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Metaflow

Free#15 of 38 in ML Experiment Tracking SoftwareDataOps Platforms MLOps Platforms

Metaflow: A Python-first workflow platform that includes experiment tracking without narrowing its focus to it. Ranked #15 of 38 in ML Experiment Tracking Software by our editors (7.4/10); pricing: Free plan; best for python teams combining workflows and tracking.

7.4/10Editor score

Visit Metaflow

Metaflow7.4 Visit Metaflow

At a glance

Editor score
7.4 / 10
Pricing
Free plan
Best for
Python teams combining workflows and tracking
Free plan
Yes
Paid from
Free
Facts checked
21 Sep 2026

Where it wins
- Automatic tracking covers flows, experiments, and artifacts.
- Combines tracking with orchestration, versioning, and deployment.
- Runs locally or across cloud, CPU, GPU, and Kubernetes environments.
Where it doesn't
- Broader workflow scope may be more than tracking-only teams need.
- Python-based authoring is central to the development experience.
- Production infrastructure runs in your cloud account or Kubernetes environment.

Our verdict on Metaflow

Metaflow is an open-source Python library for developing, scaling, deploying, and operating data science, machine-learning, and AI applications. It is intended for Python teams that need experiment tracking alongside workflow authoring, data access, production deployment, and cloud execution. The platform automatically tracks flows, experiments, and artifacts, while also supporting experiment result visualization and comparison. A free plan and open-source distribution make it suitable for teams that want to build around their own environments rather than adopt a separate hosted tracking product.

Its strongest distinction is the connection between experiment tracking and workflow operations. Teams can define workflows in Python, run them locally or on cloud infrastructure, version data and artifacts, and deploy workflows to production orchestrators. Event-triggered workflows, scalable CPU and GPU compute, and integrations with AWS Batch, AWS Step Functions, Kubernetes, Argo Workflows, and Apache Airflow extend the product beyond experiment records. Storage integrations include AWS S3, Azure Blob Storage, and Google Cloud Storage, with support for AWS, Azure, and Google Cloud environments.

That breadth is also the main trade-off. Metaflow suits teams that want one Python-centered framework for development, tracking, orchestration, and deployment. It is less suitable for organizations seeking a narrowly focused experiment-tracking system, or for teams that do not want production infrastructure managed in their own cloud account or Kubernetes environment. Commercial support is provided by Outerbounds, while the open-source framework remains the foundation for local and cloud workflows.

Your product? Our editors can test Metaflow in depth and publish a review, labelled as paid · from $149/mo →

Metaflow pricing

Plans Free planFree Free to use — no paid tier required for the core job.

See plans on metaflow.org

Metaflow fact sheet

Free plan	Yes
Paid from	Free
Run comparison	Not verified
Artifact tracking	Not verified
Dataset versioning	Not verified
Model registry	Not verified
Deployment options	Not verified
API and SDK access	Not verified
Deployment	Cloud, Self-hosted
Platforms	macOS, Linux
Support	Community, Docs
Built for	Solo, Small business, Mid-market, Enterprise (editorial estimate)
Integrations	11 integrations: AWS, Azure, Google Cloud, AWS Batch, AWS Step Functions, Kubernetes …
Pricing	Free plan
Website	metaflow.org
Facts checked	21 Sep 2026

Metaflow integrations

Metaflow lists 11 integrations on its own site.

AWS
Azure
Google Cloud
AWS Batch
AWS Step Functions
Kubernetes
Argo Workflows
Apache Airflow
AWS S3
Azure Blob Storage
Google Cloud Storage

Alternatives to Metaflow

See all Metaflow alternatives →

Also Ranked In

Metaflow is ranked in 3 of our directories.

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Featured on iTechGuides

Metaflow is listed in our ML Experiment Tracking Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update