Airflow alternative
When Airflow is too much, keep orchestration close to the OS.
Dagu is an Airflow alternative for teams that want scheduling, retries, dependencies, logs, and a UI without adopting a Python framework or operating a heavy metadata stack.
name: nightly-ops
schedule: "0 2 * * *"
steps:
- id: extract
run: python scripts/extract.py
- id: transform
run: ./bin/transform
retry_policy:
limit: 3
depends: [extract]
- id: notify
run: ./scripts/slack-success.sh
depends: [transform]No Python DAG framework required
No metadata database required to start
Run existing scripts and containers directly
Scale to workers when the workload needs it
At a glance
Airflow vs. Dagu for script-first teams
Declarative YAML that calls commands.
Python DAG definitions and operator abstractions.
Single binary and local files to start.
Scheduler, webserver, metadata database, and executor choices.
Ops automation, scripts, containers, agent CLIs, and lightweight pipelines.
Large data platform workflows that benefit from the Airflow ecosystem.
In depth
Where each tool fits
Use commands as the workflow boundary
Airflow is powerful, but many teams only need to schedule, observe, and retry existing scripts and jobs. Dagu keeps commands as the stable interface.
- Run Python, Bash, Java, Go, PHP, containers, HTTP calls, and SSH commands.
- Avoid converting every task into a framework-specific operator.
- Keep workflow definitions readable for operators and engineers.
Avoid infrastructure before you need it
Dagu starts as one binary backed by files. You can add queues and workers later without changing the basic workflow model.
- Try locally before planning a platform rollout.
- Keep upgrades and backups simpler.
- Use distributed mode only when one host is no longer enough.
A better fit for mixed engineering work
Ops jobs, ETL scripts, AI agent tasks, reporting jobs, and internal automation rarely live in one language or framework.
- Let each step use the runtime it already uses.
- Wrap schedules, retries, logs, and approvals around existing commands.
- Keep orchestration portable across teams and stacks.
FAQ
Practical questions before adopting Dagu
Is Dagu a full replacement for every Airflow deployment?
No. Airflow has a large ecosystem for data-platform teams. Dagu is the better fit when the work already runs as scripts, containers, or service calls and you want a smaller self-hosted runtime.
Can Dagu run data pipelines?
Yes, if the pipeline can be represented as commands, containers, HTTP calls, SSH commands, or sub-workflows. Dagu focuses on orchestration rather than framework-specific data abstractions.
Does Dagu support distributed execution?
Yes. Dagu supports local, queue-based, and coordinator-worker execution modes so teams can grow from one machine to distributed workers.
Next step
Start with one workflow.
Install Dagu, move one fragile script or agent task into YAML, and decide from a real run history.