Astronomer makes Airflow easier to run. Kestra makes orchestration easier to use. Astro removes the infrastructure burden of Apache Airflow. Kestra removes the Python-only constraint entirely, and extends orchestration beyond data pipelines to infrastructure, business processes, and AI workflows.
Declarative YAML orchestration layer that runs workflows in any language without wrapping your code in framework-specific abstractions. Data pipelines, infrastructure automation, and business processes run from one control plane. The workflow definition is configuration, not code.
A fully managed SaaS platform built on top of Apache Airflow. Astro removes the operational overhead of running Airflow yourself: no infrastructure to manage, enterprise RBAC and SSO included, cloud-native scaling. Workflows are still Python DAGs, and the Python-first constraint comes with them.
Astronomer offers both a managed cloud service (Astro) and a self-hosted option. This comparison uses their recommended cloud path—install the Astro CLI, create an account, initialize a project, and deploy to the Astro platform.
curl -o docker-compose.yml \https://raw.githubusercontent.com/kestra-io/kestra/develop/docker-compose.ymldocker compose up
# Open localhost:8080# Pick a Blueprint, run it. Done.Download the Docker Compose file, spin it up, and you're ready (database and config included). Open the UI, pick a Blueprint, run it. No cloud account, no CLI to install, no Python environment to configure.
brew install astroastro dev init --from-template learning-airflowastro dev start
# Now write your DAGs in Python...# Production: deploy to Astro cloud (account required)Install the Astro CLI, create an Astronomer account, initialize a project with a template, and start local Airflow. Production workflows require deploying to the Astro cloud platform. Still Python DAGs once you're running.
YAML is readable on day 1. Our docs are embedded in the UI for easy reference, the AI Copilot writes workflows for you, or start with our library of Blueprints. No Python knowledge required to understand or modify a workflow.
Astro provides a managed environment, AI-assisted debugging, and pipeline observability on top of Airflow. The authoring experience is still Python DAGs. Modifying orchestration logic requires Python, and non-Python contributors can view but not author or edit workflows.
Orchestrate across data pipelines, infrastructure operations, business processes, and AI workflows in one unified platform. Event-driven at its core, with native triggers for S3, webhooks, Kafka, and message queues.
A managed Airflow platform with enterprise-grade infrastructure. Excellent for Python-based data pipelines with observability and SLA tracking. Infrastructure automation and business process workflows are possible via Python operators but are not first-class use cases.
| | | |
|---|---|---|
| Workflow definition | Declarative YAML | Python DAGs (managed Airflow) |
| Languages supported | Any (Python, SQL, R, Bash, Go, Node.js) | Python-first (Bash/SQL via operators) |
| Infrastructure management | Self-hosted or Kestra Cloud | Fully managed on Astronomer's cloud |
| Architecture | Event-driven at core | Schedule-first (Airflow 3 Asset Watchers added event triggers) |
| Primary use case | Universal workflow orchestration | Data pipeline orchestration |
| Open source | Full open-source core, self-hostable | Apache Airflow is OSS; Astro platform is SaaS |
| Multi-tenancy | Namespace isolation + RBAC out-of-box | RBAC and SSO via Astro enterprise |
| Self-service for non-engineers | Kestra Apps | Observability UI only, no self-service authoring |
| Infrastructure automation | Native support | Possible via Python operators, not a primary use case |
We switched from Airflow because we want engineers solving problems, not coding orchestration. Kestra delivers end-to-end automation with the robustness we need at our scale. Few companies operate at this level, especially in AI/ML.
Bring your Python scripts, SQL queries, Shell commands, and R notebooks. Kestra orchestrates all of them in YAML without wrapping anything in decorators or operators. Engineers write code in the language that fits the task, not the language the orchestrator demands.
Native triggers for S3 file arrivals, Kafka messages, webhooks, database changes, and API callbacks. Kestra was built event-driven from day one, and unlimited event-driven workflows are available in the open-source version without additional configuration.
Data pipelines, infrastructure provisioning, and business process automation run from one control plane. When your data team needs to trigger an infrastructure job or a business process after a pipeline completes, it's a native YAML trigger, not a workaround.
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See how Kestra can simplify your data pipelines—and scale beyond Python-first orchestration.