Alternatives to GitLab Model Registry

DagsHub

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

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.

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

Hopsworks AI Lakehouse

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

DataRobot

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

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

ZenML

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

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.

C3 AI Demand Planning

This application suits demand planners and business leaders coordinating finance, operations, and compliance teams around forecasts.

Red Hat OpenShift Pipelines

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

Polyaxon

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

Vertex AI Vector Search

It suits teams building semantic or hybrid search, recommendation, retrieval-augmented generation, chatbot, and multimodal applications on Google Cloud.

Snowflake Feature Store

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

It is intended for developers, architects, and technically oriented business teams building enterprise AI solutions across SAP and third-party systems.

Azure Machine Learning

Azure Machine Learning suits teams needing web-based forecasting, evaluation, automated learning or model deployment.