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There is no shared cloud cost API that automatically combines AWS, Azure and Google Cloud billing data. Build the pipeline from each provider’s native export, land the original records in your warehouse or object storage, and reconcile them there using an explicit accounting policy. AWS and Azure document CSV and Parquet export paths; Google Cloud Billing exports to BigQuery, so CSV, JSON or Parquet are downstream choices for that data—not a common native format guarantee across all three providers.
What “reconciled” means for multi-cloud spend
A successful export is not the same as reconciled spend. Reconciliation is the warehouse-side process of making records comparable and checking their totals against provider billing records or statements. The providers’ export documentation describes ways to obtain billing data; it does not guarantee that their exports will reconcile with one another or match an invoice without additional work.
Before building reports, write down the accounting rules your team will use. At minimum, decide how to handle:
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- Billing periods: which date determines the reporting period, and how invoice month relates to usage dates.
- Cost basis: whether a report uses actual or billed cost, amortized cost, usage-only measures, or another provider-defined measure. Keep different bases distinct rather than adding them together.
- Currency: whether totals remain in each provider’s billing currency or are converted, and which conversion policy applies.
- Credits, discounts and adjustments: whether these reduce reported spend, appear separately, or are allocated to services or teams.
- Shared and untagged costs: how costs that cannot be assigned directly to a team, project or workload are allocated.
- Changes over time: how late-arriving records, adjustments and revised exports update prior periods.
Compare totals separately by provider, currency, billing period and cost basis. Preserve the source records and the identifiers used to trace a warehouse figure back to its export or billing record.
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Which export path should you use for each cloud?
| Provider | Native path and datasets | Documented delivery or formats | Important qualifications |
|---|---|---|---|
| AWS | Data Exports; Cost and Usage Report 2.0 (CUR 2.0) is AWS’s recommended export for detailed cost and usage information. | Recurring delivery to a designated Amazon S3 bucket; CSV and Parquet are documented. | Data Exports uses a data query with an SQL query statement and table configurations, plus a destination configuration. Use the current table dictionary and API reference for implementation details. |
| Azure | Cost Management exports include actual cost, amortized cost, usage-only and FOCUS datasets. | CSV and Parquet are supported for applicable cost datasets. Parquet/Snappy is listed for cost and usage details. | Format availability varies by dataset. A multi-dataset export exposes Parquet only when every selected dataset supports it. |
| Google Cloud | Cloud Billing export to BigQuery: standard usage cost, detailed usage cost, pricing data and FOCUS usage cost. | Export lands in BigQuery tables. CSV, JSON or Parquet can be produced downstream if needed. | FOCUS usage cost is marked preview in the setup documentation. Pricing export is not retroactive. |
These paths are not one API contract. The export configuration, schema, destination and operational behavior remain provider-specific, so keep provider-specific ingestion even if the warehouse later presents a common reporting model.
AWS: recurring CUR 2.0 exports through Data Exports
Create a Data Exports query for the CUR 2.0 table, configure the selected data and destination, and deliver recurring exports to an S3 bucket. AWS’s Data Exports announcement described SQL-based column selection and row filtering for recurring S3 delivery; for a current implementation, follow the living API reference and user guide rather than relying on announcement details.
Ingestion should account for export manifests or other provider delivery metadata. Trigger processing from an S3 event or run a scheduled process that checks for completed exports and their manifests. Preserve the original files and metadata, and map AWS-specific column names and cost dimensions in a staging layer instead of assuming they match Azure or Google Cloud fields.
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Azure: choose the dataset before the file format
Cost Management exports offer actual cost, amortized cost, usage-only and FOCUS datasets. Choose the dataset based on the report’s accounting purpose first, then verify the allowed format for that selection. CSV and Parquet are documented for applicable cost datasets; Parquet/Snappy is listed for cost and usage details. A multi-dataset selection cannot use Parquet unless all selected datasets support it.
For historical retrieval, Microsoft documents a portal limit of up to 13 months for actual, amortized and FOCUS cost/usage datasets, and REST API retrieval of up to 7 years for those datasets. These limits do not apply generally to every export type: reservation datasets and price sheets have separate limits. Microsoft recommends the REST API for larger historical backfills.
Google Cloud: export billing data into BigQuery
Google Cloud Billing’s documented warehouse path is a BigQuery dataset. The available exports include standard usage cost, detailed usage cost, pricing data and FOCUS usage cost. Detailed usage adds resource-level information, such as a VM or SSD, beyond standard usage; GKE cluster cost detail requires GKE cost allocation to be enabled. FOCUS usage cost is marked preview in the setup documentation.
