Managed Apache Airflow Hosting — production-ready from $15 a month
Programmatically author and schedule workflows. Deployed on your own dedicated instance in AWS, Azure, or GCP, kept patched, backed up, and monitored by ManageStacks — standard Apache Airflow, no lock-in.
Apache Airflow on ManageStacks is production-grade Airflow deployed to your own AWS, Azure, or GCP region — priced flat at $29 per application per month regardless of DAG count or task volume, with Celery workers, PostgreSQL metadata, Redis message broker, and Grafana monitoring pre-configured. Cheaper than MWAA or Astronomer once you're beyond a few dozen DAGs, and your data stays in your cloud region.
What does Apache Airflow do, and why do teams deploy it?
Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. Created at Airbnb, it has become the industry standard for data pipeline orchestration and is used by thousands of organisations for ETL, ML workflows, and batch processing.
Airflow uses directed acyclic graphs (DAGs) to define workflow orchestration. Tasks and dependencies are declared in Python, giving data engineers a flexible and extensible framework for anything from a nightly Snowflake load to a ML retraining pipeline. Built-in operators cover 100+ services (AWS, GCP, Snowflake, Databricks, Postgres, S3, HTTP, dbt).
Self-hosting Airflow means running a scheduler, a webserver, a metadata database (Postgres), a message broker (Redis or RabbitMQ), and a worker pool (Celery or Kubernetes). Add SSL, log storage, DAG deployment tooling, and version-upgrade discipline (Airflow 2.x → 3.x has real migration work) and you've built a small platform team. ManageStacks runs that platform team so yours doesn't have to.
- Python-based DAG definition with rich operator library
- Web UI for DAG monitoring, task retries, and log inspection
- 100+ built-in operators for AWS, GCP, Snowflake, Databricks, dbt, HTTP, S3
- Extensible with custom operators, hooks, and executors
- Built-in scheduling (cron + interval + external trigger)
- Celery workers with autoscaling based on queue depth
Programmatically author and schedule workflows
What does managed Apache Airflow hosting cost?
Flat per-app pricing, in your chosen AWS, Azure, or GCP region. No per-user pricing — a busy deployment costs the same as a quiet one.
Starter
Staging and internal tools. Dedicated instance, TLS, daily backups, managed upgrades.
Standard
Production workloads. Adds monitoring, staging environment, region choice, priority support.
Business
High-traffic and compliance workloads. Adds a high-availability replica and same-day support.
24×7 SRE retainer
Round-the-clock on-call across every hosted application, for teams that need a pager answered at 3am.
Self-hosting Apache Airflow vs managed — what does it really cost?
The software is free. The engineer-hours are not.
Running it yourself
- Stand up Postgres, Redis, scheduler, webserver, and workers on a Kubernetes cluster or EC2 fleet
- Configure log storage (S3), SSL, and DAG deployment tooling by hand
- Right-size Celery workers manually; add autoscaling scripts or Kubernetes HPAs
- Test 2.x → 3.x upgrade in staging; run metadata DB migrations; hunt breaking-DAG changes
- On-call for scheduler crashes, worker OOMs, and task-log gaps
On ManageStacks
- Subscribe through your AWS, Azure, or GCP marketplace
- Airflow comes up with Postgres, Redis, worker autoscaling, log storage, and SSL provisioned
- Grafana dashboards ship for scheduler heartbeat, queue depth, task success rate, and worker health
- New Airflow versions arrive as one-click updates after we validate migrations on your metadata clone
- Daily Postgres backups + object-storage log retention run in the background
Apache Airflow on ManageStacks vs the alternatives
How Airflow on ManageStacks compares to the two dominant managed-Airflow vendors and running Airflow on your own Kubernetes.
| Airflow on ManageStacksUs | AWS MWAA | Astronomer | Self-hosted on K8s | |
|---|---|---|---|---|
| Deployment | Managed on your AWS, Azure, or GCP | AWS-managed | Vendor-managed on any cloud | You provision + operate |
| Data residency | Your cloud region | AWS region | Vendor or your cloud | Your cloud region |
| Pricing basis | Flat per application | Per environment-hour | Per team + per deployment | Your compute cost |
| Ops burden | We run the platform | AWS runs the platform | Astronomer runs the platform | Your platform team runs it |
| Open source | Yes (Apache 2.0) | Yes (Apache, AWS-hosted) | Yes (Apache, hosted) | Yes (Apache 2.0) |
| Upgrade handling | We test + migrate metadata | AWS handles upgrades | Astronomer handles upgrades | You test + migrate metadata |
Provisioning, upgrades, backups and monitoring on your team’s plate.
What does running Apache Airflow yourself involve?
