Airflow Hosting Live In 5 Minutes

Deploy Airflow hosting and run a self-hosted workflow orchestration platform for your Python data pipelines, with the web UI and REST API on your own domain. xCloud starts the API server, scheduler, DAG processor, triggerer and a PostgreSQL metadata database, generates the admin password and secret keys, and keeps your Dags, logs and plugins on persistent volumes behind HTTPS, so your first pipeline can run within minutes.

Deploy your Airflow

No terminal
No technical skills needed
No lock-in, cancel anytime

What is Airflow?

Apache Airflow is an open-source platform to programmatically author, schedule and monitor workflows, released under Apache-2.0. You define workflows as code, called Dags, and the scheduler runs their tasks while following the dependencies you set. Upstream says it works best with workflows that are mostly static and slowly changing. Self-hosted Airflow is the option for teams that would otherwise use a hosted service such as Astronomer or Amazon MWAA.

On xCloud the template runs the official apache/airflow 3.3.2 image, pinned by digest, as an API server, scheduler, DAG processor and triggerer, with a PostgreSQL 16 metadata database on the private network and the LocalExecutor, so there is no Redis or Celery worker. Dags, logs, plugins and the database sit on four volumes. Treat admin access and write access to the dags volume like shell access.

From Sign-Up to a Live Airflow Install In Just 3 Easy Steps

Self-hosting Airflow normally means assembling five services, a metadata database, a Fernet key and HTTPS before a single DAG can run. This is the same result without assembling any of it.

Step 01
Sign Up
Create your xCloud account, pick Airflow from the OneClick catalog, and choose a domain. xCloud generates the admin password and secret keys.
Step 02
We Deploy It For You
One click. We bring up the API server, scheduler, DAG processor, triggerer and a PostgreSQL metadata database, and publish the web UI and REST API over HTTPS. First start takes 2 to 5 minutes.
Step 03
Sign In And Load Your First DAG
Open the HTTPS Airflow URL, sign in as admin with the generated password, then copy your first DAG file into the dags volume — new files can take up to 5 minutes to appear.

Everything Airflow Does, On Your Own Server

Airflow runs in full here — pipeline authoring, scheduling and monitoring, on infrastructure you control.

Author Pipelines As Python DAGs
Write your data pipelines as Python DAGs and schedule them with the LocalExecutor.
Web UI And REST API
Trigger runs and read task logs from the Airflow web UI or script the same things through the REST API.
The Full Stack, One Install
API server, scheduler, DAG processor, triggerer and a PostgreSQL metadata database are all wired together automatically.
Load DAGs Your Way
Copy files into the dags volume directly, or configure a Git DAG bundle in the environment editor.
Generated Admin Login, No Public Sign-Up
A generated admin password and secret keys are created for you; unauthenticated API calls and wrong passwords are rejected.
DAGs And History Survive A Restart
DAGs, run history, logs, plugins and the metadata database all survive an xCloud Restart.

Everything You Need To Run Your Pipelines 24/7

Managed hosting for Airflow, so the parts that keep it online are not your problem.

Airflow 3.3.2 deployed instantly
Scheduler, DAG processor and triggerer included
PostgreSQL metadata database included
Generated admin login
Free SSL certificate
Server backups and snapshots
Server security updates included
30+ server locations
Unused balance refunded

What You Can Do With Airflow

Schedule nightly data pipelines
A data team writes each pipeline as a Dag, sets its schedule and task dependencies, and lets the scheduler run it every night, with run history and task logs kept in the web UI and on the logs volume.
Retry and rerun failed tasks
When a task fails, an engineer opens the run in the web UI, reads the task log and reruns it, instead of re-running a cron script by hand and hunting through server logs.
Orchestrate machine learning steps
Upstream says Airflow is used to orchestrate training, retraining, evaluation and deployment. An ML team can chain those steps as one Dag and track every run in one place.
Load Dags from a Git repository
Point the Dag bundle setting at a Git repository so pipelines are versioned and reviewed in your usual workflow, then let the Dag processor pick up changes on its refresh interval.

Airflow vs Alternatives

Astronomer: If you run Airflow on a hosted service such as Astronomer today, this template runs the same open-source Airflow on your own Cloud VPS, with the metadata database and Dags on volumes you control and back up.
Amazon MWAA: If your Airflow runs on Amazon MWAA, this template is the version you operate yourself. It uses the LocalExecutor with no Redis or Celery worker, so it fits a single Cloud VPS rather than a worker fleet.
Prefect Server: Prefect Server has its own xCloud page, so you can compare the two templates. Airflow defines workflows as Python Dags that the scheduler runs, and upstream says it fits workflows that are mostly static and slowly changing, which is the case to weigh.
Dagster: Dagster has no OneClick page here. Airflow's template comes with a web UI, REST API, scheduler, Dag processor and PostgreSQL wired together, so you can load your first Dag after signing in.

Simple Pricing. Everything Included.

Dedicated Cloud VPS sizes, pre-configured for Airflow hosting. Pick the one that fits, and resize any time.

Cloud VPS 6 GB

Below the template's documented floor — not recommended.

