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Databases · head to head

Apache Airflow vs Prefect

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Prefect logo

Prefect

Automation Integration

Python-first workflow orchestration that heals itself

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job; Prefect limited to Python-based workflows; other languages require API calls
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Prefect covers Python function decorators.

Where they differ

Only the attributes on which Apache Airflow and Prefect actually diverge.

Attributes where Apache Airflow and Prefect differ
AttributeApache AirflowPrefect
Pricing modelOpen source, no licence fee; managed services billed separatelyFreemium with per-user and usage-based tiers
PlatformsLinux, Docker, Kubernetes, Self-hostedCloud, Self-hosted, VPC
CategoryDatabasesAutomation Integration

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Airflow

  • Pipelines as Python
  • Web UI
  • Cloud provider packages
  • Jinja templating
  • Retries and dependencies
  • Extensible operators

Only in Prefect

  • Python function decorators
  • Automatic resilience
  • Flexible deployment
  • Observable execution
  • Multiple scheduling
  • Webhooks
  • Open-source core

What people use each for

The jobs each tool is most often brought in to do.

Apache Airflow

  • Scheduling nightly ETL where step order and retries matternot Prefect
  • Coordinating machine learning training and evaluation runsnot Prefect
  • Orchestrating dbt runs alongside extraction and loadingnot Prefect
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Prefect

Prefect

  • Orchestrating data pipeline workflows in Pythonnot Apache Airflow
  • Managing ML model training and inference jobsnot Apache Airflow
  • Building ETL and event-driven automationnot Apache Airflow
  • Monitoring and alerting on workflow healthnot Apache Airflow

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Airflow

  • Self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
  • Built for scheduled batch work, and a poor fit for event-driven or sub-minute latency pipelines
  • Because DAGs are Python that the scheduler parses continuously, expensive top-level code in a DAG file slows the whole scheduler
  • Local development and testing of DAGs is awkward compared with newer orchestrators designed with it in mind

Prefect

  • Limited to Python-based workflows; other languages require API calls
  • Hobby free tier severely limited for production use
  • Team plan billing per user scales quickly with team size
  • Less visual workflow builder compared to some competitors
  • Smaller ecosystem of pre-built connectors than alternatives

Pricing, plan by plan

Apache Airflow

Free
  • Apache AirflowFree
    • Full scheduler and web UI
    • All provider packages
    • No task or DAG limits

Prefect

Free
  • HobbyFree
    • 2 users and 5 deployments
    • 500 minutes serverless monthly
    • 7-day run retention
  • Starter$100/month
    • 3 users and 20 deployments
    • 75 hours serverless compute
    • Bring-your-own infrastructure
  • Team$100/user/month
    • 4-8 users per plan tier
    • 100 deployments
    • 225 hours serverless monthly
  • Enterprise$null/custom
    • SSO and SAML authentication
    • Role-based access control
    • Multiple workspaces

Which should you pick?

Choose Apache Airflow if

  • You need pipelines as python.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want web ui.
Apache Airflow review

Choose Prefect if

  • You need python function decorators.
  • You want to start without paying.
  • You work on Cloud, Self-hosted, VPC.
  • You also want automatic resilience.
Prefect review

Questions people ask

Is Apache Airflow or Prefect better?
Neither clearly leads. Apache Airflow starts at Free and Prefect at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Prefect?
Apache Airflow starts at Free and Prefect at Free.
Does Apache Airflow or Prefect run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Prefect runs on Cloud, Self-hosted, VPC.
Can I use Apache Airflow for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Airflow best used for?
Apache Airflow is most often used for scheduling nightly etl where step order and retries matter, coordinating machine learning training and evaluation runs, orchestrating dbt runs alongside extraction and loading, replacing a sprawl of cron jobs with dependencies and visible run history. Of those, scheduling nightly etl where step order and retries matter and coordinating machine learning training and evaluation runs are not what Prefect is typically brought in for.
What can Apache Airflow do that Prefect cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Prefect covers Python function decorators, Automatic resilience, Flexible deployment, Observable execution.

Answered from the vendors’ own pages

Apache Airflow: Is Apache Airflow free?

Yes. Airflow is open source under the Apache Software Foundation with no licence fee. Costs are the infrastructure to run it, or a managed service such as Google Cloud Composer or Amazon MWAA.

Prefect: What is included in the Hobby free plan?

Hobby includes 2 users, 5 deployments, 500 minutes of serverless compute monthly, and 7-day run retention.

Source
Apache Airflow: What language are Airflow workflows written in?

Python. A workflow is a Python file, so standard language features including loops and datetime handling can generate tasks dynamically, with no XML or command-line configuration.

Prefect: What is the difference between Starter and Team plans?

Starter is $100/month for 3 users and 20 deployments. Team is $100 per user per month for 4-8 users and 100 deployments.

Source
Apache Airflow: Is Airflow suitable for real-time pipelines?

Not really. Airflow is designed for scheduled batch orchestration. Event-driven or sub-minute work is better served by a streaming platform such as Kafka or a purpose-built streaming engine.

Prefect: Can I deploy Prefect in my own infrastructure?

Yes, Starter and higher plans support bring-your-own-compute infrastructure and VPC deployments.

Source
Apache Airflow: What are the main alternatives to Airflow?

Dagster and Prefect are the two most commonly weighed against it, both newer and both designed around the local development and testing experience Airflow is criticised for.

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