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The export requires a BigQuery dataset and the appropriate Cloud Billing and BigQuery permissions. Dataset location affects initial historical coverage: for first-time standard, detailed or FOCUS exports, EU or US multi-region datasets can include data retroactive to the beginning of the previous month, while supported single-region datasets begin from the date the export is enabled. Initial catch-up can take up to five days. Pricing export does not backfill.
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How to structure the warehouse pipeline
Keep ingestion provider-specific and make the normalized model a deliberate downstream layer. That separation lets reports share common dimensions without discarding fields needed to investigate provider-specific charges.
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- Land untouched source data. Store provider exports in provider-specific raw tables or object storage. Retain original files, manifests where available, extraction metadata and the export run or source identity.
- Build staging transformations. Normalize timestamps, currencies, account, subscription and project identifiers, service and SKU fields, resource IDs, tags or labels, credits, adjustments and cost measures. Preserve source columns alongside normalized fields.
- Keep cost bases separate. Model actual or billed and amortized measures as distinct fields. Document which measure each report uses; do not sum unlike cost columns into a single total.
- Define record replacement and deduplication. Use documented provider record identifiers and replacement behavior where available. Allow records to be updated and late data to arrive; do not assume every extract is immutable or final.
- Reconcile at a controlled grain. Compare warehouse totals with provider billing records or statements by provider, currency, billing period and cost basis before combining them for cross-cloud reporting.
- Apply allocation rules. Make shared-cost and untagged-cost allocation explicit, version the policy, and distinguish allocated values from provider-reported values.
- Monitor data quality. Check for missing dates, currencies or account identifiers, duplicate records, schema changes and export lag. Alert on failures rather than silently publishing incomplete periods.
The exact SQL, partitioning, idempotency strategy and update logic depend on the target warehouse and each provider’s export semantics. There is no universal schema or single end-to-end API example established for all three clouds.
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Do not choose a format on the assumption that every provider offers the same output. AWS Data Exports and applicable Azure Cost Management exports document CSV and Parquet paths. The documented Google Cloud workflow writes billing data into BigQuery tables; formats such as CSV, JSON or Parquet are choices for a later extract or warehouse pipeline stage.
- CSV: a documented export option for AWS and applicable Azure datasets. Validate the selected dataset and its schema during ingestion.
- Parquet: a documented option for AWS and applicable Azure exports, with Parquet/Snappy listed for Azure cost and usage details. Azure’s multi-dataset restriction still applies.
- JSON: the cited provider paths do not establish JSON as a uniform native billing export format. If downstream consumers need JSON, generate it from the warehouse or another pipeline stage and retain the original provider records.
Keep raw data in its received form when possible. A downstream conversion can make data convenient for consumers, but should not replace the source copy or erase provider-specific fields.
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Plan for schema changes and export continuity
Cloud billing schemas can evolve. Google Cloud’s Cloud Billing export documentation states: “The table schema for any of the Cloud Billing data exports can change.” Google recommends using BigQuery views to project exported fields into a preferred structure.
Apply the same separation between raw and normalized data across providers: ingest source fields without assuming a permanently fixed schema, then expose a controlled model to reports. Alert when columns are added, removed or change type. For AWS, check the current Data Exports table dictionary and query configuration; for Azure, validate the selected dataset and output format against the current API version and contract.
History continuity is also an operational concern. Google Cloud does not automatically backfill former destination rows after export settings move to a different project or dataset, and deleted records cannot be backfilled. Preserve a durable copy before migrations or deletion, and verify the historical coverage of the new destination rather than assuming it contains the old dataset’s rows.
Quick Recap
Operational checklist before publishing spend reports
- Confirm each provider’s export is enabled, permissions are in place, and the destination is accessible to the ingestion process.
- Record the selected dataset, cost basis, format, destination and export configuration for each provider.
- Preserve raw records, file or export metadata, and source identifiers before transforming data.
- Define how periods, currencies, credits, discounts, adjustments and shared costs are treated.
- Test duplicate handling, late-arriving data and the process for updating prior-period totals.
- Validate warehouse aggregates against provider billing records or statements at matching periods and cost bases.
- Monitor export lag, missing data, schema changes and failed ingestion before marking a period complete.
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