Top 30+ Cloud Cost Management Tools: Focus & Pricing

Cloud spending is often one of the fastest-growing costs for modern businesses.¹ However, more than 80% of organizations consider managing cloud costs their top challenge.²

We provide concise comparisons and actionable information to make evaluating and selecting the right cloud cost management tools faster and easier for your organization.

Cloud cost management solutions

Pricing and integration

Read the Multi-Cloud Environment section to learn more.

Cloud cost visibility & reporting

Datadog Cloud Cost Management

Datadog Cloud Cost Management combines cost and performance data to give both engineers and FinOps teams visibility into cloud and SaaS spending.

• Cost visibility in workflows: Shows cloud, container, and SaaS costs in dashboards, catalogs, and notebooks. Engineers can see costs while working.
• Cost allocation: Attributes spend to teams, products, or services, including GPU, network, and shared infrastructure costs. Uses tagging and custom rules for accuracy.
• Collaboration & reporting: Standardizes data for FinOps reporting, generates CSV exports, and supports cross-team collaboration between Engineering, Finance, and FinOps.
• Governance & budgeting: Alerts for anomalies, tracks budgets, monitors commitment coverage, and enforces organization-wide FinOps policies.

Figure 1. DataDog agent selection in the installation

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CloudZero

CloudZero is a cloud cost management platform that connects spending to business value, helping teams control costs and improve profitability.

• Cost visibility: Combines multi-cloud and Kubernetes spend in one platform. Shows detailed unit economics like cost per user, feature, or token. Spots hidden cloud cost-saving opportunities.
• Business alignment: Links cloud spending to business outcomes, supporting faster decisions, better margins, and efficient scaling.
• Collaboration: Provides a shared view for engineering, finance, and product teams to coordinate on cost decisions.

Figure 2. CloudZero dashboard

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Azure Cost Management

Azure Cost Management helps organizations track, monitor, and control cloud spending. It supports cost analysis and governance across Microsoft Azure resources.

• Cost Analysis: Categorize cost data by resource group, service, location, or tag. Create custom filters to focus on relevant data.
• Budgets: Set spending limits by time period and amount. Configure alerts to notify teams of overspending or unexpected drops.
• Cost allocation: Assign costs to business units, projects, or customers. Use rules to split or transfer costs as needed.

AWS Cost Explorer

AWS Cost Explorer is a cloud cost analysis tool for AWS accounts that helps teams visualize, track, and manage spending over time.

• Cost visualization: Displays cost and usage trends using graphs and tables. Supports grouping by accounts, services, or custom filters for granular insights.
• Forecasting: Predicts future costs and usage based on historical data, helping with budgeting and planning.
• Granular data access: Supports resource-level tracking for detailed analysis, with hourly, daily, and multi-year historical data.
• Custom reports: Save and schedule reports to monitor costs by service, account, or usage type.

Amazon CloudWatch

Amazon CloudWatch is a monitoring and management service for AWS, on-premises, and hybrid environments. It collects metrics, logs, and events to track system health and resource usage.

• Observability: Provides a unified view of applications, infrastructure, and networks. Offers one-second metric visibility and up to 15 months of data retention for historical analysis.
• Automation: Uses alarms and event rules to trigger automatic actions, reducing response times and improving efficiency.
• Container insights: Monitors Kubernetes, ECS, and Fargate workloads by aggregating CPU, memory, disk, and network data, along with container-level diagnostics.
• Reporting: Delivers real-time and historical insights to help identify issues, optimize performance, and control operational costs.

Cloud cost optimization & automation

Kubecost

Kubecost helps teams control Kubernetes cloud costs by revealing which teams, products, and resources drive spend.

• Real-time visibility: Track usage across clusters, clouds, and on-prem environments.
• Cost allocation: Link spend to teams and workloads, supporting chargeback and showback.
• Unified monitoring: Combine in-cluster costs (CPU, memory) with cloud service charges from AWS, Azure, and GCP.
• Cost optimization insights: Get tailored recommendations to rightsize resources and cut costs by 30–50% without harming performance.
• Alerts and governance: Set budgets, receive alerts, and generate reports to prevent overruns.

