Astro vs. Google Managed Service for Apache Airflow (formerly Cloud Composer)

Airflow without the always-on tax.

Book a Demo See How Astro Compares ↓

Trusted by 900+ engineering teams

WHY ASTRO?

The same Airflow. None of the overhead.

Built to run Apache Airflow reliably at scale, out of the box, without the infrastructure upkeep and rebuild delays Google's Managed Airflow still requires.

Spend less time waiting on infrastructure

Routine changes ship in minutes instead of queuing behind an environment rebuild, so your team spends more of its week building pipelines.
Never pay for idle workers

Workers scale down automatically when there's no task to run; non-prod environments can also be scheduled to pause overnight or on weekends
Fewer late-breaking surprises

Changes are tested before they ship, not discovered after they're already live, with built in CI/CD and local dev tools
Isolated environments for every team

Spin up a new environment in minutes, with its own code, data, and compute, and no new cluster to provision.

COMPARE YOUR OPTIONS

Astro vs. Google Managed Service for Apache Airflow (formerly Cloud Composer)

Astro

Google Managed Airflow

Build faster & better

Local development

Astro CLI or in-browser Astro IDE to build, test, and debug pipelines before they go live — no messy local Docker setup

No local emulator; Dags are commonly tested by uploading to the environment's storage bucket and waiting for the scheduler to sync

AI-powered Dag authoring

Otto drafts and explains Dags using your actual code and history — not generic suggestions

A separate no-code pipeline builder targets non-engineers; for teams writing real Python Dags, authoring remains manual with no Airflow-specific knowledge base

CI/CD & deployment

Git-based deploys via the Astro CLI, API, or Terraform, with no environment rebuild required; dependency changes test locally first, so a version conflict shows up on your laptop, not mid-update

Manual upload of Dag files to a storage bucket, with no native CI/CD; a separate declarative pipeline builder exists for non-engineers but doesn't cover Python Dag teams

Access to latest Airflow

Guaranteed, near-immediate access to every new Airflow release, as a fully supported production version

Airflow 3 is available but still in Preview; Airflow 2 remains what Google recommends for production, and new versions have historically taken months to reach the platform after Astro already supports them

Run with confidence & efficiency

Multitenancy

Isolated environments per team, with separate code, data, and compute, so one team's mistake can't touch another's

Each team needs its own full environment and cluster, so isolating multiple teams multiplies both infrastructure and cost

Scaling & idle cost

Usage-based across compute and workers; workers scale down automatically when idle, and hibernation pauses unused dev environments entirely

Environment fee, compute, metadata database storage, and network egress are all billed continuously regardless of load — a small environment typically costs $400+ a month minimum, even sitting idle — and Google publishes separate guidance for manually tuning scheduler and worker resources as Dag count grows

Task execution

The Astro Executor runs tasks directly, avoiding common message-broker failure modes; Celery and Kubernetes executors are also available if you need them

Runs on standard Celery or Kubernetes executors on the underlying GKE cluster, with no equivalent purpose-built executor available

Support

24/7 support from the engineers who build Apache Airflow, backed by an SLA that covers Airflow itself

Google Cloud supports the infrastructure only, with no SLA covering Airflow — bugs and scheduler issues escalate to the open-source community

Upgrades

One-click upgrades with a local test step first, and rollback to any deploy from the last 90 days; Otto can also help plan and validate the upgrade itself

Any environment change, including a version upgrade, triggers the same ~25-minute rebuild; larger version jumps can require standing up a new environment entirely

Remote/hybrid execution

Remote Execution Agents let the task itself run on-prem or in another cloud, while Astro handles scheduling and orchestration

Dags can call out to other clouds or on-prem systems through operators, but the task still executes on Google's infrastructure inside a single GCP project and VPC — there's no way to run the task itself elsewhere

Observe & optimize

Pipeline observability

Real-time lineage and SLA monitoring show you why a pipeline failed, not just that it failed

Cloud Monitoring and Cloud Logging cover infrastructure only, with no pipeline lineage or SLA context

AI-assisted investigations

Otto traces failures to root cause using Astronomer's own history of Airflow-specific failure patterns and your team's setup

A built-in investigation tool analyzes logs and suggests fixes using general pattern recognition, without Airflow-specific failure history or memory of your environment

CUSTOMER STORIES

Data & AI Teams trust Astro

"Composer added friction with slow environment setup, limited flexibility for CI/CD, and long lead times for Airflow upgrades... it created a huge burden on the team. Astronomer offered a smoother setup, faster testing, and better CI/CD integration than Google Cloud Composer. We could finally focus on building pipelines instead of managing infrastructure."

Miko Chen Lead Data Engineer

60%+

faster pipeline development

50%

less infra management time

600+

DAGs orchestrated

READ STORY →

"We're expanding Astronomer across the whole firm, and the ambition only grows from here. Astro gives every data engineering team the same foundation we built on, and Otto takes the overnight operational burden off the plate entirely so engineers can focus on building better pipelines and better models, not firefighting at 8 AM."

Dipesh Kataria Director of Quantitative Development, Janus Henderson Investors

95%

faster MTTR

8 weeks

to production ready autonomous remediation

READ STORY →

"Bringing our first in-house agentic AI solutions to market is an exciting milestone and, together with our latest AI innovations, we're making strides on delivering new levels of personalization and a faster, more seamless platform."

James Waters Chief Business Officer

1,000s

DAGs running in production

1,000s

data practitioners building on Astro

READ STORY →

See Astro in action.

Discuss your migration with the Airflow experts and find out what a fully-managed platform can do for your team.

FREQUENTLY ASKED QUESTIONS

Do we have to migrate everything at once?

No. Astro runs the same open-source Apache Airflow as Google's Managed Airflow (formerly Cloud Composer), so most teams move workloads over incrementally, environment by environment.

Will our Dags need to be rewritten?

In most cases, no — Dag code is largely portable between the two. Migration work is typically in connections, secrets, and networking/IAM config; GCP-specific hooks or custom plugins should be reviewed.

How does Astro's pricing compare to Cloud Composer?

Astro is usage-based with workers that scale to zero when idle. Google's Managed Airflow bills on Data Compute Units, but the underlying cluster keeps running (and costing money) even when idle. Interested in a tailored comparison? Talk to us.

Will our Astro spend count toward our GCP commitment?

Yes. Astro is available on the Google Cloud Marketplace, so it's billed through your existing GCP account and counts toward your committed spend, with no separate procurement process.