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SPECIFICTION
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DagsHub vs ZenML

DagsHub

dagshub.com

ZenML

zenml.io

Side by side

FigureDagsHubZenML
Price from$8.25/mo$999/mo
Free planYesYes
Free trialYes—
Open source——
WebYesYes
WindowsYesYes
macOSYesYes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published53
Spec score · ML Experiment Tracking Software9.4 (#2)7.8 (#11)
Spec score · Model Registry Software9.3 (#2)7.8 (#10)
Spec score · Model registries9.4 (#4)8.4 (#9)
IntegrationsDocumented integrations include GitHub, GitLab, Bitbucket, DVC, MLflow, Label Studio, Jenkins, Slack, Discord, New Relic and Amazon S3.The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines.

Plans

DagsHub

  • IndividualFree
  • IndividualFree
  • Team$99 a year
  • Team$119 a month
  • EnterprisePriced on request

DagsHub pricing →

ZenML

  • Open SourceFree
  • Scale$999 a month
  • EnterprisePriced on request

ZenML pricing →

In their own words

DagsHub

DagsHub helps developers and teams manage machine-learning projects, from data collection and dataset curation through annotation, experimentation, and model management. A project workspace can hold code, data, experiments, models, pipelines, and annotations together. The platform is designed for text, images, audio, video, documents, medical imaging, and binary files. Experiment tracking supports run comparison, metric and parameter visualization, real-time progress monitoring, and reproducibility. Data Engine provides curation, visualization, quality review, lineage, hard-example mining, labeling, auto-labeling, and data streaming. Official integrations include tools such as GitHub, Google Colab, DVC, MLflow, Jenkins, Label Studio, Google Drive, Weights & Biases, and Hugging Face; hosted projects also have support for the MLflow and Label Studio APIs. DagsHub hosts data but does not provide compute, so projects rely on local, cloud, or edge infrastructure. Enterprise installations can run on-premises, in a VPC, in air-gapped environments, and in OpenShift environments. The Individual plan is free, with stated private-repository limits; paid pricing starts at $99/user/mo billed annually, and a 14-day trial is available.

Visit dagshub.com

ZenML

ZenML orchestrates AI and machine-learning pipelines and agents written in Python. Pipelines can run on Kubernetes, Vertex AI, SageMaker, AzureML, or a laptop without rewriting code. ZenML versions step outputs such as models and evaluation datasets, supports re-execution from any step, and reuses cached results when code and inputs have not changed. Deployment choices include local, SaaS, and self-hosted setups, with Docker and Kubernetes Helm methods. ZenML says it stores metadata while data, artifacts, and compute remain in the customer's infrastructure or VPC. Its integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. The self-hosted Open Source plan is free and includes unlimited executions and projects. Scale costs $999 a month and includes 2,000 executions, three projects, and five snapshots. Enterprise has custom pricing and adds SSO, custom-role RBAC, audit logs, and air-gapped deployment. ZenML says it is SOC 2 Type II and ISO 27001 certified.

Visit zenml.io