The following are the release notes for TetraScience AI Services versions 1.4.x. TetraScience AI Services empower developers and scientists to transform data into decisions through seamless, secure APIs, and enable intelligent automation across scientific, lab, and enterprise workflows.

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NOTE

TetraScience AI Services are currently part of a limited availability release and must be activated in coordination with TetraScience along with related consuming applications. For more information, contact your customer account leader.

v1.4.0

Release date: 5 October 2026

TetraScience AI Services v1.4.0 adds support for registering Bring Your Own Model (BYOM) models as container images and fixes bugs in private-namespace publishing, AI Workflow installation, batch inference, model promotion, and BYOM registration.

Here are the details for what's new in TetraScience AI Services v1.4.0.

Prerequisites

TetraScience AI Services v1.4.0 requires the following:

• Tetra Platform v4.4.1 or later
• For publishing "Bring Your Own Model" (BYOM) AI workflows under private-* namespaces: Tetra Platform v4.5.3 or later and TetraScience CLI (ts-cli) v2.1.0 or later

New Functionality

New functionality includes features not previously available in TetraScience AI Services.

Bring Your Own Model as a Container Image

You can now register a model packaged as a container image and receive an inference endpoint for it, instead of supplying model artifacts alone.

Bring Your Own Model as a container image is a beta release.

Enhancements

Enhancements are modifications to existing functionality that improve performance or usability, but don't alter the function or intended use of the system.

• Pay-per-token LLM serving endpoints no longer mask personal information: AI Services no longer applies a personally identifiable information (PII) masking guardrail to pay-per-token LLM serving endpoints, including endpoints configured before the upgrade. Request content, including any personal information it contains, now reaches the model unmasked. If your use of these endpoints depends on PII masking, contact TetraScience before you upgrade.

Bug Fixes

The following bugs are now fixed:

• Model promotion returns a clear error for unsupported asset types: promoting a view now returns a client error that names the problem, instead of a server error.
• Private-namespace publishing enforces permissions, file size, and namespace ownership: publishing to a private-* namespace now checks the caller's permissions, enforces the file-size limit, and confirms the caller's organization owns the namespace.
• AI Workflow installations no longer appear stuck: an installation now completes when its Databricks job succeeds, even if the completion event is missing.
• AI Workflow installation completes when an alias update fails: a failed alias update no longer aborts the whole installation or leaves the installation record unusable.
• Installation records include the namespace: GET /v1/install returns the top-level namespace field again.
• Batch inference works for existing AI workflows: batch inference no longer fails with a missing-weights error for AI workflows created before the upgrade.
• Telemetry trace search works as documented: the telemetry trace search request now matches the published API reference.
• BYOM retraining keeps the trained model: retraining a BYOM AI workflow saves the trained model again.
• BYOM AI Workflow installation completes: installation no longer fails while registering the inference endpoint.
• BYOM registration rejects a container image that doesn't exist: registering a model against a missing image now fails, instead of reporting success.
• BYOM registration accepts organization and tenant names containing hyphens or uppercase letters.
• Re-uploading an unchanged BYOM version is stable: re-uploading the same version no longer changes its manifest identifier each time.
• BYOM scaffolding targets your environment: generated scaffolding no longer contains a hard-coded internal environment.

Deprecated Features

This release deprecates no features. For more information about TDP deprecations, see Tetra Product Deprecation Notices.

Known and Possible Issues

The following are known limitations of TetraScience AI Services v1.4.0:

