Airflow vs Prefect: Comparison Guide

I evaluated Prefect seriously. I ran it in a staging environment for six weeks, built three real flows, and had the internal conversation about migrating. Then we stayed with Airflow.

That was eighteen months ago. Some of that decision was right. Some of it I would make differently today, especially now that Airflow 3.0 is out and Prefect 3.x has matured. This is a breakdown of both tools from someone who ran the evaluation, not someone summarising the docs.

If you lead or work on a small data team and are deciding between the two, or wondering whether to migrate, this post covers how the tools differ in practice, where each one wins, what a migration really costs, and what I would do differently.

What We Were Running When We Evaluated

Our stack at the time: Apache Airflow 2.7, self-hosted on Kubernetes via the Helm chart, about 60 active DAGs loading data from seven upstream sources into Snowflake. The team was four data engineers, and one of them was spending roughly 20% of their time on Airflow infrastructure maintenance.

That last number triggered the evaluation. 20% of a senior engineer’s time on scheduler maintenance is expensive. Prefect’s pitch, that you could offload orchestration state to Prefect Cloud while keeping your execution code on your own infrastructure, was aimed directly at that pain.

The Core Difference Nobody Explains Clearly

Airflow was built around the DAG file. You write a Python file that describes a directed acyclic graph of tasks. The scheduler reads those files, works out what needs to run, and hands work to workers.

The mental model: your code lives in files, and the scheduler coordinates execution.

Prefect flips this. You write normal Python functions and decorate them with @flow and @task. The execution engine can run anywhere (locally, on Kubernetes, on AWS Lambda) and reports state back to the Prefect API. Your code does not change based on where it runs.

The mental model: your code is portable, and orchestration is a service.

That sounds like a small distinction. In practice it changes the whole developer experience.

Local development

With Airflow, testing a DAG locally means running a full Airflow stack: scheduler, webserver, worker and database. Even with airflow standalone, it is not the same environment as production. Most teams end up pushing code to a dev environment and waiting to see if it fails. Iteration is slow.

With Prefect, you run the flow like a normal Python script. No server is needed. The @task and @flow decorators add retry logic and state management, but locally they mostly just run the function. The feedback loop is tight.

Dynamic workflows

Airflow DAGs are static by design. The structure of the graph is fixed at parse time, not at runtime. Airflow 2.x added dynamic task mapping, which helps, but working around the static-DAG constraint still takes real mental effort.

Prefect flows are just Python. If you want to fan out tasks over a list you only know at runtime, you just do it. The .map() method handles the parallelism.

The same pipeline in both tools

Here is the same ETL pipeline written for each tool.

Airflow version

1from airflow import DAG2from airflow.operators.python import PythonOperator3from datetime import datetime, timedelta4 5def extract(): return "raw_data"6def transform(ti): return ti.xcom_pull(task_ids='extract')7def load(ti): print(ti.xcom_pull(task_ids='transform'))8 9with DAG(10    'etl_pipeline',11    default_args={'retries': 2, 'retry_delay': timedelta(minutes=5)},12    schedule_interval='@daily',13    start_date=datetime(2024, 1, 1),14    catchup=False,15) as dag:16    t1 = PythonOperator(task_id='extract', python_callable=extract)17    t2 = PythonOperator(task_id='transform', python_callable=transform)18    t3 = PythonOperator(task_id='load', python_callable=load)19    t1 >> t2 >> t3

Each step is wrapped in a PythonOperator, data moves between tasks through XCom (ti.xcom_pull), retries are set once in default_args, and the >> operator defines the order.

Prefect Version

1from prefect import flow, task2from datetime import timedelta3 4@task(retries=2, retry_delay_seconds=300)5def extract():6    return "raw_data"7 8@task9def transform(data: str):10    return data.upper()11 12@task13def load(data: str):14    print(f"Loading: {data}")15 16@flow(name="etl-pipeline", log_prints=True)17def etl_pipeline():18    raw = extract()19    cleaned = transform(raw)20    load(cleaned)21 22if __name__ == "__main__":23    etl_pipeline()

The Prefect version is plain Python. There are no Airflow-specific operator classes, no XCom for passing data between tasks, and no DAG context manager: return values are passed straight to the next function. (The transform step is slightly different between the two examples, since the Prefect one upper-cases the string, but the structure is the same.) A Python developer who has never seen Prefect can read it immediately.

Where Airflow Still Wins

Ecosystem maturity

Airflow has 80,000+ organisations using it and 30M+ monthly downloads as of 2026. In practice that means:

• When you hit a problem, someone has hit it before and documented the fix.
• When you need to hire, Airflow experience is common.
• When you need an integration (Snowflake, dbt, Spark, Kubernetes, every AWS service), there is a provider package that works.

