Mage vs Prefect
On the evidence we track, Prefect leads this comparison with a composite score of 70/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for data engineering tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
Mage
A system for constructing data processing pipelines that validates, schedules, and executes them in production environments. Supports both cloud-managed and self-hosted deployment models.
Prefect
LeaderA workflow orchestration platform that enables teams to build resilient automated processes using Python, with features like automatic retries, state tracking, and run observability. Supports flexible deployment options ranging from managed cloud infrastructure to self-hosted environments on Kubernetes or user-provided compute.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Mage
From $100/month + usage for cloud, custom pricing for private deployments
- Mage Cloud$100/month + usage ($0.50 per cpu-hour, $0.50 per 4GB-hour)
- Workflow orchestration and scheduling
- Pipeline monitoring and observability
- Data validation and quality checks
- Unlimited users
- Development environment included
- Mage ProContact sales
- Hybrid cloud deployment
- Private cloud deployment
- On-premises deployment
- Full data isolation
- Custom infrastructure configuration
- +1 more
Prefect
LeaderFree tier. Starter from $100/mo. Team at $100/user/mo. Enterprise custom pricing.
- HobbyFree
- workflow scheduling
- workflow observability
- logging & alerting
- Starter$100/mo
- bring your own compute
- webhooks
- API access
- Team$100/user/mo
- service accounts
- audit logging
- team collaboration
- EnterpriseContact sales
- SSO (SAML/OIDC)
- RBAC with object-level ACLs
- multiple workspaces
- directory sync (SCIM)
- IP allowlisting
- +1 more
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
Mage
- Apache Spark
- PySpark
- Kafka
- Stripe
- Salesforce
- BigQuery
- AWS EMR
- Kubernetes
- EHR systems
- Billing platforms
- Lab systems
- Clinical systems
- MySQL
Prefect
Leader- Pydantic AI
- OpenAI
- AWS Secrets Manager
- HashiCorp Vault
- dbt
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.