Amazon SageMaker Autopilot vs ZenML
Amazon SageMaker Autopilot
aws.amazon.com
ZenML
zenml.ioSide by side
| Figure | Amazon SageMaker Autopilot | ZenML |
|---|---|---|
| Price from | Free | $999/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 5 | 3 |
| Spec score · Model registries | 8.7 (#7) | 8.4 (#9) |
| Deployment | After training, users can deploy Autopilot models to real-time endpoints or run batch inference jobs. | ZenML supports local, SaaS, and self-hosted deployment, including Docker and Kubernetes Helm deployment methods. |
| Headquarters | Seattle, Washington, United States | Munich, Germany |
| Integrations | Reviewer evidence describes integrations with Amazon S3, IAM, CloudWatch and ECR, while AWS documents Studio and Canvas integration. | The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. |
| Purpose | Autopilot automatically creates machine learning models with full visibility and is now in SageMaker Canvas. | ZenML orchestrates AI and machine-learning pipelines and agents written in Python. |
| Security | AWS describes its cloud security approach as combining security of the cloud infrastructure with security in the cloud through customer controls. | ZenML states that it is SOC 2 Type II and ISO 27001 certified. |
| Support | AWS provides support plans with proactive guidance, issue resolution, and tools. | The Open Source plan includes community support, Scale includes priority support, and Enterprise includes dedicated support with an SLA. |
Plans
Amazon SageMaker Autopilot
- On-demandPriced on request
- Savings PlansPriced on request
- Amazon SageMaker AI pay-as-you-goFree
- SageMaker AI Free TierFree
- On-demand SageMaker AIPrice not published
In their own words
Amazon SageMaker Autopilot
Amazon SageMaker Autopilot 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.