Prefect has fewer pre-built integrations. For standard ones it is fine. For niche systems or complex enterprise connectors, you often write more code yourself.

Airflow 3.0 closes the gap

Airflow 3.0, released in April 2025, is the biggest update since the project started. The UI is much better. Event-driven scheduling via Data Assets now works properly. Task isolation means one failing task cannot take down the whole worker. DAG versioning is finally real.

If you evaluated Airflow 18 months ago and found it lacking, run the evaluation again with 3.0. Several of Prefect’s clearest advantages have been addressed.

Proven at scale

Companies like Airbnb run tens of thousands of DAGs on Airflow. The scheduler handles serious workloads. If you are at enterprise scale with complex dependency chains, that track record matters.

Where Prefect Wins

Lower operational overhead for small teams

Running Airflow in production means managing a scheduler, a webserver, one or more workers, a PostgreSQL or MySQL database, and an executor (Celery or Kubernetes). On managed services like MWAA or Astronomer you pay for that complexity instead of managing it, but the cost is there either way.

Prefect’s hybrid model runs your execution code on your infrastructure while Prefect Cloud (which has a generous free tier) manages orchestration state. You run a lightweight agent, and that is it.

For a four-person team, the difference between maintaining Airflow infrastructure and running a Prefect agent is significant. That 20% platform overhead we had would likely have dropped to under 5%.

Monitoring and observability out of the box

Airflow monitoring needs external tooling: Prometheus, Grafana, custom alerting. Prefect’s UI includes real-time dashboards, event-driven triggers, and built-in logging that surfaces errors clearly.

The first time a Prefect flow fails and you see exactly what went wrong in the UI, with full log context, retry history and input/output state, it is a noticeably better experience than debugging a failed Airflow task.

[ADD YOUR OWN: a screenshot of a failed flow run in the Prefect UI from your staging evaluation, ideally next to the Airflow task log for a comparable failure]

ML pipelines

This is the one I regret not acting on. Prefect is much better than Airflow for ML workflows. Dynamic task mapping lets you run parallel training jobs across different hyperparameter sets without restructuring your DAG. The Pythonic interface means ML engineers can write flows without learning Airflow’s operator model. And local testing means they can iterate fast.

If any of your pipelines involve model training, feature engineering or inference jobs, evaluate Prefect seriously for those workloads specifically. You do not have to migrate everything.

Side-by-Side Comparison

What the Migration Actually Looks Like

If you are considering moving from Airflow to Prefect, this is what it involves, as opposed to the vendor’s optimistic version.

There is no automatic DAG-to-flow converter. You rewrite each DAG as a Prefect flow. For simple linear DAGs this is quick, often quicker than writing the original. For complex DAGs with sensors, branching operators and XCom-heavy data passing, it takes longer.

The harder part is operational: updating CI/CD pipelines, retraining the team, updating monitoring and alerting, and managing the transition period when some workflows run on Airflow and some on Prefect.

A realistic estimate for a team with 40 to 60 DAGs is four to eight weeks. It is not a weekend project. Budget time for the operational work, not just the code conversion. I wrote about a similar migration reality in Delta Lake vs Iceberg, and the pattern is identical: the data conversion is the easy part.

When to Choose Each Tool

In my experience, Airflow is the better fit for complex, distributed workflows, while Prefect suits smaller, more agile projects. More specifically:

Choose Airflow when

• You already run it and it is stable. Migration cost is real.
• You need enterprise-scale reliability with a proven track record.
• Your team has strong Airflow expertise and hiring for it matters.
• You are on a managed service (MWAA, Astronomer) and the overhead is already handled.
• You need the broadest possible integration ecosystem.

Choose Prefect when

• You are starting fresh with no existing orchestration investment.
• You have a small team without dedicated platform engineering.
• You are building ML or AI pipelines that need dynamic task mapping.
• Your engineers are strong Python developers who find Airflow’s operator model unnatural.
• Developer velocity matters more than ecosystem breadth right now.

What I Would Do Differently

I would have adopted Prefect for our ML pipelines straight away, while keeping Airflow for everything else. The two tools can coexist. Nothing says you have to pick one for your entire data platform.

For new batch ETL on stable sources, Airflow. For model training, feature pipelines and anything that needs dynamic execution, Prefect. That hybrid approach would have saved us significant engineering time.

[ADD YOUR OWN: one of the three flows you built during the six-week evaluation, what it did, and what was easier or harder than the equivalent Airflow DAG]

If you are starting fresh in 2026 with no legacy commitment, I would evaluate Prefect first. Airflow 3.0 is better than it has ever been, but Prefect’s developer experience is still ahead, and the operational overhead difference for small teams is real.

You can find more of my posts on orchestration and pipeline design in the data engineering archive.