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Databases · head to head

Apache Airflow vs Longhorn

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Longhorn logo

Longhorn

Cloud

Open source distributed block storage for Kubernetes, incubating at the CNCF

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; Longhorn there is no vendor and no SLA, so a production incident at three in the morning is your own problem unless you buy SUSE Rancher Prime support separately.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Longhorn covers Per-volume controllers.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Apache Airflow and Longhorn differ
AttributeApache AirflowLonghorn
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, Kubernetes
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 Longhorn

  • Per-volume controllers
  • Synchronous replication
  • Snapshots and backups
  • Volume expansion
  • Disaster recovery volumes
  • Web interface

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

Longhorn

  • An on-premises Kubernetes cluster with local disks and no SAN that needs replicated persistent volumesnot Apache Airflow
  • Edge sites where shipping a storage array is impractical and three nodes is the whole clusternot Apache Airflow
  • A K3s deployment where the storage layer must be light enough to run alongside the workloadsnot Apache Airflow
  • A team that wants snapshots and S3 backups of persistent volumes without paying per-node storage licencesnot 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

Longhorn

  • There is no vendor and no SLA, so a production incident at three in the morning is your own problem unless you buy SUSE Rancher Prime support separately.
  • Synchronous replication across nodes means write latency depends on the slowest replica and the network between nodes, which makes it a poor fit for latency-sensitive databases.
  • Every replica is a full copy, so three-way replication consumes three times the raw capacity, unlike erasure-coded systems that are far more space efficient.
  • It is designed for block storage on modest clusters and does not scale to the node counts or throughput that Ceph or a commercial array handles, so growth eventually forces a migration.
  • Recovery from certain degraded states, such as a volume stuck detaching or replicas failing to rebuild, requires manual intervention and knowledge of Longhorn internals that is not widely held.

Pricing, plan by plan

Apache Airflow

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

Longhorn

Free
  • LonghornFree
    • Apache 2.0 licensed, no licence fee
    • Community support via GitHub and Slack only
    • No vendor SLA or escalation path

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 Longhorn if

  • You need per-volume controllers.
  • You want to start without paying.
  • You work on Linux, Kubernetes.
  • You also want synchronous replication.
Longhorn review

Questions people ask

Is Apache Airflow or Longhorn better?
Neither clearly leads. Apache Airflow starts at Free and Longhorn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Longhorn?
Apache Airflow starts at Free and Longhorn at Free.
Does Apache Airflow or Longhorn run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Longhorn runs on Linux, Kubernetes.
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 Longhorn is typically brought in for.
What can Apache Airflow do that Longhorn cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Longhorn covers Per-volume controllers, Synchronous replication, Snapshots and backups, Volume expansion.

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.

Longhorn: Who do we call when it breaks?

Nobody, unless you buy SUSE Rancher Prime, which includes commercial support for Longhorn. This is the decisive question for production use.

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.

Longhorn: How much capacity does replication cost?

Full copies, so three replicas means three times the raw capacity. Budget accordingly rather than assuming erasure coding efficiency.

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.

Longhorn: Is it suitable for production databases?

For modest workloads yes, but synchronous replication adds write latency and high-transaction databases usually want something faster.

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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