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Databases · head to head

Apache Airflow vs Coolify

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Coolify logo

Coolify

Cloud

Open source self-hosted platform that deploys applications and databases to servers you already own

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; Coolify commit history since June 2026 shows one maintainer at 73 commits and a second at 15 with nobody else above one, and the same two people also ship four other coolLabs products, so the project depends on a very small number of individuals.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Coolify covers Git push deployments.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Coolify differ
AttributeApache AirflowCoolify
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb, Linux, Docker, Self-hosted
CategoryDatabasesCloud

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 Coolify

  • Git push deployments
  • One-click services
  • Automatic certificates
  • Managed databases
  • Docker Compose support
  • Notifications

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 Coolify
  • Coordinating machine learning training and evaluation runsnot Coolify
  • Orchestrating dbt runs alongside extraction and loadingnot Coolify
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Coolify

Coolify

  • Replacing a managed platform bill with a single Hetzner or DigitalOcean instance running a dozen small applicationsnot Apache Airflow
  • An agency hosting many low-traffic client sites on shared servers with certificates handled automaticallynot Apache Airflow
  • Self-hosting analytics, databases and internal tools from the one-click catalogue without writing Compose filesnot Apache Airflow
  • Deploying inside a country or jurisdiction where no managed platform operates a regionnot 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

Coolify

  • Commit history since June 2026 shows one maintainer at 73 commits and a second at 15 with nobody else above one, and the same two people also ship four other coolLabs products, so the project depends on a very small number of individuals.
  • Multi-server support is documented as experimental, load balancer configuration is manual, every server must share the same CPU architecture, and moving images between hosts requires you to run your own Docker registry.
  • In self-hosted mode the Coolify machine holds every SSH key and secret with no documented high availability, so the dashboard host is a single point of failure that the paid Cloud tier fixes and self-hosting does not.
  • Scheduled S3 backups cover PostgreSQL, MySQL, MariaDB and MongoDB only, so application persistent volumes are your responsibility and a restore is untested unless you test it yourself.
  • Release cadence is fast enough that six patch versions shipped inside ten days in August 2026 against several hundred open issues, and because instances can update themselves a regression can reach a production host before you have read the changelog.

Pricing, plan by plan

Apache Airflow

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

Coolify

Free
  • Self-hostedFree
    • Apache-2.0, no features held back
    • Unlimited servers, applications and users
    • You run, update and back up the dashboard yourself
  • Cloud$5/month
    • Connects 2 servers, then 3 US dollars a month per extra server
    • Managed and updated Coolify dashboard with high availability
    • Managed backups of the Coolify instance itself

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 Coolify if

  • You need git push deployments.
  • You want to start without paying.
  • You work on Web, Linux, Docker, Self-hosted.
  • You also want one-click services.
Coolify review

Questions people ask

Is Apache Airflow or Coolify better?
Neither clearly leads. Apache Airflow starts at Free and Coolify at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Coolify?
Apache Airflow starts at Free and Coolify at Free.
Does Apache Airflow or Coolify run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Coolify runs on Web, Linux, Docker, Self-hosted.
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 Coolify is typically brought in for.
What can Apache Airflow do that Coolify cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Coolify covers Git push deployments, One-click services, Automatic certificates, Managed databases.

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.

Coolify: Is anything held back for the paid tier?

No. Coolify Cloud hosts, updates and backs up the dashboard and gives it high availability. Every feature that touches your applications is in the Apache-2.0 build you can run yourself.

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.

Coolify: What does Coolify Cloud cost?

Five US dollars a month connects two servers, and each additional server is three dollars a month, with 20 per cent off for annual billing. You still supply and pay for the servers themselves.

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.

Coolify: Does it back up my application data?

It backs up PostgreSQL, MySQL, MariaDB and MongoDB to S3 on a schedule. Application persistent volumes are not covered, so anything stored outside a managed database needs your own backup plan.

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.

Coolify: Can I run it across several servers?

Yes, but the documentation calls multi-server experimental. Load balancing is not configured for you, all nodes must share a CPU architecture, and you need to run your own Docker registry.

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