ManageStacks provisions Airflow with pre-configured Celery workers, PostgreSQL metadata database, Redis message broker, and Grafana monitoring. We handle scaling, database maintenance, upgrade testing, and DAG deployment tooling so your data team spends its time on pipelines rather than the Airflow platform underneath them.
Apache Airflow key numbers
How long from subscribing to a live instance?
Subscribe
Subscribe to ManageStacks through your AWS, Azure, or GCP marketplace.
Provision
Airflow spins up with scheduler, webserver, Celery workers, Postgres, Redis, log storage, SSL, and Grafana monitoring — typically 3-5 minutes.
Connect DAGs
Point Airflow at your DAG Git repo (GitHub, GitLab, Bitbucket). DAGs sync on push, no manual upload needed.
Run pipelines
Trigger DAGs from the web UI, the REST API, or on schedule. Autoscaling handles bursts; backups and monitoring keep running in the background.
When is self-hosting Apache Airflow the right answer instead?
“Managed hosting is not always the correct call.”
Self-host when a platform team already runs the infrastructure and on-call rotation to operate Apache Airflow at genuinely low marginal cost. Self-host when compliance requires an air-gapped or on-premises deployment that no hosted option can satisfy. And self-host when the deployment depends on heavy customisation with a fast internal build-deploy loop, because an internal release process will beat any managed change process.
For everyone else — teams whose engineers have better things to do than shepherd upgrades — managed hosting is cheaper than the hours it replaces.
Which cloud should Apache Airflow run on — AWS, Azure or GCP?
For most workloads, the choice of cloud matters less than proximity: run Apache Airflow in the same cloud and region as the applications and data it talks to, because every request between them adds a round trip. The underlying compute performs equivalently across AWS, Azure, and GCP.
In practice, an existing cloud footprint decides it. All plans support all three clouds, and moving regions later is a scheduled migration, not a rebuild.
Deepest managed-service catalog, default when there's no existing footprint
Best fit for teams already on Microsoft 365 or Entra ID
Strongest for data/analytics-adjacent workloads
Every plan supports AWS, Azure, and GCP — region choice included.
Common questions about Apache Airflow on ManageStacks
How long does it take to deploy Airflow on ManageStacks?
Under 5 minutes. ManageStacks provisions Airflow with Celery workers, PostgreSQL metadata, Redis message broker, and Grafana monitoring automatically. No Docker Compose, no Kubernetes cluster to stand up, no scheduler/webserver/worker orchestration to configure.
How does this compare to AWS MWAA or Astronomer?
MWAA is priced per environment-hour (~$400-2,000/month baseline) and Astronomer is per-team with Deployments as add-ons (starting ~$500-1,500/month). ManageStacks is a flat $29 per application per month. For most mid-market Airflow workloads (10-200 DAGs), self-hosted on ManageStacks is 5-20x cheaper. MWAA and Astronomer are worth it if you need vendor-managed Airflow specifically integrated with AWS/GCP data services out of the box.
Can I use custom Python packages and providers with Airflow on ManageStacks?
Yes. Custom Python packages install into the Airflow environment and persist across restarts. Airflow provider packages (Snowflake, Databricks, dbt Cloud, custom internal packages) all work. On Business plans we support custom base images for anything requiring OS-level libs.
Does ManageStacks handle Airflow version upgrades?
Yes — this is where ManageStacks pays for itself most. Airflow major upgrades (2.x → 3.x) involve database migrations and DAG-API changes. We test each release, run migrations in a staging clone of your metadata DB, flag any DAG-level breaking changes, and coordinate the cutover. One-click apply once we've validated.
How does Celery worker autoscaling work?
Workers scale based on Celery queue depth. During a burst (nightly batch of 500 tasks), workers spin up automatically; between bursts they scale back to save cost. You control the min/max worker count and per-worker resource envelope through the ManageStacks dashboard.
Can I deploy DAGs via CI/CD?
Yes. Point your DAGs at a Git repository (GitHub, GitLab, Bitbucket) and ManageStacks syncs on push — no manual DAG upload. Alternatively, use the Airflow REST API or the built-in DAG upload endpoint for scripted deploys.
Is task-log storage handled?
Yes. Task logs stream to your Airflow webserver in real time and are persisted to object storage (S3, GCS, or Azure Blob depending on your cloud) with configurable retention. No SSH-into-a-worker to find last night's log.
What happens to my DAGs and metadata if I leave?
Full export — DAGs from your Git repo (they're already yours), full Postgres metadata dump, task logs from object storage. Airflow is Apache-licensed, so you can spin it up anywhere. Migration off is a supported operation.
Run Apache Airflow without carrying the pager
Subscribe through your AWS, Azure, or GCP marketplace. We handle provisioning, SSL, monitoring, backups, updates, and security. From $15/mo.