Prefect vs Airflow: Which Should You Self-Host?

Quick Verdict

Airflow is the safer choice for self-hosted data pipeline orchestration. It’s battle-tested, has a massive ecosystem, and its architecture is well-documented for self-hosting. Prefect offers a more modern, Pythonic API that’s genuinely nicer to write, but its self-hosted server has historically been a second-class citizen compared to Prefect Cloud — and the resource difference for self-hosting is minimal.

Overview

Apache Airflow is the industry-standard platform for orchestrating data pipelines. Created at Airbnb in 2014 and now an Apache Software Foundation project, it’s used by thousands of companies for ETL, ML pipelines, and batch processing. Workflows (DAGs) are defined in Python, scheduled via the webserver, and executed by workers.

Prefect is a modern workflow orchestration framework that positions itself as a simpler alternative to Airflow. It uses native Python — you decorate functions with @flow and @task, and Prefect handles scheduling, retries, and observability. Prefect 3.x includes a self-hosted server with a web UI, backed by SQLite or PostgreSQL.

Both tools target the same audience: data and ML engineers who write Python.

Feature Comparison

Feature	Prefect	Apache Airflow
Workflow definition	Python decorators (@flow, @task)	Python DAG objects and operators
API style	Native Python functions — call flows like regular code	DAG-centric — operators, hooks, sensors pattern
Scheduling	Cron, interval, RRule, deployments with work pools	Cron, dataset triggers, timetables
Web UI	Clean modern UI — flow runs, logs, deployments, work pools	Mature UI — DAG views (graph, tree, gantt), task logs, connections
Minimum containers	1 (Prefect server with SQLite)	5+ (webserver, scheduler, DAG processor, worker, PostgreSQL, Redis)
Minimum RAM	512 MB - 1 GB	4+ GB
Database	SQLite (default) or PostgreSQL	PostgreSQL (recommended) or MySQL
Executor model	Workers poll for work, run flows locally or in Docker/K8s	Celery workers, Kubernetes pods, or local
Provider ecosystem	Prefect integrations (50+)	70+ provider packages (deeply integrated)
Backfill	Manual re-runs via UI or API	Native backfill with date range selection
Dynamic tasks	Native — generate tasks in Python loops	Dynamic task mapping (Airflow 2.3+)
Local development	Run flows as normal Python scripts	Requires Airflow installation, DB, scheduler
License	Apache 2.0 (server), Prefect Community License (some components)	Apache 2.0
First stable release	2018 (Prefect 1), 2022 (Prefect 2), 2024 (Prefect 3)	2015 (incubating), 2019 (1.0), 2024 (3.0)

Installation Complexity

Prefect self-hosts with minimal effort. The server runs as a single process with SQLite by default:

docker run -d -p 4200:4200 prefecthq/prefect:3.6.18-python3.12 \
  prefect server start --host 0.0.0.0

For a Docker Compose setup with PostgreSQL:

services:
  prefect-server:
    image: prefecthq/prefect:3.6.18-python3.12
    command: prefect server start --host 0.0.0.0
    ports:
      - "4200:4200"
    environment:
      PREFECT_API_DATABASE_CONNECTION_URL: postgresql+asyncpg://prefect:prefect@postgres:5432/prefect
    depends_on:
      - postgres
    restart: unless-stopped

  postgres:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: prefect
      POSTGRES_PASSWORD: prefect
      POSTGRES_DB: prefect
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped

volumes:
  postgres_data:

Workers run separately — typically on the same machine or wherever your flows need to execute. You install Prefect in your Python environment and start a worker that polls the server for work.

Airflow requires a multi-container deployment. The official Docker Compose has 5+ services, Redis for the Celery executor, and significant configuration. See the Airflow vs n8n comparison for Airflow’s architecture details.

Performance and Resource Usage

Metric	Prefect	Apache Airflow
Idle RAM (server)	300-500 MB (SQLite), 500 MB - 1 GB (PostgreSQL)	4-6 GB (all services)
Idle CPU	Low	Medium (scheduler parses DAGs continuously)
Disk	1-2 GB	2-5 GB
Worker model	Process-based workers, Docker workers, K8s workers	Celery workers, K8s pods
DAG parsing overhead	None — flows are Python scripts, no continuous parsing	Significant — scheduler re-parses DAG files on interval

Prefect’s lighter architecture is a genuine advantage for self-hosting. There’s no DAG parsing loop, no separate scheduler process, and the server is a single FastAPI application. Airflow’s scheduler continuously reads the DAG directory, parses Python files, and updates the metadata database — which adds CPU overhead even when idle.

Community and Support

Aspect	Prefect	Apache Airflow
GitHub stars	20,000+	39,000+
License	Apache 2.0 / Prefect Community License	Apache 2.0
Backing	Prefect Technologies (VC-funded, offers Prefect Cloud)	Apache Software Foundation + Astronomer
Community	Slack community, Discourse forum	Massive — Slack, mailing lists, Airflow Summit conference
Job market	Growing but smaller	Industry standard for data engineering
Documentation	Good — tutorials, API reference, concepts	Excellent — extensive, mature
Provider ecosystem	50+ integration libraries	70+ provider packages, community operators
Managed offering	Prefect Cloud (free tier available)	Astronomer, MWAA (AWS), Cloud Composer (GCP)

Use Cases

Choose Prefect If…

• You want a modern, Pythonic API for defining workflows
• You need lighter self-hosted infrastructure (fewer containers, less RAM)
• You prefer running flows as normal Python code during development
• You want easier local development and testing (no Airflow boilerplate)
• You’re building a new data platform and don’t have Airflow lock-in
• You value a cleaner web UI with better observability

Choose Airflow If…

• You need the industry-standard tool with the largest ecosystem
• You’re hiring data engineers who already know Airflow
• You need advanced scheduling (data-aware triggers, complex timetables)
• You rely on specific provider packages (Spark, dbt, Snowflake operators)
• You need backfill with date range selection
• You want multiple managed hosting options as a fallback

Final Verdict

For self-hosting, Prefect has a real architectural advantage — it’s lighter, simpler to deploy, and doesn’t require the 5+ container stack that Airflow demands. The Pythonic decorator-based API is genuinely more pleasant to write than Airflow’s DAG boilerplate.

However, Airflow remains the safer pick for production data pipelines. Its ecosystem is unmatched, the community is massive, and every data engineer knows it. If you’re building a team or organization around data orchestration, Airflow’s ecosystem and hiring advantages matter.

For a self-hosted home lab or small team: Prefect. Lower resource usage, simpler deployment, modern API. For production data engineering: Airflow, unless your team actively prefers Prefect’s model and doesn’t need Airflow’s provider ecosystem.

FAQ

Is Prefect’s self-hosted server production-ready?

Yes, Prefect 3.x server is production-ready. However, Prefect Cloud offers features not available in the self-hosted version (push work pools, automations, RBAC). The self-hosted server covers scheduling, execution tracking, and the API — the core orchestration loop.

Can I migrate from Airflow to Prefect?

There’s no automated migration. Airflow DAGs need to be rewritten as Prefect flows. The Python logic transfers directly, but the orchestration wrappers (operators, hooks, sensors) need to be replaced with Prefect equivalents or native Python code. It’s a rewrite, not a migration.

Which handles more concurrent workflows?

Both scale horizontally. Airflow with Celery or Kubernetes executors handles thousands of concurrent tasks. Prefect with multiple workers achieves similar scale. For self-hosted single-server setups, Prefect uses less overhead per workflow due to its lighter architecture.

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