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 defining and coordinating batch-job pipelines, dependencies, workflow execution, and failures. Workflows and dependency graphs are specified in Python, and tasks can also trigger work outside Python. Its documentation describes task support for Hadoop, Hive, Pig, and Spark, plus HDFS and local file abstractions. Those abstractions make file operations atomic, helping avoid pipelines being left with partial data. The Luigi server includes a web interface for searching and filtering tasks and viewing dependency graphs. It also supports command-line integration and use from Python code. The documentation describes pipelines with thousands of tasks that may run for days or weeks, including Hadoop jobs, database transfers, and machine-learning algorithms. The package is self-hosted, with no registration wall. Install the latest stable version with `pip install luigi`; adding `[toml]` enables TOML-based configuration. Documentation lists Python 3.10, 3.11, 3.12, and 3.13 as tested versions. A mailing list is available for discussion and questions.