Visual overview
Detailed records are exported durably, while Cost Explorer remains the interactive analysis surface.
Technical reference
CUR 2.0 turns AWS billing into an analyzable dataset. The schema and line-item type are part of the financial meaning, not implementation trivia.
AWS Data Exports provides CUR 2.0 with a consistent schema and recurring delivery.
S3 becomes the durable boundary for downstream SQL, reporting, or financial data pipelines.
Supported Data Exports queries can constrain the data set and rename columns, reducing unnecessary processing and exposure.
Reservation, Savings Plans, discount, cost-category, and tag fields must be interpreted using the AWS data dictionary.
Use when a dashboard-level aggregate is insufficient for allocation, reconciliation, or custom unit economics.
Service limits and capabilities can change. Values shown here reflect the current AWS documentation; use the linked official sources below as the source of truth.
CUR 2.0 is the detailed billing dataset, not another dashboard
AWS Data Exports creates recurring exports from Billing and Cost Management datasets. AWS currently recommends Cost and Usage Report 2.0 (CUR 2.0) as the path for detailed AWS cost and usage data. Instead of interacting with charts one filter at a time, an organization can export granular records for durable downstream analysis, join them with business metadata, and build repeatable reporting pipelines.
Data Exports can deliver recurring output to Amazon S3. With the console you can select columns, and through the CLI or SDK AWS supports SQL-based column selection, row filtering, and column renaming for supported exports. CUR 2.0 improves on legacy CUR with a consistent schema and additional structure, which reduces the pipeline fragility caused by columns appearing and disappearing as usage changes.
Line-item semantics matter more than file size
CUR data is detailed because billing events are represented as line items with dimensions such as payer and usage account, product, usage type, operation, pricing attributes, reservation information, Savings Plans information, cost categories, and activated resource tags where applicable. The data dictionary is the authoritative reference for interpreting each field. Analysts should not infer the meaning of a column from its name alone, especially around discounts and commitment negations.
This level of detail enables allocation and reconciliation that would be cumbersome in a dashboard. For example, a team can aggregate cost by activated cost-allocation tag, compare effective commitment rates with On-Demand-equivalent usage, or construct unit-cost metrics after joining billing data with business outcomes. It also creates a data-governance responsibility because exported billing data can expose account identifiers, resource details, and commercially sensitive prices.
Choose an export shape for the downstream question
AWS Data Exports supports more than CUR 2.0, including cost-optimization and FOCUS-aligned datasets, and can integrate a cost-and-usage dashboard with Amazon QuickSight. The right table depends on whether the goal is detailed billing reconciliation, optimization recommendations, standardized financial reporting, or executive visualization. Exporting every column is not always necessary; selecting only the needed fields can reduce processing effort and limit exposure of sensitive cost information.
Legacy CUR still exists, but AWS documentation identifies CUR 2.0 through Data Exports as the new and recommended detailed cost-and-usage path. Teams with existing CUR pipelines should plan migrations based on schema compatibility and downstream consumers rather than assuming an abrupt deletion of legacy reporting.
Key takeaways
- 01
CUR 2.0 through AWS Data Exports is AWS's recommended detailed cost-and-usage export path.
- 02
Data Exports supports recurring S3 delivery and customizable column/row selection.
- 03
The CUR data dictionary defines billing line-item semantics and should drive analysis logic.
- 04
Activated tags, cost categories, commitment details, and pricing fields enable granular allocation and optimization analysis.
- 05
Cost Explorer is interactive; Data Exports is suited to repeatable downstream analytics pipelines.
Official AWS sources
Use these primary AWS resources for the source material behind this article and for deeper reference.