Metaflow vs mlop
Metaflow
metaflow.org
mlop
mlop.aiSide by side
| Figure | Metaflow | mlop |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · ML Experiment Tracking Software | 8.7 (#4) | 7.5 (#18) |
| Intended users | Metaflow is aimed at data scientists and ML/AI engineers; its documentation says it offers little benefit for small notebook experiments with no scaling, criticality, or complexity needs. | The documentation describes mlop as a tool for people who train machine learning models and says its free tier is for individual developers and small teams. |
| Support | The documentation points users to the Metaflow Slack community for help. | The pricing page includes email support for Pro and 24/7 support from founders for Enterprise. |
Plans
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.