$24.99per month

6 GB RAM
4 vCPU cores
100 GB NVMe SSD
30 TB
Unlimited

Get started

Cloud VPS 16 GB

Cloud VPS 24 GB

Airflow alongside heavier, longer-running DAGs.

$84.99per month

24 GB RAM
8 vCPU cores
300 GB NVMe SSD
30 TB
Unlimited

Get started

Every size is a dedicated Cloud VPS we provision, patch, monitor and restore. Billed monthly with no lock-in — delete the server and the unused balance comes back to your account. Airflow itself is licensed under Apache License 2.0.

Docker hosting

Already Have a Server? Bring Your Own

Connect any VPS — DigitalOcean, Vultr, Linode, AWS, Hetzner or bare metal — and deploy Airflow on it through the same panel, for a per-server management fee instead of a Cloud VPS rental.

Free

Start your journey without cost.

$0forever

1 server
10 sites

Start free

Starter

Professional

The same platform, discounted at 6–10 servers.

$4per server / month

6–10 servers
Unlimited sites
All features included
Bring any VPS provider

Get started

Agency

The same platform, discounted at 11+ servers.

$3per server / month

11+ servers
Unlimited sites
All features included
Bring any VPS provider

Get started

Priced per connected server, so your rate adjusts automatically as you add or remove servers. You pay your own provider for the server itself.

Self-managed hosting

Ready To Own Your Data Pipelines?

Airflow on xCloud runs on a dedicated Cloud VPS that we provision, patch, monitor and restore. Deploy faster. Own the server. Stay in control.

Fast Provisioning
Deploy faster using an xCloud Managed Server built with optimized stacks and high-speed SSDs.
Enterprise-grade security
Protect your infrastructure with a free SSL certificate and more advanced security features at no additional cost.
Intuitive Control Panel
From Server creation to application deployment, everything can be managed from our intuitive control panel.

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Frequently Asked Questions

Feel free to contact us with any questions or concerns you may have.

What is Apache Airflow used for?

Apache Airflow is used to author, schedule and monitor workflows defined as Python code. Teams run data pipelines on it, and upstream says it is also used to orchestrate machine learning steps. You write a Dag, set the task dependencies, and watch runs, logs and retries from the web UI or the REST API.

Is Apache Airflow free?

Yes. Apache Airflow is open-source software under the Apache License 2.0, so there is no licence fee. On xCloud you pay for the dedicated Cloud VPS that runs the five containers and the deployment around them. Preserve the upstream licence notices if you redistribute the image or your own changes.

How much RAM does Airflow need?

The template manifest asks for 4096 MB of RAM, 2 CPU cores and 10 GB of disk, following upstream's documented minimum for Docker Compose. QA measured about 1.3 GB peak with one small Dag. This page sells the 16 GB Cloud VPS, which leaves wide headroom for heavier or longer-running Dags.

Can you run Airflow with Docker?

Yes. For Airflow Docker hosting the template runs the official apache/airflow 3.3.2 image for every component, pinned by digest, plus a private PostgreSQL 16 container, and mounts four named volumes. It generates the admin password, Fernet key and secret keys, and puts HTTPS on your domain.

How do I add Dags to Airflow?

Copy Python files into the dags volume, for example with docker cp into the dag-processor container, or load them from Git by setting the Dag bundle list in the environment editor. A new file can take up to 5 minutes to appear, because the Dag processor scans every 300 seconds by default.

How do I upgrade Airflow?

Airflow 3.3.2 is pinned by digest, so existing installs do not change when the template does. Read the upstream release notes, back up all four volumes and the Fernet key, then move to a newer template version that bumps the image tag and digest together. Check that your Dags still parse afterwards.

How do I back up the Airflow metadata database?

An Airflow backup covers four volumes together: postgres_data holds the metadata, connections and run history, while airflow_dags, airflow_plugins and airflow_logs hold your files. Store the Fernet key with the backup, because stored connection passwords cannot be decrypted without it. Test a restore.

Backups in xCloud

Is Airflow secure to expose to the internet?

There is no public sign-up, and only the health check answers without signing in. But Dag files run as arbitrary Python in the containers, so admin access is like shell access. Basic Auth in front is optional and not enabled by the template; add it or restrict the firewall for a second gate.

Configure firewall management

Can I rotate the Fernet key?

Not safely once connections are stored. The Fernet key encrypts the passwords of stored connections, so changing it makes them unreadable. The JWT and API secret keys can be rotated, which only signs everyone out. Keep the Fernet key from your credentials panel with your backups.

How do I sign in, and reset the admin password?

Sign in as admin with the generated 24-character password in the credentials panel. Editing the password variable later does not change the login. Reset it by running airflow users reset-password for the admin user inside the api-server container, as the template's environment notes describe.

Can I run Airflow in the cloud on xCloud?

Yes. For Airflow cloud use, xCloud sets up a dedicated Cloud VPS, deploys the API server, scheduler, Dag processor, triggerer and PostgreSQL on it and attaches your domain and certificate. A machine you already run elsewhere can be attached as a self-managed server and the stack deploys onto it.

Can I cancel anytime?

Yes. There is no lock-in — cancel any time. Hosting is billed monthly and charged for what you actually use, so when you delete the server the unused balance is refunded to your account, less up to 10% in payment-processing fees.