Prefect
What Prefect is, how flows and tasks turn Python into orchestrated pipelines, Prefect Cloud pricing as of October 2026, and how it compares to Airflow.
Python-native workflow orchestration: decorate functions as flows and tasks, then schedule, retry and observe them.
Best for
Data and ML engineering teams that write pipelines in Python and want orchestration without a DSL.
Not great for
No-code teams and app-to-app business automation.
Verdict
The most Pythonic orchestrator. Lighter to adopt than Airflow; still a tool for engineers, not ops.
Pros
- + Plain Python, minimal boilerplate
- + Open source under Apache 2.0
- + Free Hobby tier on Prefect Cloud
Cons
- − Engineer-only tool
- − No catalog of SaaS business-app actions
- − Team features priced per user
Key features
- • Flows and tasks defined with Python decorators
- • Retries, caching and timeouts per task
- • Schedules, event triggers and webhooks
- • Deployments on your own compute or Prefect Serverless
- • Observability UI for runs, logs and states
- • Open-source server (Apache 2.0) or Prefect Cloud
What is Prefect?
Prefect is a Python-native workflow orchestration framework. You take existing Python functions, mark them as flows and tasks with decorators, and Prefect adds scheduling, retries, caching, logging and a UI to watch every run. The open-source project is licensed under Apache 2.0, and Prefect Cloud is the company’s managed control plane.
People searching “what is Prefect” are usually data engineers comparing orchestrators. The short answer: Prefect does what Airflow does (schedule and monitor pipelines), but lets you write normal Python instead of building DAG objects.
How Prefect works with Python
from prefect import flow, task
@task(retries=3, retry_delay_seconds=30)
def extract():
...
@task
def load(rows):
...
@flow(log_prints=True)
def nightly_sync():
load(extract())- Tasks are units of work with their own retry, timeout and caching settings.
- Flows are the orchestrated functions that call tasks; they can branch and loop with ordinary Python control flow.
- Deployments register a flow with a schedule or trigger (cron, interval, event, webhook) and the infrastructure it should run on.
- Workers or Prefect Serverless pick up scheduled runs and execute them on your compute (Kubernetes, Docker, cloud containers) or Prefect’s.
Prefect pricing
As of October 2026:
- Open source: free, self-hosted Prefect server under Apache 2.0.
- Hobby (Cloud): free; 2 users, up to 5 deployments, 500 minutes of Prefect Serverless.
- Starter: $100/month; 3 users, up to 20 deployments, bring-your-own compute, webhooks.
- Team: $100 per user per month; 4 to 8 users, up to 100 deployments, service accounts and audit log.
- Enterprise: custom; SSO, RBAC, SCIM, PrivateLink.
Check prefect.io/pricing for current terms.
Who Prefect is for (and not for)
Good fit: data and ML teams running ETL, model training, report generation and other Python jobs that need schedules, retries and visibility.
Poor fit: business teams that want “when a deal closes in HubSpot, post to Slack and create an invoice”. Prefect can do it, but every connector is Python you write and maintain.
Prefect alternatives
- Airflow, the incumbent; see Airflow vs Prefect.
- Dagster, asset-oriented orchestration; see Prefect vs Dagster.
- Temporal for durable application workflows rather than data pipelines.
- The full list: Prefect alternatives.
How Prefect compares to Automatite
Prefect orchestrates Python code; Automatite orchestrates business apps. Automatite gives ops teams a visual builder, app actions and scheduling, with code steps for custom logic. If the work is data engineering in Python, Prefect fits better. See Automatite vs Prefect.
FAQ
What is Prefect used for?
Scheduling, running and monitoring Python data pipelines and jobs, such as ETL, ML training and reporting, with retries and observability.
Is Prefect open source?
Yes. The Prefect project on GitHub is licensed under Apache 2.0. Prefect Cloud is the paid managed offering.
Prefect vs Airflow: which is easier?
Prefect is generally faster to adopt for Python teams because flows are plain functions; Airflow has the larger ecosystem and installed base.