Model registry tools with immutable artifacts and CI policy gates

For ML model registries, the strongest candidates are Amazon SageMaker Model Registry, Databricks Unity Catalog Model Registry, and Snowflake Model Registry. If you're looking for a broader artifact registry that can store immutable artifacts, also consider JFrog Artifactory and Sonatype Nexus Repository.

The key distinction is that immutable versions, fine-grained access control, and automated CI policy gates are three separate capabilities. A registry may support the first two natively while requiring your CI/CD pipeline or a separate policy engine to enforce the third.

Best candidates to evaluate

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1. Amazon SageMaker Model Registry

(Best managed ML option)

• Immutable artifacts: Can lock registered model versions against updates using IAM and resource-tag conditions; artifact storage access can also be restricted.
• Fine-grained access: IAM policies, model package groups, and cross-account permissions.
• CI policy gates: Model approval status and automated CI/CD workflows support controlled promotion to production.

Strong choice for AWS-native ML platforms.docs.aws.amazon.com+2

Docs: Model Registry ·Prevent model updates

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2. Databricks Unity Catalog Model Registry

• Version control: Versioned registered models with lifecycle management and lineage.
• Fine-grained access: Privileges such as EXECUTE, CREATE MODEL VERSION, and MANAGE, with centralized governance.
• CI policy gates: Integrates with Databricks workflows and external CI/CD; enforce evaluation and approval rules in your pipeline.

Best for teams already using Databricks and MLflow. Treat immutability as a policy to enforce and validate rather than assuming every version is inherently write-once.docs.databricks.com+2

Docs:Model lifecycle

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3. JFrog Artifactory

• Immutable artifacts: Supports release protection by restricting overwrite and delete permissions; configure repository policies for stronger immutability.
• Fine-grained access: Repository- and project-level permission targets and RBAC.
• CI policy gates: Pairs well with JFrog Xray and CI integrations for security and compliance checks before promotion.

Best for organizations managing model files alongside containers, packages, binaries, and other build artifacts. Validate which immutability and policy features are included in your edition.docs.jfrog.com+1

Docs:JFrog permissions

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4. Snowflake Model Registry

• Version management: Supports model versions and controlled lifecycle updates.
• Fine-grained access: Model-level OWNERSHIP, USAGE, and READ privileges.
• CI policy gates: Use external CI/CD checks to validate models and control deployment or promotion.

Best when models and ML workflows already live in Snowflake. Verify write-once guarantees separately; versioning alone does not establish immutability.docs.snowflake.com

Docs:Model management

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5. Oracle Cloud Infrastructure Artifact Registry

• Immutable artifacts: Explicit immutable repository setting prevents replacement of uploaded artifacts.
• Fine-grained access: IAM policies scoped to artifact types and compartments.
• CI policy gates: Add CI checks and promotion rules through your pipeline; policy gates are not a complete built-in ML approval workflow.

A particularly clear fit if immutable generic artifacts are a hard requirement, although it is not a dedicated ML model registry.docs.oracle.com+2

Docs: Create a repository ·IAM policies

How they compare

My recommendation

• For ML model governance: Start with SageMaker or Databricks, depending on your cloud and ML platform.
• For a universal artifact registry: Start with JFrog Artifactory.
• For explicit artifact immutability: Evaluate OCI Artifact Registry alongside your existing CI/CD system.

For any of these, a robust CI policy gate should verify the artifact digest, provenance, vulnerability scan results, model evaluation thresholds, and required approvals before allowing production promotion.

One important qualification: none of the options should be assumed to provide all three requirements automatically in every configuration. Confirm the exact version, edition, and enforcement mechanism before selecting a platform.

To fulfill requirements for immutable artifacts, fine-grained access control (FGAC) , and automated CI policy gates , you need an MLOps architecture that treats model files like cryptographically secure, version-controlled software packages rather than arbitrary storage blobs.ultralytics.com

The top enterprise-grade model registries and how they address these three strict requirements include the following options:

1. Top Enterprise Solutions

• • Amazon SageMaker Model Registry: Built natively on AWS IAM and KMS. Artifacts stored in S3 are immutable by default per version. Offers granular access control via resource-level IAM policies and explicit approval statuses (Approved, Rejected, PendingManualApproval ) that block automated deployment pipelines if unfulfilled. Integrates deeply into CI/CD using AWS Step Functions or Amazon SageMaker Pipelines to evaluate automated evaluation thresholds before setting package status.docs.aws.amazon.com+1
• • Databricks Unity Catalog / MLflow: Modern MLflow relies on Aliases and Tags coupled with Unity Catalog for governance. Provides strict catalog-schema-model level ACLs (fine-grained access control down to read/write/manage privileges per model lineage). Policy gates can be enforced programmatically via webhooks or Databricks Asset Bundles in CI/CD that reject model promotion if validation metrics (such as bias checks or accuracy floors) fail.cloudai.pt
• • JFrog Artifactory (with ML Model Management): Treats models like standard binary artifacts (OCI compliant or custom layouts). Exceptional at immutability (enforcing non-overwritable repository rules). Leverages enterprise RBAC/ABAC integrated with LDAP/OIDC and Xray scanning to run automated vulnerability and policy compliance gates inside the CI build pipeline before a model is marked as released.
• • Vertex AI Model Registry (Google Cloud): Integrated with Google Cloud IAM for granular access control on individual model resources. Model versions are immutable once written to storage, and deployment can be programmatically gated using Cloud Build and Vertex AI Evaluation APIs to run automated quality/safety checks prior to promoting a version alias to production.