Originally an open-source project, Kubecost is purpose-built for Kubernetes and integrates tightly with tools like Prometheus and Grafana. It offers self-hosted and SaaS options for individuals, teams, and enterprises.

Cast AI

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Source: CAST AI ³

Cast AI is a platform for automating Kubernetes application performance and cloud cost management. It focuses on optimizing resources while maintaining stability.

• Workload optimization: Automatically right-sizes workloads, bin-packs nodes, and scales clusters to reduce overprovisioning.
• Pod management: Adjusts pod specifications and autoscaling to balance performance and cost.
• Cost monitoring: Tracks expenses in real time by namespace, workload, and allocation group.
• Resource insights: Provides efficiency analysis, cost anomaly detection, and historical cost trends.
• Cluster dashboard: Offers visibility into CPU, GPU, and memory usage across clusters, helping waste reduction efforts.

Turbonomic

Turbonomic is an Application Resource Management (ARM) platform that automatically allocates resources to improve performance and reduce cloud costs.

• Dynamic resource allocation: Adjusts resources in real time across cloud, hybrid, and on-prem environments. Supports applications, containers, VMs, and Kubernetes clusters.
• Cloud cost optimization: Reduces cloud waste, right-sizes workloads, and optimizes GPU and VM usage for efficiency.
• Kubernetes & container management: Automatically resizes pods, moves workloads, and scales clusters based on demand and service-level objectives (SLOs).

Enterprise FinOps & governance platforms

Apptio Cloudability

Apptio Cloudability is a FinOps platform that helps teams track, analyze, and optimize cloud spending across multiple cloud providers.

• Business alignment: Maps cloud costs to business value, budgets, and KPIs. Supports unit economics, workload planning, and financial forecasting.
• Integrations: Works with tools like Jira, Datadog, and PagerDuty for a complete view of cloud costs and usage.

VMware Tanzu CloudHealth

VMware Tanzu CloudHealth is a multi-cloud FinOps platform that helps organizations manage costs, optimize resources, and enforce governance.

• Reporting & visibility: Provides tailored, scalable reports with drill-down analysis across cloud environments.
• Budgeting & forecasting: Supports cost allocation, chargebacks, and predictive financial planning.
• Asset & sustainability management: Tracks cloud assets and supports GreenOps initiatives for sustainable cloud usage.

Flexera Cloud Cost Management

Flexera is a multi-cloud environment cost management platform that helps organizations track, optimize, and govern cloud spending.

• Cost visibility & allocation: Provides a unified view of spend across clouds. Uses billing tags to automatically allocate costs for accurate budgeting and forecasting.
• Governance & FinOps alignment: Enables setting FinOps goals, tracking KPIs, and applying automated cost policies across IT, finance, and business units.
• Sustainability: Measures carbon emissions alongside cost, supporting green cloud operations with policy-driven automation and ISO-14064 compliant data.

Harness Cost Management

Harness Cost Management provides automation and visibility to help control multi-cloud expenses.

• Granular reporting: Breaks down costs by workload, team, or environment for accurate allocation.
• AI-driven optimization: Detects idle resources, shuts them down, and restarts them when needed to reduce non-production costs.
• Cluster orchestrator: Automates EKS node scaling and Spot Instance usage to improve efficiency.
• Budgets and alerts: Tracks spend against daily, monthly, or quarterly budgets with forecasting and notifications.

Cloud infrastructure & cost planning

Apache CloudStack

Apache CloudStack is open-source software for managing Infrastructure as a Service (IaaS) clouds. It is not a cost tool, but a private cloud management system with cost management feature.

• IaaS features: Provides compute orchestration, networking, user and account management, resource accounting, and a web-based interface.
• Access options: Supports management through a web interface, command line, and RESTful API. It also offers compatibility with AWS EC2 and S3 APIs.
• Open-source: Allows flexibility in hardware and software choices, supported by an active community.

Infracost

Infracost integrates cloud cost visibility into the engineering workflow. It shows the financial impact of code changes before deployment.

