Dagster vs Mage

Dagster is a data-aware orchestrator for building, running and monitoring data pipelines and assets. It models assets and dependencies to show lineage across a data platform, while supporting partitions, incremental runs, declarative workflows and fault-tolerant orchestration. An integrated catalog combines data discovery, lineage, workflows and operational metadata. Quality features include code-embedded validation, automated testing, freshness checks and observability tools. The integration directory lists Airbyte, Anthropic, Apache Airflow, AWS services and Azure Data Lake Storage. Dagster+ hybrid deployment keeps compute in a customer’s infrastructure while Dagster manages the control plane. The core framework is open source on GitHub. Platforms listed are API, Linux, self-hosted and web; deployment is hybrid. A 30-day trial is listed. Solo costs $10 a month plus usage charges, and Starter costs $100 a month plus usage charges. Dagster’s website says it is now part of Prefect.

Mage is a web platform for building and operating data workflows. Its listed functions include workflow scheduling, pipeline monitoring, data quality checks, and version control. Integrations cover Stripe, Salesforce, BigQuery, HubSpot, databases, SaaS APIs, cloud storage, files, and streams. Hybrid deployment is available. Mage has a free plan, while Starter costs $29 a month, billed monthly, plus $0.50 per CPU-hour. Starter includes one developer seat, one development environment, and 50 AI credits per month. Team costs $100 a month, billed monthly, plus $0.50 per CPU-hour; it supports up to 10 developer seats, includes one environment, and provides 250 shared AI credits per month. Mage is headquartered in Santa Clara, California, United States.