For scheduled work that is not a data pipeline.
Airflow schedules and monitors pipelines, and it is good at that. If you are reaching for a DAG to call APIs and run prompts, a Task is a program on a managed runtime with no scheduler underneath it.
since = qbash.run.last_run_at.isoformat() for repo in qbash.integrations.github.list_repos()["repos"]: commits = qbash.integrations.github.list_commits( owner=repo["owner"]["login"], repo=repo["name"], since=since, )["commits"] brief = qbash.ai.run_prompt(slug="release-notes", variables={"commits": commits}) qbash.memories.store("release-notes", content=brief)
Model and provider agnostic.
Anthropic
OpenAI
Gemini
xAI
OpenRouter
Amazon
Perplexity
MoonshotAI
Meta
Qwen
qbash vs Airflow
These tools are aimed at different jobs, so read this as scoping rather than a scoreboard. Airflow orchestrates pipelines. A task runs a business process. The rows below decide which one you have.
| Feature | qbash | Airflow |
|---|---|---|
| The job | A business process with AI steps | Data pipeline orchestration |
| Language | Python | Python |
| What you operate | Nothing. It is managed. | Scheduler, DAG processor, API server, database |
| Deploying a change | Saving puts the revision live | Sync DAG files, then wait for the parse |
| Model calls | qbash.ai.run_prompt, with structured output | Bring your own SDK and keys |
| Prompts | Owned by a Task, versioned with it, and called by slug | You build it |
| Integration credentials | Brokered server-side, OAuth refresh handled | Connections you configure and rotate |
| Change history | A revision on every save, with a line-level diff | Whatever your Git and CI provide |
| Resuming a partial run | Checkpoints skip the items that finished | Clear and re-run failed tasks |
| Backfills over date ranges | Not a first-class concept | Yes, and it is a core strength |
| Asset lineage | Not included | Via assets and downstream tooling |
| Run surface for non-developers | Typed inputs render a form | Trigger with config from the UI |
| Self-hosting | Not available. The runtime is proprietary. | Yes, it is Apache 2.0 |
The AI layer is part of the platform.
Putting model calls in a DAG means your own SDK, your own keys, your own retry logic and somewhere to keep prompts. In a task those are the platform.
Model calls are functions
Each inline prompt belongs to a Task and has its own model settings. Choose a model in the prompt editor, and use an enabled provider that supports the capabilities your step needs. The surrounding Python stays as it is.
brief = qbash.ai.run_prompt( slug="release-notes", variables={"commits": commits}, )
Nothing to operate
An Airflow 3 deployment means a scheduler, a standalone DAG processor, an API server and a metadata database, plus workers depending on your executor, all kept patched. If the DAG exists mainly to call some APIs on a schedule, that is a lot of platform for the job.
# Save is the deploy. # No scheduler, no DAG processor, no API server, # no metadata database, no worker pool, # no DAG parse to wait for, no version upgrade weekend.
Credentials are brokered outside your code
Connect an account once for the org. Real API calls made server-side, not routed through a model, and credentials never appear in your code, your logs, or a model’s context.
commits = qbash.integrations.github.list_commits( owner="acme", repo="platform", since=qbash.run.last_run_at.isoformat(), )
Dagster vs Airflow comes down to assets or tasks.
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 Datasets to Assets, but an Airflow Asset is a signal that a dataset was updated, used to schedule downstream DAGs. A Dagster asset is the unit you define the pipeline in, and the difference shows up in testing, in backfills and in what a failure tells you.
| Dagster | Airflow | qbash | |
|---|---|---|---|
| Core abstraction | An asset | A task in a DAG | A script with typed inputs |
| 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, draft runs |
| 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 |
| 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 |
- Dagster fits when you think in tables and datasets that should exist, want lineage and a catalogue without bolting on a second tool, keep data quality checks next to the asset that produces them, and want a strong local development loop.
- Airflow fits when you already run it well and the operational cost is paid, need the breadth of its provider and operator ecosystem, or the work is task scheduling rather than asset production. It also has the larger hiring pool and the most existing answers online.
For Dagster jobs that produce no data asset.
A weekly summary or a triage routine modelled as an asset buys lineage and materialisation semantics for something that produces no table. When the output is a decision, a message or a deck, a task is a program with typed inputs and a schedule.
- Typed inputs render a form, so the person who asked for the deck runs it with their own quarter and account, without a launchpad or a config file.
- Keep anything with lineage, partitions, backfills or asset checks in Dagster.
- A task exposes a webhook with a synchronous response, so an asset can call it and use the result while materialisation and lineage stay in Dagster.
- A tests.py rides with the task in the same revision. Tests run in a sandbox where any unmocked integration or model call raises, so a test cannot post to a real channel or bill a real model call.
deals = qbash.integrations.hubspot.list_deals(limit=100) summary = qbash.ai.run_prompt(slug="qbr-summary", variables={"deals": deals}) template = qbash.files.read(qbash.inputs.get("template"), encoding="bytes") deck = qbash.files.write( qbash.powerpoint.fill(template, summary), "qbr.pptx", content_type=PPTX, ) qbash.output(qbash.files.download_url(deck))
What moves, and what should not.
Most teams keep Airflow. The DAGs worth moving are the ones that were never really pipelines.
- Move DAGs that mostly call APIs, summarise, or run prompts on a schedule.
- Keep DAGs that build tables, depend on other datasets, or need backfills.
- A task exposes a webhook with a synchronous response, so an operator can call it and use the result.
- That keeps scheduling and lineage in Airflow while the AI step runs where the prompts and credentials are.
Where Airflow is the right tool.
Airflow has the deepest provider ecosystem in data orchestration, and none of the above changes that for pipeline work.
- You are orchestrating pipelines with real dependencies between datasets.
- You need backfills across historical date ranges.
- You need to self-host, for data residency or cost at scale.
- You depend on the provider and operator ecosystem for warehouses and transformation tools.
Questions
Is qbash a replacement for Airflow or Dagster?
Not for data orchestration, and it is not trying to be. There are no backfills, no lineage, no partitions and no asset checks. It replaces either one only where a DAG or an asset was being used to call APIs and run prompts on a schedule, which does not need an orchestrator underneath it.
Can Airflow or Dagster call a qbash task?
Yes, and that is the common arrangement. A task can be triggered by webhook and return its result synchronously, so an Airflow operator or a Dagster asset calls it, gets the output and carries on. Scheduling and lineage stay in the orchestrator.
Is Dagster better than Airflow?
For new projects that are 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.
Is migrating from Airflow to Dagster worth it?
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.
What is the lightest alternative to Airflow for simple scheduled jobs?
For pure scheduling with no AI involved, a cron host or a managed job runner is usually enough, and Prefect and Dagster are the closest orchestrator alternatives. A task fits when the job involves model calls, integration credentials, or a result someone on the team reads.
How long can a task run?
The default timeout is 15 minutes and you can raise it in the task settings, up to 12 hours. Checkpoints mean a failure part way through resumes rather than restarting.
Competitor details reviewed . Vendors change plans and features without notice, so check theirs before deciding.
More comparisons
Keep the pipelines. Move the AI part.
Run Python tasks on managed infrastructure with task-owned prompts, structured model output, and run logs.