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GitLab Package Registry vs ZenML

GitLab Package Registry

docs.gitlab.com

ZenML

zenml.io

Side by side

FigureGitLab Package RegistryZenML
Price from$2.42/mo$999/mo
Free planYesYes
Free trialYes—
Open source——
WebYesYes
Windows—Yes
macOS—Yes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published33
Spec score · Model registries8.7 (#6)8.4 (#9)
HeadquartersNo physical headquarters; GitLab is all-remoteMunich, Germany
PurposeThe registry lets users publish and share packages that downstream projects can consume as dependencies.ZenML orchestrates AI and machine-learning pipelines and agents written in Python.
SupportThe Premium plan includes Priority Support; GitLab states paid-plan support is 24/7 for Emergency severity and 24/5 for other impact levels.The Open Source plan includes community support, Scale includes priority support, and Enterprise includes dedicated support with an SLA.

Plans

GitLab Package Registry

  • FreeFree
  • Premium$29 a year
  • UltimatePriced on request

GitLab Package Registry pricing →

ZenML

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

ZenML pricing →

In their own words

GitLab Package Registry

GitLab Package Registry lets projects publish packages for downstream projects to use as dependencies. It can be private or public for supported package managers and is available on GitLab.com, GitLab Self-Managed, and GitLab Dedicated within Free, Premium, and Ultimate. Generally available formats include Generic packages, Helm, Maven, npm, NuGet, and PyPI; Composer and Conan are beta, while Debian, Go, and Ruby gems are experiments. Conda, CRAN, RPM, and Swift are not supported. A project can host multiple package types, including packages built from a monorepo. GitLab CI/CD can build or import packages, with jobs authenticating through CI_JOB_TOKEN, and the registry can be administered through an API. Project roles govern access; protection rules can restrict pushes or deletions for supported formats. Publication and deletion audit events are available when enabled, but require Premium or Ultimate. The dependency proxy is a beta feature on those paid tiers. GitLab is all-remote and has no physical headquarters.

Visit docs.gitlab.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