What's the best model registry for tracking model lineage across training runs?

There isn’t a single “best” model registry for everyone, but if your main goal is tracking model lineage across training runs, the strongest choice depends on how much you want around the registry besides lineage.

Best overall for lineage tracking

MLflow Model Registry
Why it stands out:

• Strong integration with training runs via MLflow Tracking
• Clear linkage between:
  • code/version
  • parameters
  • metrics
  • artifacts
  • registered model versions
• Easy to trace a deployed model back to the exact run that produced it
• Works well in many stacks and is widely adopted

If lineage is the priority, MLflow is often the most practical default.

Best if you want enterprise governance

Weights & Biases Model Registry

• Very good experiment tracking
• Strong lineage and artifact graph
• Nice UI for auditing model history
• Good if your team already uses W&B for experiments

Best for large-scale cloud-native MLOps

Google Vertex AI Model Registry

• Good integration with Vertex AI pipelines, training, and deployment
• Strong cloud governance and metadata handling
• Best if you’re already on GCP

Best for AWS-centric teams

SageMaker Model Registry

• Tight integration with SageMaker training/pipelines
• Good lineage within AWS workflows
• Best if your infrastructure is already in AWS

Best for end-to-end governance/metadata

DataHub or OpenMetadata with ML metadata

• Better as a metadata platform than a pure model registry
• Useful if you want lineage across datasets, pipelines, features, and models
• More setup, but stronger cross-system lineage

My recommendation

• If you want the best general-purpose answer: MLflow
• If you want the best polished UI and experiment-to-model lineage: Weights & Biases
• If you want cloud-native registry + deployment: use your cloud provider’s registry

Quick rule of thumb

Choose a registry that can store and link:

1. training run ID
2. code commit hash
3. dataset/version
4. parameters and metrics
5. artifact location
6. model version and stage

If you want, I can also give you a side-by-side comparison table of MLflow, W&B, SageMaker, Vertex AI, and Azure ML specifically for lineage and auditability.