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Cloud Computing
Updated on Sep 1, 2025

Top 30+ Cloud Cost Management Tools: Focus & Pricing

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Cloud spending is often one of the fastest-growing costs for modern businesses, which is projected to reach $723.4 billion in 2025, up from $595.7 billion in 2024.1 However, 84% of organizations consider managing cloud costs their top challenge.2 This article helps you navigate the landscape of cloud cost management tools by breaking down key features, pricing models, and multi-cloud capabilities in a clear, structured way.

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

Table 1. Customer utilization focus of cloud cost management tools

Updated at 09-01-2025
CategoryToolMain Purpose / Notes
Cloud Cost Visibility & ReportingCombines cost visibility with performance monitoring
Multi-cloud cost intelligence and allocation
FinoutUsage-based cost dashboards and allocation
Vega CloudCloud spend visibility and reporting
PointFiveVisibility into cloud financial waste
Tracks and analyzes AWS spend
Tracks and analyzes Azure spend
Monitors infrastructure & apps, provides cost metrics
Cloud Cost Optimization & AutomationKubernetes cost allocation and optimization
Automates Kubernetes scaling and resource optimization
Performance-driven cost optimization
DensifyWorkload right-sizing and policy-based optimization
ProsperOpsAutomates AWS Savings Plans & Reserved Instances
XosphereAutomated Spot instance and EC2 cost optimization
ZestyAutomated commitment & storage optimization
Virtana OptimizeRightsizing and performance optimization for hybrid cloud
SedaiAI-driven autonomous cloud optimization
Enterprise FinOps & Governance PlatformsEnterprise cost governance, allocation, reporting
Multi-cloud FinOps, cost visibility & governance
Cost management + license optimization
ServiceNow Cloud Cost ManagementIntegrated FinOps with ITSM/ITFM
CloudBoltHybrid cloud management & cost visibility
KionMulti-cloud governance & cost visibility
YotascaleCost allocation and FinOps reporting
Aquila Clouds BillOpsBilling automation and FinOps governance
TernaryEnterprise-grade cost analytics and reporting
Kubernetes & multi-cloud optimization, governance-as-code
Cloud Infrastructure & Cost PlanningOpen-source IaaS management and orchestration
Cloud management and automation platform
Estimates cost of IaC changes before deployment

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

Table 2. Pricing and integration

Updated at 09-01-2025
ToolsFree TrialFree TierPricingMulti-cloud management
Datadog Cloud Cost Management✅ (14-days)$ 7.50 per host per monthAWS, Azure, GCP
CloudZeroNAAWS, Azure, GCP
FinoutNAAWS, Azure, GCP, Oracle
Vega CloudNAAWS, Azure, GCP, Oracle
PointFiveNAAWS, Azure, GCP
AWS Cost Explorer$0.01 per requestAWS
Azure Cost Management✅ (30-days)NAAzure
Amazon CloudWatch$0.50 per GBAWS
KubecostNAAWS, Azure, GCP
Cast AI$1,000 per month + $5 per CPUAWS, Azure, GCP
Turbonomic✅ (30-days)NAAWS, Azure, GCP
Densify✅ (30-days)$2.50 per host per monthAWS, Azure, GCP
ProsperOpsNAAWS, Azure, GCP
XosphereNAAWS
Zesty$500 + $5 per managed vCPUs per monthAWS, Azure
Virtana OptimizeNAAWS, Azure
SedaiNAAWS, Azure
Apptio CloudabilityNAAWS, Azure, GCP
VMWare Tanzu CloudHealthNAAWS, Azure, GCP, Oracle, Alibaba
Flexera Cloud Cost ManagementNAAWS, Azure, GCP
ServiceNow Cloud Cost ManagementNAAWS, Azure, GCP
CloudBoltNAAWS, Azure, GCP
KionNAAWS, Azure, GCP
YotascaleNAAWS, Azure, GCP
Aquila Clouds BillOpsNAAWS, Azure, GCP
TernaryNAAWS, Azure, GCP, Oracle, Alibaba
Harness Cost ManagementNAAWS, Azure, GCP
Apache Cloudstackopen-sourceopen-sourceAWS
Codiac✅ (14-days)$189 per host per monthAWS, Azure, GCP
Infracost$1,000 per monthAWS
Nops.io✅ (14-days)NAAWS, Azure, GCP

Read the Multi-Cloud Environment section to learn more.

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

agent selection in the installation in DataDog, one of the cloud cost management tools

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

dashboard of CloudZero, one of the cloud cost management tools

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.

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.

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.

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).

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.

Cast AI

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.

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.

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.

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.

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 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.3

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.

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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.
Ezgi is an Industry Analyst at AIMultiple, specializing in sustainability, survey and sentiment analysis for user insights, as well as firewall management and procurement technologies.

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