Databricks Workflows vs Kestra

Databricks Workflows has no write-up on Specifiction yet; its figures are above.

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.