Both are managed Apache Airflow services. MWAA wins for AWS-native workloads with simpler pricing. Cloud Composer wins for GCP-native workloads and supports Airflow 2.x features earlier.
Introduction
### Managed Airflow: AWS vs. GCP
Both **Amazon MWAA (Managed Workflows for Apache Airflow)** and **Google Cloud Composer** provide fully managed Apache Airflow environments — eliminating the pain of running Airflow's scheduler, webserver, workers, and metadata database yourself.
**Amazon MWAA** launched in 2020 and provides a straightforward managed Airflow experience. Your DAG files live in S3, requirements are installed automatically, and MWAA handles scaling workers. It integrates natively with AWS services (S3, Redshift, Glue, Lambda, EMR) and uses VPC for networking.
**Google Cloud Composer** (based on Airflow running on GKE) was the first managed Airflow offering (2018) and is generally ahead on Airflow version support. It integrates natively with GCP services (BigQuery, Dataflow, GCS, Vertex AI) and offers both Composer 1 (Classic) and Composer 2 (Autopilot, with better scaling).
**Choosing between them is almost always determined by which cloud you're on.**
Feature Comparison
Feature
Amazon MWAA
Google Cloud Composer
Winner
Underlying Infrastructure
AWS Fargate (containers)
Google Kubernetes Engine (GKE)
Tie
DAG Storage
S3 bucket
GCS bucket
Tie
Auto-Scaling
Worker auto-scaling (min/max workers)
Composer 2 Autopilot (GKE-based auto-scaling)
Cloud Composer
Airflow Version Support
Slightly behind on latest Airflow versions
Generally ahead on version support
Cloud Composer
Pricing Simplicity
Environment class-based (simple tiers)
Component-based (compute + database + storage)
MWAA
Cloud Integration
S3, Redshift, Glue, Lambda, EMR, SageMaker
BigQuery, Dataflow, GCS, Vertex AI, Pub/Sub
Tie
✅ Amazon MWAA Pros
Simpler pricing model based on environment class
VPC networking for secure private DAGs
Native integration with AWS services
Plugins folder in S3 for custom operators
Straightforward setup with minimal configuration
⚠️ Amazon MWAA Cons
Can be slow to adopt latest Airflow features
Limited customization compared to self-hosted
Environment updates can require recreation
Worker scaling can be slow during burst workloads
✅ Google Cloud Composer Pros
First to market — more mature managed Airflow offering
Composer 2 Autopilot provides better auto-scaling
Generally faster Airflow version adoption
GKE-based: can customize Kubernetes resources
Triggerer support for deferrable operators
⚠️ Google Cloud Composer Cons
More complex pricing (many individual components)
Composer 1 is being deprecated (migration needed)
GKE underlying complexity sometimes leaks through
Environment creation is slow (15-30 minutes)
Final Verdict
### Verdict
**Choose MWAA if:**
* Your data infrastructure is on AWS
* You want the simplest managed Airflow experience
* Your DAGs use AWS services (S3, Redshift, Glue, EMR)
* You prefer simpler, predictable pricing
**Choose Cloud Composer if:**
* Your data infrastructure is on GCP
* You want the latest Airflow features first
* Your DAGs use GCP services (BigQuery, Dataflow, GCS)
* You need better auto-scaling (Composer 2 Autopilot)
**Note:** If you're considering alternatives to managed Airflow entirely, evaluate [Astronomer](https://www.astronomer.io/) (cloud-agnostic managed Airflow) or modern orchestrators like Prefect and Dagster.