2. How the Requirements Map to Implementation

Immutable Artifacts

• The Mechanism: Registered model files (weights, tokenizers, config, evaluation reports) are written to a version-locked object store (e.g., S3/GCS bucket with Object Lock or versioning enabled).
• Best Practice: Never allow in-place overwrites of a version tag. If a model changes, a new hash-addressed version or semantic version (v1.2.0 ) must be stamped.ultralytics.com

Fine-Grained Access Control (FGAC)

• The Mechanism: Access is restricted via attribute- or role-based policies (RBAC/ABAC).
• Best Practice: Data scientists have CanWrite/CanRegister on experimental namespaces, but only automated CI service principals or designated Risk/Security groups hold CanApprove or CanPromote privileges to production environments.trustlogix.ai

Automated CI Policy Gates

• The Mechanism: The CI pipeline (GitHub Actions, GitLab CI, or Cloud Build) executes regression, latency, bias, and security scans on the candidate model artifact.
• The Gate: If the evaluation script returns a passing exit code, the CI script invokes the registry API to attach a passing tag/alias or update the approval status. If it fails, the pipeline exits, and the model remains un-promotable.cloudai.pt

To help narrow down the ideal platform, let me know:

• Are you operating primarily within a specific cloud provider (AWS, GCP, Azure) or looking for a multi-cloud/on-prem agnostic tool?
• Do you prefer an open-source core (like MLflow) or an enterprise commercial platform?

For your requirements, I’d start by evaluating Weights & Biases (W&B) Registry and Databricks Unity Catalog with MLflow. Both support model versioning, governance, and CI/CD workflows, but the best fit depends on whether you want an integrated MLOps platform or a registry with stronger infrastructure-level artifact controls.

The key distinction is that immutable artifacts, fine-grained permissions, and automated policy gates are three separate capabilities. A registry may version models and enforce access control without natively blocking promotion or deployment when a policy fails.

Recommended options

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1. Weights & Biases Registry

(Best starting point)

W&B provides versioned artifacts, lineage, organization-level access controls, and automation for model CI/CD. Its registry is designed to share approved model versions across teams and connect them to downstream workflows.docs.wandb.ai+1

• Immutable artifacts: Version-specific artifacts and lineage.
• Access control: Permissions for registry artifacts and collections.
• CI/CD: Webhooks and downstream automation.
• Policy gates: Verify how your required approval rules and failed-check blocking are implemented; these may require pipeline logic.

Docs:W&B Registry documentation

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2. Databricks Unity Catalog

A strong choice for enterprise governance. Unity Catalog integrates with MLflow Model Registry to provide fine-grained permissions, cross-workspace sharing, and model lineage.www.mlflow.org+1

• Immutable artifacts: Versioned models; validate storage-level immutability requirements separately.
• Access control: Fine-grained model permissions.
• CI/CD: Integrates with environment-specific model promotion workflows.
• Policy gates: Implement required evaluations and approval checks in CI/CD before production promotion.

Docs: Unity Catalog documentation ·MLflow Model Registry

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3. Harbor

Consider Harbor if immutable, digest-addressed artifacts and registry-level controls are more important than built-in ML experiment management. Harbor supports configurable tag-immutability rules and project-level artifact management.goharbor.io

• Immutable artifacts: Tag-immutability rules prevent overwriting protected tags.
• Access control: Project-level permissions.
• CI/CD: Fits container-oriented build and deployment pipelines.
• Policy gates: Add signing, scanning, and promotion checks through compatible tools and pipeline enforcement.

Docs:Harbor documentation

How I’d implement your requirements

Regardless of which registry you choose, I’d make the CI pipeline enforce a promotion contract like this:

1. Register immutable model artifact

Unique version or digest, provenance, source commit

2. Run automated policy gates

Security scan, evaluation thresholds, license and provenance checks

Pass

Approve and promote the exact digest

Fail

Block promotion and record the failure

Enforce least-privilege permissions so developers can register candidates but only authorized service identities or approvers can promote them. Make production deployments consume the exact artifact digest that passed validation, rather than a mutable tag such as latest.

Which should you choose?

• W&B Registry: Best initial evaluation if you want model lifecycle management, artifact lineage, and ML-focused workflow automation.
• Databricks Unity Catalog: Best if centralized enterprise governance and fine-grained access controls are your primary concerns.
• Harbor: Best if immutable artifact distribution is central to your architecture and you're comfortable assembling the ML-specific governance and policy pipeline.

One important caveat: I would not treat any of these as satisfying all three requirements until you verify the exact immutability guarantees and confirm that failed policy checks cannot be bypassed.

One question to narrow this down: Are you looking for a dedicated ML model registry, or a general-purpose artifact registry that also stores models?