Canonical MLOps vs ZenML
Canonical MLOps
canonical.com
ZenML
zenml.ioSide by side
| Figure | Canonical MLOps | ZenML |
|---|---|---|
| Price from | — | $999/mo |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · MLOps Platforms | 7.6 (#11) | 7.8 (#7) |
| Deployment | Charmed MLflow can run on a workstation, CNCF-conformant Kubernetes, or a public cloud. | ZenML supports local, SaaS, and self-hosted deployment, including Docker and Kubernetes Helm deployment methods. |
| Headquarters | London, United Kingdom | Munich, Germany |
| Integrations | Canonical lists Jupyter, Grafana, and Prometheus as tools to use with Charmed MLflow. | The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. |
| Purpose | Canonical MLOps is an open source platform for moving machine learning models from experimentation to production across the ML lifecycle. | ZenML orchestrates AI and machine-learning pipelines and agents written in Python. |
| Security | Canonical says its MLOps components receive up to 15 years of security maintenance, regular updates, and CVE fixes. | ZenML states that it is SOC 2 Type II and ISO 27001 certified. |
| Support | Optional 24/7 support with SLAs is available for Canonical’s MLOps stack. | The Open Source plan includes community support, Scale includes priority support, and Enterprise includes dedicated support with an SLA. |
Plans
In their own words
Canonical MLOps
Canonical MLOps has no write-up on Specifiction yet; its figures are above.
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.