• Pre-deployment visibility: Engineers see cost estimates for infrastructure changes in GitHub, GitLab, or Azure Repos.
• Cost guardrails: Alerts highlight potential budget overruns before resources are created.
• Policy enforcement: Automatically checks tagging and FinOps best practices across teams.
• Code-level recommendations: Suggests optimizations and fixes for cost, policy, or tagging issues during code review to prevent mistakes and reduce rework.

Codiac

Codiac is a cloud platform for running and managing Kubernetes workloads. It focuses on automating infrastructure and making deployments repeatable.

• Zombie Mode: Turns off idle environments on a schedule and restarts them when needed to reduce costs.
• Auto-scaling and workflow optimization: Supports zero-touch deployments and adjusts resources automatically to match demand.

For further information on categorization, read the cloud cost optimization tool categories section.

Cloud cost management strategies

Effective cloud cost management requires collaboration between finance, IT, and business teams. This approach, known as FinOps, ensures costs are controlled while maintaining performance and agility.⁴

1. Understand and track costs

• Review billing data from cloud providers to identify high-cost services and usage patterns.
• Attribute costs to teams, projects, or products using tags and resource labels.
• Track cost center spending with dedicated accounts or clear ownership metadata.

2. Budgeting and forecasting

• Set budgets aligned with product plans and delivery schedules.
• Forecast usage and spend based on historical patterns and expected growth.
• Adjust budgets dynamically as workloads scale or new services are deployed.

3. Optimize resource usage

• Identify unutilized and idle cloud resources, such as unattached storage or low-use servers.
• Right-size instances to match actual workload requirements for CPU, memory, and storage.
• Leverage automation with auto-scaling, load balancing, and on-demand resources.

4. Leverage cost-saving options

• Use Reserved Instances and Savings Plans for predictable workloads to reduce pricing.
• Leverage Spot Instances for flexible, non-critical workloads to minimize costs.
• Select appropriate storage tiers to match performance needs and reduce unnecessary spending.

5. Integrate cost awareness into development

• Embed cost management and optimization into the SDLC from planning to monitoring.
• Evaluate software licensing to avoid paying for unused licenses.
• Implement cloud-native designs with auto-scaling and efficient architecture.

6. Monitor and govern continuously

• Detect cost anomalies using alerts and machine learning-based monitoring.
• Apply governance policies to enforce resource ownership, tagging, and budgeting rules.
• Regularly review multi-cloud deployments to balance costs, availability, and vendor dependencies.

Multi-Cloud Environment

A multi-cloud environment involves using multiple major cloud providers, such as AWS, Azure, GCP, Oracle, or Alibaba, simultaneously. We include public cloud providers to the table. Organizations adopt multi-cloud setups to avoid vendor lock-in, improve redundancy, and leverage specialized services from different providers. Managing such environments requires centralized visibility, cost control, and governance, as workloads, budgets, and resources are distributed across multiple platforms.

Cloud cost optimization tool categories

Cloud cost visibility & reporting

This category focuses on tracking and understanding cloud spending across providers. Tools provide:

• Cost dashboards for real-time spend tracking.
• Historical and predictive reporting to forecast future costs.
• Granular views by account, project, or service to identify high-cost areas.

Purpose: Helps teams make data-driven decisions on cloud management, ensure accountability, and spot anomalies before they escalate.

Cloud cost optimization & automation

This category involves automating cost-saving actions and optimizing resource usage. Features include:

• Rightsizing workloads to match performance with actual demand.
• Idle and unused resource identification to remove waste.
• Automated scaling and provisioning for dynamic workloads.
• Use of reserved, spot, or savings-plan instances to reduce cost.

Purpose: Reduces unnecessary spending while maintaining application performance and agility.

Enterprise FinOps & governance platforms

These platforms support cross-functional collaboration between finance, IT, and business units. They provide:

• Budgeting, cost allocation, and chargeback capabilities.
• Policy-driven governance to enforce cloud cost controls.
• Metrics and KPIs aligned with business value.

