---
title: Feast feature store | MLOps
description: Enterprise-ready Feast feature store for ML pipelines. Charmed Feast
  integrates with Kubeflow and simplifies feature management.
url: https://canonical.com/mlops/feast?format=md
---

# Best in class feature store, integrated into your MLOps stack

Open source. Fully supported. Built for scale.

Supported with Ubuntu Pro

Charmed Feast is Canonical's securely designed, production-ready feature store for machine learning. Deploy it in minutes alongside Charmed Kubeflow, and manage features across environments with confidence.

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[Install Charmed Feast](https://documentation.ubuntu.com/charmed-feast/)
[Contact us](https://canonical.com/mlops/contact-us)

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## All the features of upstream Feast

Charmed Feast delivers all the features of upstream Feast that you know and love, including:

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* Centralized feature registry
* Offline and online stores for consistent training and serving
* Real-time and batch feature ingestion
* Versioning and reproducibility for ML pipelines

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[Learn more about Feast](https://documentation.ubuntu.com/charmed-feast/)

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## Trusted, supported, production-ready

Charmed Feast is an enterprise-grade feature store. In addition to upstream features, it also includes long-term security maintenance and expert support from Canonical.

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Part of the [Ubuntu Pro](https://ubuntu.com/pro) subscription

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Regular updates and CVE patches

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Optional 24/7 enterprise-grade support with SLAs

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[Discover Enterprise Support](https://ubuntu.com/pro)

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## Operationalized and portable by design

Built as a [Juju operator](https://canonical.com/juju), Charmed Feast brings a new level of simplicity and flexibility to your feature store with easy deployment and scaling.

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* Run it anywhere: cloud, hybrid, on-prem
* Integrate with [Charmed Kubeflow](https://canonical.com/mlops/kubeflow/what-is-kubeflow) out of the box
* Seamlessly plug into Feast from Charmed Kubeflow Jupyter Notebooks

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[Deploy with Juju](https://documentation.ubuntu.com/charmed-feast/)

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## Use Charmed Feast with

[---

Deploy Jupyter Ui using Charmhub](https://charmhub.io/jupyter-ui)

[---

Learn more about Apache Spark on Kubernetes](https://canonical.com/data/spark)

[---

Learn more about Kubeflow](https://canonical.com/mlops/kubeflow/what-is-kubeflow)

[---

Deploy Grafana using Charmhub](https://charmhub.io/grafana-k8s)

[---

Deploy Prometheus using Charmhub](https://charmhub.io/prometheus-k8s)

[---

Learn more about Juju](https://canonical.com/juju)

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Feast integrates natively with Charmed Kubeflow and other [Canonical's MLOps tools](https://ubuntu.com/ai).

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## Resources

[Guide to MLOps](https://ubuntu.com/engage/mlops-guide)

Take your models to production using open source MLOps platforms.

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[Build your machine learning pipeline with Kubeflow](https://ubuntu.com/engage/build-ml-pipeline-kubeflow)

Watch the webinar to learn how to build ML pipelines using Kubeflow.

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## [Start your machine learning journey with open source ›](https://canonical.com/mlops/contact-us)

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### Talk to our MLOps experts

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Looking to scale your MLOps infrastructure or need consulting services to kick start your AI journey? Our experts are here to help you.