• Telemetry Sink SQL Warehouse Is Fixed at Creation: A provisioned telemetry sink's SQL warehouse can't be changed after the sink is created. To use a different warehouse, contact TetraScience to provision a new sink.
• Sink Change History Isn't Tracked: A sink record shows who created it, but not who has changed its configuration since.
• Trace Search Isn't Bounded by Time: A trace search request isn't bounded by a time range, so a search can return traces across the full history of a sink. Result counts are capped, and you can narrow results by filtering on time in your query.
• No Telemetry Deprovisioning Path: A provisioned telemetry sink can't be removed through any supported path. Contact TetraScience Support before removing a telemetry sink.
• Vector Store Deletion Does Not Remove Databricks Resources: Deleting a vector store removes the internal database record, but the associated Databricks vector search endpoint and index, the Delta tables, and the knowledge base files in Amazon S3 are not removed and require manual cleanup. Contact TetraScience Support to remove the orphaned resources.
• Vector Store Update Status Remains syncing After a Re-index Job Completes: After a vector store update is triggered, the status returned by the vector store API might remain syncing even after the re-index job completes successfully. The updated content is indexed and queryable, but because the status doesn't transition back to ready, any automated process that polls the status to confirm completion can wait indefinitely. To verify that an update completed, query the vector store and confirm that the results reflect the updated content. TetraScience is working with the vendor on a fix.
• Model Promotion to a Different Region Fails: Promoting a model to a target environment in a different AWS region from the source fails. Promotion between environments in the same region is unaffected.
• Some Install and Registry Requests Report Success When They Should Be Rejected: For example, uninstalling an AI workflow version that is not installed returns a success message. Confirm the result by reading the installation or registry state after the request.
• AI Workflows Generated by ts-cli Need Manual Edits Before They Install: An AI workflow created with ts-cli init ai-workflow fails to install until the generated files are edited. Contact TetraScience for the required changes.
• An Incorrect ETag When Completing a Multipart Upload Returns a Server Error: Completing a multipart file upload with an incorrect ETag returns a 500 response instead of a 400, and marks the file as failed. Restart the file upload instead of retrying the completion request.
• BYOM Model Metadata Can Repeat Training Information: A BYOM AI workflow's stored model metadata can contain the same training information under both training and training_parameters. The AI Services UI is unaffected.
• Model Training UI Upload Failure: The Upload Supporting Files button in the AI Services UI does not work for model training workflows. File uploads fail because of an incorrect S3 file path constructed by the UI. As a workaround, upload model training files and initiate model training by using the AI Services API directly. The related cross-origin (CORS) error was fixed in Tetra Platform v4.6.0.
• Analyst Policy Install Workflow UI Access: On Tetra Platform v4.4.1, users assigned a role with an Analyst policy can view, but not use, the Install Workflow button even though the policy doesn't allow them to install an AI workflow or run an inference. This issue is fixed in Tetra Platform v4.4.2 and later.

Upgrade Considerations

To upgrade to the latest AI Services version, see Update the AI Services UI Version in the TetraScience AI Services User Guide.

If your use of pay-per-token LLM serving endpoints depends on PII masking, contact TetraScience before you upgrade. See the PII masking entry under Enhancements.

After the upgrade, verify the following:

• Existing AI Workflow installations continue to function correctly
• Inference requests complete successfully
• Knowledge base and vector store operations function as expected
• Asset promotion completes successfully for models, volumes, and aliases
• If telemetry has been enabled for your organization, telemetry writes and trace read-back complete successfully
• The AI Services UI displays correctly

GxP Impact Assessment

All new TetraScience AI Services functionalities go through a GxP impact assessment to determine validation needs for GxP installations.

Enhancements and Bug Fixes do not generally affect Intended Use for validation purposes.

TetraScience AI Services is currently a limited availability release. Model Promotion and Bring Your Own Model as a container image are beta releases. Items marked as either a beta release or a limited availability release are not validated for GxP by TetraScience. However, customers can use these prerelease features and components in production if they perform their own validation.

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Security

TetraScience continually monitors and tests the codebase to identify potential security issues. Security updates are applied on an ongoing basis.

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Quality Management

TetraScience is committed to creating quality software. Software is developed and tested following the ISO 9001-certified TetraScience Quality Management System.

For instructions on how to validate core AI Services functionality programmatically, see Test TetraScience AI Services Programmatically in the TetraConnect Hub. For access, see Access the TetraConnect Hub.

For more information, see the TetraScience AI Services documentation.

Other Release Notes

To view other TetraScience AI Services release notes, see TetraScience AI Services Release Notes.