Amazon Bedrock · Finder
Remove Idle Bedrock Customized Models
RemoveIdle Bedrock Customized Models
Delete idle Amazon Bedrock customized models incurring storage costs when not serving inference. CloudFix identifies unused fine-tuned models for removal.
What happens, in order.
Finder · read-only
Finds the opportunity
Scans your connected accounts with a read-only role. It can see resource metadata and usage; it can’t change anything.
Report
Reported to you
It appears in your Recommendations view with what it would save. The change is yours to make.
Finder role
Read-only. It can see resource metadata and usage. It can’t change anything.
Fixer role
Not used here: this one is reported by the finder, so the change is yours to make.
Saves by
CloudFix identifies Bedrock customized models that have gone unused for an extended period, with no inference requests. Removing them eliminates the storage charges associated with these models, optimizing costs for AWS users.
Amazon Bedrock allows users to create customized foundation models, but these models incur storage costs even when not actively used for inference. CloudFix identifies customized Bedrock models that have not received any inference requests for an extended period, suggesting they might be idle and candidates for deletion to optimize storage costs.
Manual Fix Required
CloudFix identifies potentially idle Bedrock customized models but does not automatically delete them. Deleting a customized model is irreversible. Users must manually verify that the model is no longer needed for any purpose before performing the deletion.
Overview
Problem Statement
Customized foundation models in Amazon Bedrock represent valuable assets but also contribute to ongoing storage costs. Models created for specific projects, experiments, or older versions that are no longer actively serving inference requests become idle resources, leading to unnecessary expenditure.
Solution Identification
CloudFix uses your cost and usage data and Bedrock’s inference activity to identify customized models that have gone unused for an extended period. This flags the model as potentially idle, allowing users to manually investigate and decide whether to delete the model to save on storage costs.
AWS Services Affected
| Service | Icon |
|---|---|
| Amazon Bedrock |
 |
| Amazon CloudWatch |
 |
How CloudFix Identifies the Opportunity
CloudFix uses your cost and usage data and Bedrock’s own inference activity in Amazon CloudWatch to find customized models that have gone unused for an extended period. Finding is read-only. Each model appears in your Recommendations with its estimated storage savings, and you decide whether to delete it. You can exclude models you want to keep.
Manual Fix Steps
After CloudFix identifies a potentially idle Bedrock customized model:
- Verify Idleness: Confirm that the identified customized model is genuinely not in use and is not required for any current or future applications, experiments, or development. Double-check any processes or applications that might reference it.
- Consider Backup/Archival (Optional): If there’s any chance the model might be needed later, consider exporting or backing up relevant model artifacts or training data before deletion, according to your data retention policies.
- Delete the Customized Model: Use the AWS Management Console or the Bedrock API to delete the specific customized model.
Note: Always refer to the latest official Amazon Bedrock documentation for the precise API calls and procedures for managing custom models.
FAQ
Q: Why doesn’t CloudFix automatically delete the model?
A: Deleting a customized model is irreversible. User verification is essential to prevent accidental removal of a valuable model that might be used intermittently or planned for future use.
Q: What are the potential savings?
A: Savings come directly from eliminating the storage costs associated with the idle customized Bedrock model. The amount depends on the size of the model.
Q: Does deleting the model impact active inference endpoints using it?
A: If a model is truly idle (no inference requests), deleting it shouldn’t impact active workloads. However, ensure no provisioned throughput or endpoints are still configured to use the model before deletion, as this could cause errors.
Q: What considerations are important before deleting?
A: Confirm the model is unused, check dependencies, consider backup needs, and review access policies to prevent accidental deletion.
Related Resources
- Custom models in Amazon Bedrock (AWS Documentation) – (Check for specific management/deletion sections)
- Monitoring Amazon Bedrock (AWS Documentation)
- Amazon Bedrock Endpoints and Quotas (Includes API info)
- ML Delete Idle Bedrock Models (CloudFix Support)
Ready to start saving on AWS? See how much you could cut from your cloud bill with a free cost optimization assessment, or explore CloudFix automated Finder/Fixers that eliminate waste across 30+ AWS services.
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