Visualizing and Analyzing Costs with AWS Cost Explorer - Tag Strategy and Savings Plans
Visualize AWS spending with cost analysis by service, account, and tag, plus ML-based anomaly detection and cost forecasting. Learn how to optimize costs using Reserved Instance and Savings Plans purchase recommendations.
Overview of Cost Explorer
AWS Cost Explorer is a service for visualizing and analyzing your AWS usage costs and consumption. It lets you analyze the past 13 months of cost data across multiple dimensions including service, account, region, and tag. The ML-based cost forecasting feature predicts spending for the next 12 months, supporting budget planning. While Budgets specializes in alert-based cost management, Cost Explorer focuses on analysis and visualization. Integration with Cost Anomaly Detection automatically detects cost increases that deviate from normal patterns and helps identify root causes.
Cost Analysis and Filtering
The Cost Explorer console displays daily and monthly cost trends in graphs, with the ability to group and filter by dimensions such as service, linked account, region, instance type, and usage type. Activating cost allocation tags lets you categorize costs by custom dimensions like project, environment (dev/staging/prod), and team. Tags are activated in the Billing console, and tag-based analysis becomes available for data collected after activation. You can also use the API (GetCostAndUsage) to programmatically retrieve cost data and build custom dashboards or automated reports. A common pattern is to periodically export Cost Explorer reports to S3 and perform advanced analysis with Athena or Amazon Quick Sight (formerly Amazon QuickSight).
Purchase Recommendations and Savings Plans
Cost Explorer provides purchase recommendations for Reserved Instances (RIs) and Savings Plans. It analyzes past usage patterns and suggests purchase plans that offer the greatest cost savings with 1-year or 3-year commitments. Savings Plans come in two types: Compute Savings Plans (applicable to EC2, Fargate, and Lambda) and EC2 Instance Savings Plans (applicable to specific instance families). Compute Savings Plans offer greater flexibility but slightly lower discount rates. Recommendations optimize from both coverage (the percentage of usage covered by RIs/SPs) and utilization (the percentage of purchased RIs/SPs actually being used).
Cost Explorer Pricing
The Cost Explorer console is free to use. API calls (GetCostAndUsage, GetCostForecast, etc.) are billed at approximately USD 0.01 per request (as of August 2026). Cost Anomaly Detection is free, with no additional charges for receiving anomaly detection alerts. Activating cost allocation tags is free, but managing and governing tags requires organizational processes. When using Organizations consolidated billing, you can centrally analyze costs for all member accounts from the management account.
Using Cost Anomaly Detection
Cost anomaly detection is a mechanism for catching unexpected increases in spending early. When you set up monitors by service, by account, or by cost category, machine learning detects increases that deviate from normal trends and notifies you with alerts. When an anomaly is detected, a breakdown is shown of which services or usage types contributed to the increase, so identifying the cause becomes faster. By setting thresholds appropriately and adjusting them so that small noise does not cause frequent alerts, you can focus only on the cost fluctuations that you should truly pay attention to. Being able to notice before the bill is finalized is a major advantage.
Tag Strategy and Governance
To analyze costs correctly by project or team, thorough tagging of resources is a prerequisite. If you define naming conventions along dimensions such as environment, purpose, and owner, and enforce required tags with organizational policies, later cost allocation becomes easier. Build a routine process that regularly identifies untagged resources and corrects them. Because inconsistent tagging lowers the accuracy of analysis, it is important to manage tags continuously as part of governance. Well-maintained tags are useful not only for cost allocation but also for security and operational automation.
Automating and Sharing Reports
For ongoing cost management, automating reports is effective. In addition to analysis in the console, if you retrieve cost data through the API, you can assemble your own dashboards and regular reports. If you need more detailed line items, a common pattern is to output Cost and Usage Reports to S3 and perform in-depth analysis with Athena or Quick Sight. If you regularly deliver reports summarized along the dimensions that teams and executives care about, cost awareness takes root in the organization. Creating a state in which everyone can grasp the current situation is the foundation for continuous optimization.
The Practical Cycle of Cost Optimization
Visualization is not the goal but the starting point. You run a cycle that connects the waste revealed by analysis to concrete actions. Typical measures include stopping and deleting unused resources, rightsizing excessive specifications, and purchasing commitments for steady-state usage. Compute Optimizer recommendations are helpful for resizing decisions. For commitment purchases, you look for the optimal point while watching both coverage (the percentage covered by discounts) and utilization. By repeating this cycle monthly and measuring the effects, you can continuously reduce costs.
Summary
AWS Cost Explorer visualizes AWS spending through multi-dimensional cost analysis and ML-based forecasting. Combine cost allocation tags for project-level and team-level analysis, RI/Savings Plans purchase recommendations, and Cost Anomaly Detection for anomaly alerts to drive continuous cost optimization.
References (Official AWS Resources)
The primary sources for this page are the official AWS website and documentation. Check the official pages below for the latest specifications and pricing.
- AWS Cost Explorer official page
- AWS Compute Optimizer official page
- AWS Organizations official page
- AWS Documentation (official)
If this page and the official documentation disagree, treat the official documentation as authoritative.