Model Store Overview

Vendor Name


Stand-alone vs. Platform

Standalone platform

Delivery Model

Open-source or managed

Clouds Supported

Cloud-native, available in your VPC on public cloud and/or Kubernetes.

Pricing Model


Service Level Guarantees

For enterprise plans


Community support on Slack, with additional channels and SLA for enterprise plans.


SSO for enterprise plans.
Integrates with cloud-native solutions for ACL.

Security and Compliance

Depends on deployment type, typically implemented by the customer as data stays in the customer cloud account.

Model Store Capabalities


For greenfield environment, we provide cookie-cutter deployment templates that can be deployed in ~1 click. For customers that need tighter integration with existing infra, it depends but there aren't too many moving pieces in Metaflow itself.

Flexibility, Speed, and Accessibility

Smooth integration with other tools is a core part of the value prop.
Battle-tested at scale at Netflix and many other companies.

Model Versioning, Lineage, and Packaging

Provides holistic versioning and reproducibility of your ML pipeline artifacts (not just models) and environments with dependencies.

Log and Display of Metadata

You can log and see logs in real-time; for experiment metadata, you can store it as Metaflow artifacts. You have a full record of runs, but Metaflow is not opinionated about the format or specific metrics and can be used with specialized experiment metadata tracking libraries.

Comparing Experiments and Models

Provides holistic versioning and reproducibility of your ML pipeline artifacts (not just models) and execution environments with dependencies. Visualization is up to you (or your favorite modality-specific library).

Organizing and Searching Experiments and Models

There is a hierarchy of flows/runs/steps with a full audit trail and reproducibility.
You can navigate through them in Python client or UI and use tags to organize them.

Model Review, Collaboration, and Sharing

You can implement your audit and review processes by using Metaflow tagging capabilities.
Artifacts from every run are recorded and are immutable.

CI/CD/CT Compatibility

Integration with Argo on K8S and Step Functions on AWS for production.
You can run your flows in fully local mode for testing inside your CI/CD system for testing.


The integration philosophy is very open so with most tools Metaflow doesn't get in the way; so far the focus has been on integrating with cloud-native modern data stack and notebooks.


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