Apache Airflow vs Google Cloud Workflows

Apache Airflow9.2Visit Google Cloud Workflows8.9Visit

Higher score Apache Airflow #2 in Workflow Orchestration Software 9.2/10 Free plan Free plan✓ 4 of 5 features Visit Apache Airflow

vs

Google Cloud Workflows #7 in Workflow Orchestration Software 8.9/10 Free plan Free plan✓ 4 of 5 features Visit Google Cloud

• Apache Airflow fits best

  Mature open-source data workflows

• Google Cloud Workflows fits best

  Google Cloud service orchestration

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Side by side

Feature	Apache Airflow 9.2/10 Visit ↗	Google Cloud Workflows 8.9/10 Visit ↗
At a glance
Editor score	9.2	8.9
Ranking	#2 in Workflow Orchestration Software	#7 in Workflow Orchestration Software
Best for	Mature open-source data workflows	Google Cloud service orchestration
Pricing model	Free	Free plan + paid
Starting price	Free plan	$0.01/unit/mo
Free plan	✓	✓
Free trial	—	—
Deployment	Self-hosted	Cloud
Platforms	Web	Web
Support	Community, Docs	Docs
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Apache Airflow 4/5 · Google Cloud Workflows 4/5
Visual workflow builder	—	—
Event triggers	✓	✓
Automatic retries	✓	✓
Execution observability	✓	✓
Human approvals	✓	✓
Specs
Deployment model	Self-hosted	Cloud
Our review
Pros	Python-based DAG authoring with dependencies and scheduling Event-driven execution, retries, monitoring, and execution logs Human approvals, provider packages, and REST API access	Ordered steps, branching, parallel execution, retries, and callbacks Eventarc triggers plus connectors for major Google Cloud products Step-level execution history, Cloud Logging, and Cloud Monitoring metrics
Cons	Self-hosted deployment requires teams to manage the environment No visual workflow builder for drag-and-drop authoring Python-based authoring may not suit teams avoiding code-defined workflows	Cloud-only deployment limits portability across environments Google Cloud-centric integrations may not suit multi-cloud teams YAML or JSON authoring lacks a visual workflow builder
Our verdict	Apache Airflow is an open-source workflow orchestration platform for teams that define and operate data pipelines, machine-learning workflows, infrastructure automation, and other batch-oriented processes. Workflows are authored in Python… Read the review →	Google Cloud Workflows is a fully managed orchestration service for defining and executing sequences across Google Cloud services, custom services, and HTTP APIs. It suits teams coordinating serverless applications, automated processes, IT… Read the review →

Strengths and trade-offs

• Apache Airflow — where it wins

  • Python-based DAG authoring with dependencies and scheduling
  • Event-driven execution, retries, monitoring, and execution logs
  • Human approvals, provider packages, and REST API access

  Where it doesn't

  • Self-hosted deployment requires teams to manage the environment
  • No visual workflow builder for drag-and-drop authoring
  • Python-based authoring may not suit teams avoiding code-defined workflows

• Google Cloud Workflows — where it wins

  • Ordered steps, branching, parallel execution, retries, and callbacks
  • Eventarc triggers plus connectors for major Google Cloud products
  • Step-level execution history, Cloud Logging, and Cloud Monitoring metrics

  Where it doesn't

  • Cloud-only deployment limits portability across environments
  • Google Cloud-centric integrations may not suit multi-cloud teams
  • YAML or JSON authoring lacks a visual workflow builder

• Apache Airflow9.2/10 · Free plan

  A mature self-hosted choice for teams building Python-based data workflows.

  Visit Apache AirflowFull verdict →
• Google Cloud Workflows8.9/10 · Free plan

  A low-cost managed workflow engine for coordinating Google Cloud services and APIs.

  Visit Google CloudFull verdict →