Kestra vs Mage

Kestra is a declarative orchestration platform for building, running, and governing workflows across an organization. Workflows, called flows, are defined in YAML and can combine tasks, dependencies, conditions, triggers, and runtime parameters. Execution controls include retries, failure policies, timeouts, concurrency limits, worker assignment, and log capture. Kestra advertises more than 2,000 plugins for connecting to external tools, with examples including Airbyte, AWS, Azure, BigQuery, dbt, Docker, GitHub, Kafka, Kubernetes, Slack, Snowflake, and Terraform. A flow can orchestrate SQL, Python, shell, APIs, Terraform, dbt, Spark, and custom tools. The platform includes an MCP server for AI agents and supports exposing flows as callable tools. Teams can self-host with Docker or Kubernetes; Enterprise Edition also supports cloud, on-premises, and air-gapped environments. Setup instructions cover macOS, Windows, and Linux, alongside web and API platforms. Kestra Cloud advertises up to two months free during team setup. The Open Source and Enterprise Edition plans have no price listed.

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.