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.
Kestra vs. Apache Airflow: Orchestrate Your Stack, Not Just Your DAGs
Airflow schedules Python pipelines for data engineering teams. Kestra orchestrates workflows across your entire stack. One serves Python-native data teams. The other changes what's possible for your business.
Two Architectures, Two Philosophies
Universal Orchestration: Pipelines and Workflows
Declarative orchestration layer where YAML describes intent and any language executes. Orchestration is configuration, not code. Data pipelines, infrastructure automation, and business processes run from a single control plane without forcing every task into one language.
"How do we run data pipelines, infrastructure jobs, and business workflows from one place?"
Python DAG Execution Engine
A Python-based workflow engine where DAGs are executable programs. Workflows are Python code that the scheduler runs. Everything—scheduling, dependencies, retries—is defined in Python and tied to the Airflow runtime.
"How do we schedule and monitor our Python data pipelines?"
Python Pipeline Scheduling vs. Universal Workflow Orchestration
Universal Workflows
- Data pipelines, infrastructure automation, business processes, AI workflows
- Multi-language: Python, SQL, Bash, Go, Node.js, R
- Event-driven at core: respond to real events, not just schedules
- Self-service for non-engineers via Kestra Apps
- One control plane for the entire organization
Data Pipelines for Python Teams
- Schedule-first pipeline orchestration (event-driven features added in v3)
- Python workflow definitions (Bash/SQL via operators)
- Data engineering scope
- Limited multi-tenancy
- Serves Python-native data teams
Time to First Workflow
Airflow's standalone command works for local development, but production deployments need an API server, DAG processor, metadata database, and executor configured separately. Kestra's single Docker Compose command stands up everything in a format that's already production-shaped.
~5
Minutes
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 Python environment, no DAG library to configure.
~30
Minutes
pip install apache-airflowpip install apache-airflow-providers-standard
# Dev: airflow standalone (SQLite, no parallelism)# Production: configure API server, DAG processor,# metadata DB, and executor separately# Then write your DAGs in Python...The standalone command gets a dev instance running quickly, but on SQLite with no parallelism. Production requires installing providers, configuring the API server, DAG processor, metadata DB, and executor separately.
YAML Anyone Can Read vs. Python Only Devs Can Write
Kestra: Readable by Your Whole Team
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.
Airflow: Python knowledge required for workflow authoring
DAG definitions, retry logic, and scheduling are all Python code. Airflow can run Bash and SQL via operators, but modifying orchestration logic requires Python. Non-Python contributors can view runs in the UI but can't author or edit workflows directly.
Full-Stack Orchestration vs. Python Pipeline Scheduling
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.
Purpose-built for Python data pipeline scheduling. Airflow 3 adds event-driven Asset Watchers, but the core is schedule-driven. Infrastructure automation and business processes are possible via Python but not first-class use cases.
Kestra vs. Apache Airflow 3 at a Glance
| | | |
|---|---|---|
| Workflow definition | Declarative YAML | Python DAGs |
| Languages supported | Any (Python, SQL, R, Bash, Go, Node.js) | Python-first (Bash/SQL via operators; multi-language Task SDK experimental) |
| Architecture | Event-driven at core | Schedule-first (event-driven Asset Watchers added in v3) |
| Assets and lineage | Data and infrastructure assets | Data assets only |
| Multi-tenancy | Namespace isolation + RBAC out-of-box | Limited |
| Deployment | Single service (Docker Compose) | API server + DAG processor + metadata DB + executor |
| Self-service for non-engineers | Kestra Apps | UI is observability-focused, no self-service triggers |
| Human-in-the-loop | Native task types | Requires custom operator or external tooling |
| Infrastructure automation | Native support | Possible via Python operators, not a primary use case |
200Engineers onboarded
2xFaster workflow creation
0Pipeline failures
Kestra Is Built for How Data Teams Actually Work
No refactoring required
Bring existing Python scripts, SQL queries, and Shell commands. Kestra orchestrates them in YAML without wrapping them in decorators or operators. Your code stays portable and your team stays unblocked.
Orchestrate beyond data pipelines
Data pipelines, infrastructure automation, and business processes run from one platform. Airflow focuses on data pipeline orchestration. Infrastructure and business workflows are possible through Python operators but aren't first-class use cases.
Event-driven, not just scheduled
Native triggers for S3, webhooks, Kafka, database changes, and API events. Airflow 3 added Asset Watchers for event-driven scheduling, but triggers in Kestra are first-class YAML declarations, not layered onto a schedule-first core. Unlimited event-driven workflows in open source.
The Right Tool for the Right Job
Choose Kestra When
- Your team writes Python, SQL, Bash, and dbt, and you want orchestration that doesn't force everyone into Python decorators.
- You need event-driven workflows that respond to real-time events, not just cron schedules.
- You're managing data, infrastructure, and business processes and want one control plane.
- Non-engineers need to trigger or monitor workflows without writing code.
- You're evaluating orchestrators and want to compare architectures before committing.
Choose Airflow When
- Your team is Python-native and invested in the Airflow ecosystem: custom operators, trained staff, institutional knowledge.
- You're running on managed services (MWAA, Cloud Composer) and are locked into the Airflow ecosystem.
- Python-based DAG definitions and Airflow's scheduling model fit your team's workflow.
- You have significant custom operator libraries you'd need to rebuild elsewhere.
Frequently asked questions
Find answers to your questions right here, and don't hesitate to Contact Us if you couldn't find what you're looking for.
Getting Started with Declarative Orchestration
See how Kestra can simplify your data pipelines—and scale beyond them.