Purpose: Enables organizations to adopt FinOps practices, ensuring financial accountability and optimizing cloud ROI.

Cloud infrastructure & cost planning

This category is about planning cloud architecture and resource allocation to meet both cost and performance goals. Features include:

• Forecasting future resource needs based on historical usage.
• Scenario modeling for multi-cloud deployment decisions.
• Integration with development and operations workflows for cost-conscious design.

Purpose: Aligns cloud infrastructure with organizational strategy, controlling costs while maintaining flexibility.

Choosing the right cloud cost management tools

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Source: Cloud Cost Governance⁵

The Visibility, Insights, and Actions (VIA) framework is a structured approach to assess cloud cost management tools and align them with organizational needs.⁶

Visibility

Effective CCM starts with comprehensive visibility into the cloud environment. This includes:

• Viewing costs at service, tag, and hierarchical levels.
• Analyzing trends to predict spending.
• Monitoring costs by accounts, regions, projects, and resources.
• Providing unified visibility for multi-cloud environments and specific insights into container/microservice usage.

Insights

Insights drive optimization and cost control through:

• Unit economics and resource utilization analysis.
• Budgeting, forecasting, and real-time alerts for cost anomalies.
• Identifying waste, such as idle resources, and receiving actionable recommendations for optimization.
• Customizable dashboards and anomaly detection for targeted cost management.

Actions

Informed actions help streamline cost management and maximize efficiency by:

• Automating tasks like stopping idle resources, scaling usage, and tagging for cost attribution.
• Integrating with tools like Jira, ServiceNow, Slack, and Teams for collaboration and accountability.
• Utilizing advanced features like Kubernetes bin packing and spot instance management for further optimization.
• Leveraging training and support resources to enhance usability and adoption.

By using the VIA framework, organizations can effectively manage cloud costs, maximize return on investment, and ensure cost efficiency throughout their cloud lifecycle.

Benefits of cloud cost management tools

1. Cost savings through optimization

By identifying idle resources, underutilized services, and opportunities like reserved instances or savings plans, these tools help optimize cloud costs, ensuring efficient use of cloud investments.

2. Improved visibility and allocation

Cloud cost management solutions enhance cost transparency by offering detailed breakdowns of expenditures across departments, projects, and teams. This empowers engineering teams to understand and control their cloud budget effectively.

3. Enhanced planning and forecasting

With historical data analysis and real-time monitoring, organizations can accurately predict future cloud costs and allocate resources strategically, aligning with business objectives.

4. Scalability and efficiency

Cost management tools streamline cloud infrastructure to support future growth, enabling organizations to scale efficiently in response to evolving needs.

Key features of cloud cost management tools

Cost breakdown

Granular visibility into cloud costs by categorizing expenses by resource, project, or department helps identify high-spending areas and optimize resource use.

Cost anomaly detection

AI-driven tools monitor usage patterns to detect and alert on irregular spending, such as unexpected spikes, enabling quick corrective action and reducing waste.

Cost estimation and forecasting

Predictive analytics project future costs based on historical data, aiding in budgeting for reserved instances, savings plans, and scaling needs.

Cloud Deployment Models

Cloud services are delivered through three main deployment models, each offering unique benefits:

1. Public Cloud: Provided by third-party vendors, public clouds offer cost-effectiveness, scalability, and high reliability, accessible to the general public or industry groups.
2. Private Cloud: Dedicated to a single organization, private clouds provide enhanced security, customization, and performance through exclusive resources.
3. Hybrid Cloud: Combining public and private infrastructures, hybrid clouds offer flexibility, enabling organizations to balance sensitive data storage with scalable public cloud resources and support legacy systems.

Key components of cloud computing

Cloud computing is based on three primary service models, each catering to different needs:⁷

1. Infrastructure as a Service (IaaS): Offers virtualized computing resources, such as virtual machines and storage, giving users full control over operating systems and applications without needing physical hardware.
2. Platform as a Service (PaaS): Provides a platform for application development and management, including tools for testing and scaling, simplifying the development process by handling infrastructure complexities.
3. Software as a Service (SaaS): Delivers software applications online, enabling access from multiple devices without requiring installation or maintenance, enhancing convenience and reducing operational overhead.

