Do Cost Allocation Tags on Textract Custom Adapter Versions carry through to per-page inference costs in Cost Explorer?

Based on the available information about AWS cost allocation tagging, I cannot definitively confirm whether Cost Allocation Tags applied to Textract Custom Adapter Versions will flow through to per-page inference costs in Cost Explorer.

The sources I have access to discuss cost allocation tagging for Amazon SageMaker Studio resources and Amazon Bedrock foundation models, but do not specifically address Amazon Textract Custom Adapters or their tagging behavior during inference operations.

For Amazon Bedrock, there is a specific feature called "application inference profiles" that was introduced to enable cost allocation tags to track on-demand foundation model usage. This suggests that for some AWS AI services, special mechanisms are needed to propagate tags from model resources to inference costs.

For Amazon SageMaker, automated tags from resources like domain ARN, user profile ARN, or space ARN can be used for cost tracking after being activated in the AWS Billing console.

However, without specific documentation about Textract Custom Adapter Version tagging behavior, I cannot confirm whether tags applied at adapter version creation time will automatically propagate to the per-page inference costs when that adapter is used in AnalyzeDocument or StartDocumentAnalysis API calls.

I recommend testing this in a non-production environment first, or contacting AWS Support directly to confirm whether Textract Custom Adapter Version tags flow through to usage costs before committing to this architecture for cost segregation across your environments.
Sources
Cost attribution - SageMaker Studio Administration Best Practices
Build a proactive AI cost management system for Amazon Bedrock – Part 2 | Artificial Intelligence

Hi Luis,

Great question on cost segregation strategy. Unfortunately, Cost Allocation Tags on Textract Custom Adapter Versions do NOT automatically flow through to per-page inference costs in Cost Explorer.

Why Tags Don't Propagate to Inference Costs

Here's how Textract billing works:

1. Resource-level tags (applied via CreateAdapterVersion) are associated with the adapter version ARN as a resource. These tags appear in AWS CUR for resource-level line items (e.g., if there were a charge for the adapter resource itself).

2. Per-page inference costs from AnalyzeDocument or StartDocumentAnalysis API calls are billed as API usage — they appear as flat usage line items under the AmazonTextract service (usage type like AnalyzeDocument:Custom). These usage-based charges are not tied to the adapter ARN in the CUR, so the adapter's resource tags are not inherited by the inference costs.

This is fundamentally different from services like Amazon Bedrock, which recently introduced Application Inference Profiles specifically to bridge this gap between resource tags and inference-time cost attribution. Textract does not currently have an equivalent mechanism.

In short: even though you tag each adapter version with environment=prod, when 10,000 pages are processed, those costs will show up as generic Textract API usage — not broken down by the environment tag.

Recommended Alternatives for Cost Segregation

Since tag-based attribution won't work for Textract inference costs, here are proven alternatives:

1. Separate AWS Accounts per Environment (Recommended)

The most reliable approach — use AWS Organizations with a dedicated account per environment (Dev, QA, Staging, Prod). Each account's Textract usage is automatically separated in consolidated billing. You can still maintain a centralized adapter management pattern using cross-account IAM roles.

2. Custom Metering via CloudTrail + Athena

• Enable CloudTrail logging for Textract API calls
• Each AnalyzeDocument call in CloudTrail includes the adapterVersion ARN in the request parameters
• Build an Athena query that joins CloudTrail events (with adapter version) against your tag mapping to attribute costs
• Example: Count API calls per adapter version per day, then multiply by the per-page price

3. Application-Level Metering

• Instrument your calling applications to log each Textract call with metadata (environment, project, adapter version)
• Publish custom CloudWatch metrics per environment
• Use this data for internal chargeback dashboards

4. Use Distinct IAM Roles per Environment

• Create separate IAM roles for each environment's Textract calls
• Use IAM access reports or CloudTrail filtering by role to attribute usage

Summary

For a centralized AI Hub architecture, I'd recommend Option 1 (separate accounts) if possible, or Option 2 (CloudTrail + Athena) if you must stay in a single account.

References:

• Textract Custom Queries tutorial
• Granular cost attribution for Amazon Bedrock (shows the pattern Textract lacks)
• Cost allocation tags in Cost Explorer

Hope this helps clarify before you commit to the architecture!