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Metaflow vs ZenML

Metaflow

metaflow.org

ZenML

zenml.io

Side by side

FigureMetaflowZenML
Price fromFree$999/mo
Free planYesYes
Free trial——
Open sourceYes—
WebYesYes
Windows—Yes
macOSYesYes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published13
Spec score · ML Experiment Tracking Software8.7 (#4)7.8 (#11)
Spec score · MLOps Platforms8.6 (#4)7.8 (#7)
Data handlingMetaflow includes patterns for moving data through workflows and accessing warehouses and data lakes.ZenML says it stores metadata while data, artifacts, and compute stay in the customer's infrastructure or VPC.
DeploymentWorkflows can be deployed to production with one command and can react to events.ZenML supports local, SaaS, and self-hosted deployment, including Docker and Kubernetes Helm deployment methods.
IntegrationsDocumented integrations include AWS Batch, AWS Step Functions, Kubernetes, Argo Workflows, and Apache Airflow.The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines.
SupportThe documentation points users to the Metaflow Slack community for help.The Open Source plan includes community support, Scale includes priority support, and Enterprise includes dedicated support with an SLA.

Plans

Metaflow

  • Open-source MetaflowFree

Metaflow pricing →

ZenML

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

ZenML pricing →

In their own words

Metaflow

Metaflow is a free, open-source Python library for developing, deploying, and operating data-intensive data science, machine learning, and AI applications. It automatically tracks flows, experiments, and artifacts, and supports local workflow construction and testing. For larger workloads, it can use compute in a customer’s cloud account or Kubernetes clusters. Documented integrations include AWS Batch, AWS Step Functions, Kubernetes, Argo Workflows, and Apache Airflow. Deployments can use AWS, Azure, Google Cloud, Kubernetes, or on-premises clusters; artifact storage options include AWS S3, Azure Blob Storage, and Google Cloud Storage. The Python package runs on macOS and Linux, while Metaflow Sandbox offers free browser access to cloud and Kubernetes stacks. The project is licensed under Apache License 2.0, and its own-cloud model can use existing infrastructure and security policies. A local-only setup does not support external compute scaling or production-grade deployments. Metaflow is aimed at data scientists and ML or AI engineers; its documentation says it offers little benefit for small notebook experiments without scaling, criticality, or complexity needs.

Visit metaflow.org

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