Best Model Registries in 2026

DagsHub

DagsHub suits ML developers and teams that need shared data, experiment, and model management.

It suits teams that need a common interface for model access, routing, usage monitoring, and controls.

GitLab Package Registry

The registry suits teams publishing package dependencies alongside GitLab projects, including teams using GitLab CI/CD or multiple package types in a monorepo.

ClearML

ClearML suits teams managing AI/ML work across GPU infrastructure and model development, especially those needing cloud, on-premises, hybrid, or self-hosted deployment.

ZenML

The Open Source plan is described for individuals and small teams; Scale targets teams running ML in production.

CoCo suits Snowflake customers doing data engineering, analytics, machine learning, or agent-building work who want coding assistance in Snowsight, a desktop environment, or a CLI.

Hopsworks AI Lakehouse

It suits developers, data scientists, platform engineers, security engineers, and administrators building AI systems around lakehouse data.

Jozu describes Hub for enterprises, regulated industries and governments, including teams that operate in air-gapped environments.

The registry suits solo data scientists and larger machine-learning platform teams that need model versioning and lifecycle workflows.

MLRun

MLRun provides a shared technology stack for data engineers, data scientists, and machine-learning engineers.

DataRobot

DataRobot is aimed at global enterprises, including regulated organizations in manufacturing, public sector, life sciences, insurance, financial services, and other listed sectors.

Polyaxon

It suits AI and machine-learning teams running workloads on Kubernetes that need experiment tracking, orchestration and observability.

It suits analysts and programmers building statistical models collaboratively on SAS Viya.

Red Hat OpenShift Pipelines

It suits teams delivering applications on OpenShift, particularly decentralized teams working with microservice-based architectures.

It is aimed especially at Oracle customers developing Java, SuiteScript, PL/SQL, or OCI applications.

Snowflake Feature Store

It suits data-science and machine-learning teams already working in Snowflake who need governed feature creation, lineage, and serving.

Domino positions this platform for large AI-driven enterprises and regulated industries running mission-critical AI.