Prefect versus Airflow is really a question about which era your pipeline belongs to. Airflow was built for scheduled batch data — run this DAG at 2 a.m., move these tables, done. AI pipelines don't look like that. They're dynamic, the graph often isn't known until runtime, and every other node is a slow, flaky network call to a model that might time out or rate-limit. The orchestrator's failure model matters more than its feature list.
We run orchestration in production across several systems and have written about Airflow, Celery, and Temporal as the spine of an AI pipeline. Prefect sits in the same conversation, and where it fits comes down to how your pipeline fails.
Airflow: maturity and ecosystem
Airflow's case is strong and boring in the best way. It's the incumbent scheduler: a huge provider/operator catalog, battle-tested at scale, and — crucially — an ops team that probably already knows how to run it. For scheduled batch data pipelines where the DAG is static and known ahead of time, Airflow is a safe, well-worn default. You're not the first person to hit any problem you'll hit.
Its friction shows up when the work is dynamic. Airflow's heritage is static DAGs, and AI pipelines that decide their shape at runtime — fan out over however many documents arrived, branch on a model's output — fight that grain.
Prefect: Python-native and dynamic
Prefect was built for the shape Airflow strains against. Workflows are ordinary Python; the graph can be constructed at runtime; and retries, timeouts, and failure handling read like normal code rather than DAG configuration. For a pipeline that's mostly "call an LLM, handle the flaky result, sometimes loop, sometimes branch," that ergonomic fit is real. The flaky-LLM-call problem is exactly what its retry and state model was designed around.
The trade is maturity and ecosystem depth: Airflow has more operators, more community answers, and more people who've already run it in anger.
Both are schedulers, and that's the ceiling
Here's the part both camps skip. Prefect and Airflow are schedulers. They coordinate tasks and can retry them, but neither is a durability engine. When a worker dies mid-task, the guarantee you get about your workflow's state is weak — and for AI work that spans slow steps, workers die mid-task routinely.
Once the unit of work is a workflow that must survive for days, or one that moves money and cannot be safely re-run, you've outgrown the scheduler question entirely. That's Temporal's job: it guarantees the workflow's state survives the worker dying, which is a categorically different promise than "the scheduler will try again."
Which should you pick
Choose Airflow when you have scheduled batch data pipelines, an ops team that already runs it, and you value a mature ecosystem over developer ergonomics. Choose Prefect when your pipelines are dynamic, Python-native, and full of flaky LLM calls, and you want retries and failure handling to feel like normal code.
But check the ceiling first. If a lost step costs money or the work must survive for days, neither scheduler is the answer — you want a durable workflow engine. Picking Prefect over Airflow is a real improvement for AI-shaped work; picking a scheduler when you needed durability is how pipelines die at 2 a.m.