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STACKIT AI Model Experiments

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STACKIT AI Model Experiments delivers a fully managed MLflow environment to track training runs, record metrics, and manage model registries without infrastructure overhead.

Service Overview

Section titled “Service Overview”
  • Central MLOps Hub: Eliminates fragmented local tracking setups by centralizing artifacts, prompts, and parameters.
  • Seamless Integrations: Natively compatible with standard frameworks like PyTorch, TensorFlow, and LangChain.
  • Sovereign Execution: Hosted in German data centers with full GDPR compliance and encrypted TLS connections.

Technical Details

Section titled “Technical Details”
  • Architecture: Managed MLflow service using STACKIT IAM/SSO for web UI access and token-based SDK/API authentication.
  • Storage Separation: Metadata resides in a high-availability database; heavy model artifacts are stored in STACKIT Object Storage.
  • GenAI Tracing: Full support for tracing LLM execution steps, prompts, and evaluation metrics.

Storage and Access

Section titled “Storage and Access”

The values below come from the STACKIT documentation and update themselves.

From the STACKIT docsArchitecture of AI Model Experiments › Artifact storageSource updated 07.04.2026 · copied 05.10.2026

Unlike the metadata, artifacts (models, images, large datasets) are stored within the user’s own STACKIT project space.

  • Sovereignty: You retain ownership and control over your binary data.

  • Access: The MLflow™ UI and SDK communicate directly with your storage bucket to upload and retrieve these files.

What is this?

This section is copied from the STACKIT docs automatically, several times a day. It cannot be changed here. Changes belong in the STACKIT docs.

From the STACKIT docsArchitecture of AI Model Experiments › Authentication & securitySource updated 07.04.2026 · copied 05.10.2026

Security is managed through a combination of STACKIT permissions and application-level tokens:

  • Management: Admins create and manage the instances and access tokens and set access roles via the STACKIT Portal or API.

  • Logging data: Engineers use tokens to authenticate the Python SDK against the specific instance URI to log their training data.

  • UI access: Each instance UI is accessible via a unique URI. Access is restricted via the STACKIT authentication and the roles of the user.

What is this?

This section is copied from the STACKIT docs automatically, several times a day. It cannot be changed here. Changes belong in the STACKIT docs.

Limitations & Constraints

Section titled “Limitations & Constraints”
  • Coarse Token Scoping: Service tokens offer full workspace access; fine-grained read-only API roles are unavailable.
  • Project Scope: Tracking instances and linked object storage buckets are strictly bound to a single STACKIT project.
  • No Native In-UI Backup: Soft-deleted experiments are retained for 30 days before purge; point-in-time database restoration is not exposed in the UI.
STACKIT documentation docs.stackit.cloud STACKIT AI Model Experiments Documentation Open the documentation
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Maintainers
  • ?Name not public?Name not publicThe Cloud Framework team knows who this is. The name is not shown on the site.
TMTobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updatesSTACKITwww.linkedin.com/in/tobias-müller-011304172??Name not publicThe Cloud Framework team knows who this is. The name is not shown on the site.Contributed in STACKIT

  • Tobias M.Tobias M.Head of STACKIT Cloud Framework · STACKITOwnerActive 12 of the last 12 weeks · 168 updatesSTACKITwww.linkedin.com/in/tobias-müller-011304172 · Oct 5, 2026

  • Name not public?Name not publicThe Cloud Framework team knows who this is. The name is not shown on the site. · Sep 9, 2026