CloudHealth (formerly called Aria Cost and Aria Automation) to manage deployments across all major cloud platforms as well as hybrid clouds. The cost accounting module tracks spending, allocating it to business teams while optimizing deployments to minimize costs. The modeling layer can build out amortization and consumption schedules to forecast future demand. Financial managers and development teams can drill down into these forecasts to focus on specific applications or constellations of services. The larger product line integrates the cost management with automated deployment and security enforcement. Its standout features include:

• Spending governance ensures that teams are following individual budgets for resource consumption
• Integrate cloud costs with business metrics and key performance indicators to understand the connection between computational costs and the bottom line

Yotascale: Much of the responsibility for cloud costs comes from the engineers who write and deploy the code. They make the granular decisions to startup more instances and store more data. Yotascale wants to put more information in their hands to enable them to optimize their hardware consumption with tools designed to track machines and allocate their costs directly to the teams responsible. The forecasting tools can also spot anomalies, raising alerts to prevent any surprise bills at the end of the month. The dashboards promise to replace static spreadsheets with dynamic, responsive and accurate “single pane of glass.” Its standout features include:

• Engineer-targeted tools deliver budget information directly to the teams building the software and starting up the machines
• Automated tracking delivers forecasts and flags problems and overconsumption

Zesty: While many cloud managers offer insights through sophisticated reports, Zesty is designed to automate the work of spinning up and shutting down extra instances. A key feature enables it to watch the reserved market for deeply discounted instances with excess capacity on the cloud. It offers a tool informed by artificial intelligence algorithms that can work with AWS’s API to make decisions that keep just enough machines running to satisfy users without breaking the budget. The tool can even control the amount of disk space allocated to individual machines while buying and selling processor time on the spot from reserved instance marketplaces. Its standout features include:

• Deep management of details such as storage space allocation to minimize costs
• Integration with the spot market to take advantage of the lowest possible costs

Essential reading

• DIY cloud cost management: The strategic case for building your own tools
• What is finops? Your guide to cloud cost management