Prefect

What is Prefect?

Prefect is a workflow orchestration platform for building, scheduling and monitoring data and AI pipelines in Python. It is made by Prefect Technologies and is available as open source software and as Prefect Cloud. The problem it solves is unreliable automation: teams write ordinary Python functions, add Prefect decorators, and get retries, scheduling, logging and a UI without building a scheduler and state machine themselves.

Key features

• Python-native flows. Workflows are defined with @flow and @task decorators, so existing Python code can be orchestrated without a separate DSL.
• Scheduling and triggers. Flows can run on schedules, on demand or from events and deployments.
• Retries and caching. Tasks can retry on failure and cache results, which reduces repeated expensive work.
• Observability. The Prefect UI shows runs, task states, logs and history for debugging.
• Deployments. Teams can package flows as deployments and run them on their own workers or on Prefect Cloud.
• Prefect Cloud. The managed service adds collaboration, retention and governance beyond the open source server.
• FastMCP. Prefect also maintains FastMCP, a framework for building Model Context Protocol servers in Python.

Who is Prefect for?

Data engineers and Python developers building ETL, analytics, machine learning and AI pipelines. It suits teams that have outgrown cron jobs and scripts, platform teams standardising orchestration, and organisations that want to keep workflow code in Python while adding observability. It is also used by AI teams that need reliable multi-step agent and data jobs.

Use cases

• Scheduling nightly ETL and data warehouse loads.
• Orchestrating model training, evaluation and deployment steps.
• Running retryable API sync jobs across several services.
• Coordinating multi-step AI agent workflows.
• Monitoring scheduled Python jobs with logs and run history.

Pricing

Prefect has a Hobby plan at $0 with full access to core functionality for up to five deployments and seven days of run retention. Starter costs $100 per month and adds three users, up to 20 deployments, 75 hours of serverless workflow compute and webhooks. Team costs $100 per user per month and adds collaboration, service accounts, audit logs and up to 100 deployments. Enterprise pricing is custom with governance, security and support options. See the official Prefect pricing page.

Getting started

1. Install Prefect in your Python environment.
2. Start a local Prefect server or connect to Prefect Cloud.
3. Define a flow and tasks with the decorators.
4. Run the flow locally, then create a deployment to schedule it.

pip install prefect
prefect server start

from prefect import flow, task
 
@task
def load_data():
    return [1, 2, 3]
 
@flow
def pipeline():
    data = load_data()
    print(sum(data))
 
pipeline()

Alternatives

• Temporal is a durable execution platform for long-running workflows.
• Hatchet is an orchestration engine for background tasks and AI agents.
• Inngest is an event-driven workflow platform for background jobs.

FAQ

Is Prefect free?

Yes. The Hobby plan costs $0 and supports up to five deployments with seven days of run retention. Paid plans start at $100 per month.

What is Prefect used for?

Prefect is used to orchestrate Python workflows such as ETL, data processing, machine learning and AI pipelines. It adds scheduling, retries, logging and a UI to ordinary Python code.

Prefect vs Airflow?

Airflow uses DAGs defined in Python and is common in data engineering. Prefect lets teams write native Python functions with decorators and focuses on dynamic workflows and developer experience. The better fit depends on existing Airflow investment and how dynamic your pipelines are.

Can I self-host Prefect?

Yes. The open source Prefect server can run on your own infrastructure. Prefect Cloud is the managed option with collaboration and governance features.

Does Prefect support AI workflows?

Yes. Prefect can orchestrate multi-step AI and agent workflows, and the team maintains FastMCP for building MCP servers in Python.