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Databases · head to head

Apache Airflow vs Atlantis

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Atlantis logo

Atlantis

Developer Tools

Runs Terraform plan and apply from pull request comments, self-hosted and free

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; Atlantis there is no company, no service level agreement and no paid support at any price, and the six maintainers are volunteers with day jobs, which some risk committees will not accept for a component holding production cloud credentials.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Atlantis covers Pull request plans.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Atlantis differ
AttributeApache AirflowAtlantis
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
CategoryDatabasesDeveloper Tools

Identical on both: starting price (Free), free tier (Yes), platforms (Linux, Docker, Kubernetes, Self-hosted), 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 Atlantis

  • Pull request plans
  • Comment driven apply
  • Workspace locking
  • Policy checking
  • Multi-platform webhooks
  • Custom workflows

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

Atlantis

  • Making Terraform review meaningful by putting the actual plan output in front of the approvernot Apache Airflow
  • Removing local applies and the credential sprawl that comes with every engineer holding production keysnot Apache Airflow
  • Getting pull request driven infrastructure without paying a per-user subscription for a hosted platformnot Apache Airflow
  • Enforcing Conftest policies as a blocking check before an apply can runnot 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

Atlantis

  • There is no company, no service level agreement and no paid support at any price, and the six maintainers are volunteers with day jobs, which some risk committees will not accept for a component holding production cloud credentials.
  • The security surface is inherently sharp, because it is an internet-reachable webhook endpoint that executes applies with privileged credentials, gated by pull request comment authorisation that is coarse next to a real policy engine.
  • It is stateful, holding a working directory and lock database on local disk, so high availability and horizontal scaling are awkward and you own upgrades, webhook plumbing, secret rotation and the host itself.
  • It lacks the features that distinguish the commercial alternatives, with no native drift detection, no cost estimation, no managed state interface and no policy engine beyond the Conftest integration.
  • Terragrunt and monorepo layouts are not first-class, requiring hand-written custom workflows and repository configuration that becomes a growing maintenance burden as the number of repositories increases.

Pricing, plan by plan

Apache Airflow

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

Atlantis

Free
  • AtlantisFree
    • Apache-2.0, no usage limits and no seat count
    • No commercial edition and no paid support tier
    • Community support through GitHub and the CNCF Slack channel

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 Atlantis if

  • You need pull request plans.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want comment driven apply.
Atlantis review

Questions people ask

Is Apache Airflow or Atlantis better?
Neither clearly leads. Apache Airflow starts at Free and Atlantis at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Atlantis?
Apache Airflow starts at Free and Atlantis at Free.
Does Apache Airflow or Atlantis run on more platforms?
Both run on Linux, Docker, Kubernetes, Self-hosted, so platform support will not decide this one for you.
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 Atlantis is typically brought in for.
What can Apache Airflow do that Atlantis cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Atlantis covers Pull request plans, Comment driven apply, Workspace locking, Policy checking.

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.

Atlantis: Is there a company behind Atlantis?

No. It runs as a series of LF Projects under the Linux Foundation and is maintained by volunteers. There is no vendor to buy support from and no service level agreement.

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.

Atlantis: Is it a CNCF project?

No. It uses a channel in the CNCF Slack, which is often misread as membership, but the project sits under LF Projects rather than the CNCF.

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.

Atlantis: Does it work with OpenTofu?

Yes, there is a dedicated integration guide alongside Terraform, and it also has configuration for working with hosted Terraform backends.

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.

Atlantis: What does it not do that a paid platform does?

Drift detection, cost estimation, a managed state and run interface, and a real policy engine. Atlantis does pull request plan and apply well and stops there.

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