Guild AI vs Metaflow
Guild AI
guildai.orgMetaflow
metaflow.orgSide by side
| Figure | Guild AI | Metaflow |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 0 | 1 |
| Spec score · ML Experiment Tracking Software | 7.6 (#14) | 8.7 (#4) |
| Installation | The getting-started guide says to install Guild AI with pip install guildai. | The package is installed with pip, and conda-forge installation is also documented. |
| Integrations | The documentation describes TensorBoard support and integrations involving TensorFlow and Keras. | Documented integrations include AWS Batch, AWS Step Functions, Kubernetes, Argo Workflows, and Apache Airflow. |
| Support | The Guild AI forum says its support systems are moving to GitHub and that the forum will be read-only. | The documentation points users to the Metaflow Slack community for help. |
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.