AI-based cloud cost management: revolutionizing cost control

AI cloud cost management tools utilize advanced capabilities like predictive analysis, anomaly detection, and automation to optimize cloud costs and maximize savings. Unlike traditional methods relying heavily on human input and static policies, AI operates on real-time data to dynamically manage cloud infrastructure, enabling cost savings and enhanced cloud cost optimization.⁸

Key capabilities of AI in cloud cost management

1. Predictive analysis for cost optimization AI leverages historical data to forecast future resource needs, enabling organizations to plan their cloud infrastructure accordingly. For example, an e-commerce SME can analyze patterns to anticipate high traffic during a sales promotion. AI ensures optimal resource allocation during such peaks, minimizing costs while maintaining performance. This approach reduces cloud spend and prevents disruptions in cloud computing services.
2. Cost anomaly detection AI continuously monitors usage patterns and cost data to detect anomalies like unexpected surges in storage costs caused by misconfigurations. By addressing such issues early, AI minimizes waste and prevents overspending, reinforcing effective cloud cost management strategies.
3. Automation for resource management Traditional resource scaling relied on fixed schedules, often leading to inefficiencies. AI dynamically adjusts resources, such as scaling down during low-traffic periods or powering off idle cloud infrastructure, ensuring cost optimization without compromising performance. For multi-cloud cost management, this automation is critical in reducing associated costs across diverse platforms.
4. Detailed cost allocation and transparency AI-based tools improve cost allocation by categorizing expenses across projects, teams, or departments, making inefficiencies visible. For example, an organization can identify excessive resource use in one department and take corrective action. Enhanced transparency supports better cost management and waste reduction efforts.
5. Actionable recommendations and policy enforcement AI systems provide cost optimization recommendations and enforce predefined policies, such as shutting down unnecessary environments during non-peak hours. These automated systems ensure adherence to budget constraints and enhance cloud management tasks.

Challenges addressed by AI in cloud cost management

1. Resource overheads AI identifies inactive or underutilized resources, reallocating or deactivating them as needed to minimize costs. For example, reserved instances and savings plans are optimized based on usage patterns.
2. Fluctuating costs Predictive analysis allows organizations to anticipate cost variations, such as a spike in storage due to a misconfiguration, and resolve them proactively.
3. Multi-cloud complexity Managing resources across multiple cloud providers is simplified by AI’s ability to integrate platforms, offering a unified view of costs and optimizing resource allocation.

FAQs

Reference Links

1.

Gartner Forecasts Worldwide Public Cloud End-User Spending to Total $723 Billion in 2025

2.

New Flexera Report Finds that 84% of Organizations Struggle to Manage Cloud Spend | Press Release

3.

Kubernetes Cost Optimization Platform – Cast AI

Cast AI

4.

Taking control of cloud costs: The FinOps imperative

5.

Kanumuri, S., & Zeier, M. (2024). Cloud Cost Governance. In Scaling Cloud FinOps: Proven Strategies to Accelerate Financial Success (pp. 127-154). Berkeley, CA: Apress.

6.

Kanumuri, S., & Zeier, M. (2024). Cloud Cost Governance. In Scaling Cloud FinOps: Proven Strategies to Accelerate Financial Success (pp. 127-154). Berkeley, CA: Apress.

7.

Cloud Computing - Infracost

Infracost

8.

Malikireddy, S. K. R. (2020). Transforming SME Cloud Cost Management with Artificial Intelligence. International Journal of Scientific Research in Science and Technology, (7):3, 520-542.

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Ezgi Arslan, PhD.

Industry Analyst

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Ezgi holds a PhD in Business Administration with a specialization in finance and serves as an Industry Analyst at AIMultiple. She drives research and insights at the intersection of technology and business, with expertise spanning sustainability, survey and sentiment analysis, AI agent applications in finance, answer engine optimization, firewall management, and procurement technologies.

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