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Managed Apache Airflow Hosting
Programmatically author and schedule workflows
What is Apache Airflow on ManageStacks?
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.
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.
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About Apache Airflow
What Apache Airflow does, and why 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.
DIY vs ManageStacks
What running Apache Airflow yourself looks like — and what it looks like with us.
DIY self-hosting
- 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 — key numbers
$29/mo
Flat per application, regardless of DAG or task count
100+
Built-in operators (AWS, GCP, Snowflake, dbt, HTTP, S3)
Autoscale
Celery workers scale to queue depth automatically
Key features
Everything Apache Airflow ships with, running on our stack.
- 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
- Dynamic DAG generation for parameterised pipelines
- SLA monitoring, task alerting, and DAG-run history
- REST API + CLI for programmatic DAG deployment
- Full data export — DAGs, metadata database, task logs
How it deploys
From subscribe to live in minutes.
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.
Who this is for
Built for teams that want Apache Airflow to just work.
Data engineering teams
You run 20-500 DAGs — nightly Snowflake loads, dbt runs, ML training pipelines, external API syncs. MWAA is too expensive per environment; Astronomer is over-provisioned for your scale. ManageStacks is flat-priced Airflow with the ops burden lifted.
Analytics teams moving off cron
You've outgrown cron + shell scripts and need dependency-aware scheduling, retries, and observability. Airflow is the standard; ManageStacks removes the platform-engineering prerequisite.
ML platform teams
You need repeatable, monitored, dependency-tracked ML pipelines. Airflow orchestrates the training + evaluation + deployment steps; ManageStacks runs the Airflow underneath.
Compliance & compatibility
What we handle, what Apache Airflow runs on.
Compliance & operations
- Automated SSL/TLS via Let's Encrypt with custom-domain support
- Daily encrypted Postgres backups stored in a separate region
- Task-log persistence to S3, GCS, or Azure Blob with configurable retention
- OS-level security patches applied during your maintenance window
- GDPR data-residency — deployment stays in your chosen cloud region
- Airflow connections and variables encrypted at rest with Fernet
Compatibility
- Version
- Latest stable Airflow 2.x (validated before release); Airflow 3.x tracked
- Runtime
- Python 3.11 on containerised infrastructure
- Dependencies
- PostgreSQL 15, Redis 7, Celery workers, object storage for logs
- Min. resources
- 2 vCPU / 4 GB RAM (scheduler + webserver + 1 worker minimum)
How ManageStacks helps
We handle the parts you shouldn't be writing yourself.
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.
How it compares
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.
| Property | 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 |
Comparison focuses on architectural properties (deployment model, pricing basis, open-source status) that don't change with vendor pricing pages. Verify current pricing on each vendor's own site.
FAQ
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.
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Deploy Apache Airflow in under 5 minutes.
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