Kestra vs Luigi

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.

Luigi is a free Python package for building and coordinating pipelines of batch jobs. It manages dependencies, workflow execution, visualization, failure handling and command-line integration. The documentation presents it as workflow plumbing for long-running work such as Hadoop jobs, database transfers and machine-learning jobs, including pipelines with thousands of tasks that can take days or weeks. Documented systems include Hadoop, Hive, Pig, Spark, HDFS, databases and local files. A Luigi server provides a web interface for searching and filtering tasks, while its HDFS and local filesystem abstractions support atomic file operations. The project instructs users to install the latest stable version with `pip install luigi`; TOML configuration is available through the optional `luigi[toml]` installation. The repository says Python 3.10 through 3.14 are tested and identifies Apache-2.0 as the license. Luigi is self-hosted, and Spotify's Data Team maintains the project. A mailing list is linked for discussion and questions. Its security policy directs people to report sensitive issues through Spotify's bug bounty program rather than GitHub.