Airflow schedules tasks. Dagster models assets. That one difference in abstraction drives most of the rest, including how you test, how you backfill, and what a failure tells you.
Mostly a question of whether your work is best described as steps to run or as data that should exist.
The first two columns are the decision most readers came for. The third is here because a lot of what gets built in an orchestrator is not a pipeline at all.
| Feature | Dagster | Airflow | Aisle |
|---|---|---|---|
| Core abstraction | An asset | A task in a DAG | A script with typed inputs |
| The job | Asset-oriented orchestration | Pipeline scheduling | A business process with AI steps |
| Lineage and catalogue | Built in | Via assets and other tooling | Not included |
| Data quality checks | Asset checks | Bring your own | Not included |
| Partitions and backfills | First class | First class | Not a first-class concept |
| Local development | Full local loop | Workable, historically awkward | Editor with the SDK in scope |
| What you operate | Self-host, or Dagster+ | Scheduler, DAG processor, API server, database | Nothing. It is managed. |
| Deploying a change | Ship a code location | Sync DAG files, wait for the parse | Saving puts the revision live |
| Model calls | Your own SDK and keys | Your own SDK and keys | aisle.ai.run_prompt, with structured output |
| Prompts | You build it | You build it | Versioned, called by slug |
| Integration credentials | Resources you configure | Connections you rotate | Brokered server-side |
| Non-developer run surface | Launchpad with run config | Trigger with config | Typed inputs render a form |
| Licence | Open source, plus Dagster+ | Apache 2.0 | Proprietary, managed only |
A weekly summary, a triage routine, a report five people read. These become DAGs or assets because the orchestrator was already in the building, then carry a scheduler cluster for work that produces no table.
A Task is a deterministic AI automation you build in Python, hosted and run on secure cloud infrastructure. Keep the orchestrator for pipelines, and run this where the prompts and credentials already are.
since = aisle.run.last_run_at.isoformat() for repo in aisle.integrations.github.list_repos()["repos"]: commits = aisle.integrations.github.list_commits( owner=repo["owner"]["login"], repo=repo["name"], since=since, )["commits"] notes = aisle.ai.run_prompt(slug="release-notes", variables={"commits": commits}) aisle.integrations.slack.create_message("#eng", text=notes)
For new projects that are genuinely about producing data assets, Dagster fits the problem more directly, and its local development and testing story is better. Airflow keeps the advantage on ecosystem breadth, hiring pool, and the fact that it is already running and understood in most data teams.
The abstraction. Airflow schedules tasks, so you describe the steps to run. Dagster models assets, so you describe the data that should exist and it works out what to run. Airflow 3 renamed its Datasets to Assets, which narrows the vocabulary gap but not the design one: an Airflow Asset is a signal that a dataset was updated, used to schedule downstream DAGs, while a Dagster asset is the thing you define your pipeline in. The difference still shows up in testing, in backfills, and in what a failure tells you.
Rarely as a wholesale rewrite. The common path is to leave existing DAGs alone and build new work in Dagster, or run Dagster over Airflow during a transition. A full migration is justified mostly when lineage and data quality have become the bottleneck.
You can, and plenty of teams do. Both leave you to supply the model SDK, the keys, the retry logic and somewhere to keep prompts. Neither versions prompts or constrains the shape of what a model returns, which is where AI-heavy jobs tend to break.
Often not. If the script has no dataset dependencies and no backfill requirement, a scheduler cluster is a lot of platform for the job. A managed runtime that versions the code and brokers credentials covers it with less to operate.
Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.
A managed Python runtime with versioned prompts, structured model output and nothing to operate.