---
url: "https://xcademia.com/news/how-checkout-com-reduced-airflow-costs-by-30-with-google-cloud-composer-3"
title: "How Checkout.com Reduced Airflow Costs by 30% with Google Cloud Composer 3"
description: "Discover how Checkout.com migrated to Google Cloud Composer 3 to reduce Airflow costs by 30%, improve reliability, automate scaling, and accelerate data engineering workflows."
publishedAt: "2026-07-23T10:24:16.676+00:00"
updatedAt: "2026-07-23T10:29:16.67371+00:00"
type: news
category: devops
source_name: Google Cloud Blog
source_url: "https://cloud.google.com/blog/products/data-analytics/how-checkout-com-tallies-data-with-cloud-composer-3"
tags:
  - "#GoogleCloud"
  - "#CloudComposer3"
  - "#ApacheAirflow"
  - "#DataEngineering"
  - "#DataAnalytics"
  - "#CloudComputing"
  - "#CheckoutCom"
  - "#DevOps"
---

# How Checkout.com Reduced Airflow Costs by 30% with Google Cloud Composer 3

> Checkout.com migrated from a self-managed Apache Airflow environment to Google Cloud Composer 3, improving reliability, enabling automatic scaling, accelerating developer workflows, and reducing monthly infrastructure costs by an estimated 30%.

Source: **Google Cloud Blog** · 23 July 2026

## From Infrastructure Maintenance to Data Innovation

Managing modern data platforms involves more than building data pipelines. Once an orchestration platform is deployed, data engineering teams often face a new challenge: maintaining the infrastructure that keeps those pipelines running.

For many organizations using self-managed Apache Airflow, routine tasks such as server maintenance, software patching, dependency updates, and incident response consume valuable engineering time that could otherwise be spent delivering new data capabilities.

Checkout.com encountered this exact challenge. By migrating from a self-managed Apache Airflow environment on another cloud provider to **Google Cloud's Managed Service for Apache Airflow (Gen 3), also known as Cloud Composer 3**, the company modernized its orchestration platform, improved operational reliability, accelerated developer productivity, and reduced monthly infrastructure costs by an estimated 30%.

The migration demonstrates how managed workflow orchestration can help organizations shift their focus from infrastructure maintenance to innovation.

## The Challenge of Managing Self-Hosted Apache Airflow

## Apache Airflow has become one of the industry's most widely adopted workflow orchestration platforms for scheduling, monitoring, and managing data pipelines. While highly flexible, operating Airflow at enterprise scale introduces significant operational complexity.

Before adopting Cloud Composer 3, Checkout.com managed its own Airflow infrastructure. Although the environment successfully supported its workloads, maintaining the underlying platform required continuous engineering effort.

Routine operational responsibilities included:

- Managing infrastructure and application servers
- Applying security patches and software updates
- Coordinating Airflow upgrades
- Resolving dependency compatibility issues
- Responding to production incidents
- Scaling worker resources during peak demand

Instead of investing time in building new data pipelines and analytics capabilities, engineers increasingly found themselves maintaining the orchestration platform itself.

As workloads continued to grow, these operational demands became more noticeable across multiple areas of the data platform.

**Reducing Operational Friction**

The self-managed environment experienced stability challenges during periods of heavy workload. Resource contention occasionally affected platform reliability, increasing the amount of operational attention required to maintain consistent performance.

**Complex Dependency Management**

Keeping Python packages, Airflow components, and third-party libraries compatible required careful planning and manual testing.

Every upgrade introduced the possibility of dependency conflicts, making routine maintenance both time-consuming and potentially disruptive.

**Slow DAG Synchronization**

Another limitation involved deployment speed.

After deploying Directed Acyclic Graphs (DAGs) to Amazon S3, synchronization with the scheduler typically required around six minutes.

Although seemingly minor, this delay slowed development iterations, debugging, and testing throughout the engineering lifecycle.

**Manual Scaling**

Scaling the environment also required manual intervention.

Rather than dynamically allocating resources based on workload, infrastructure was provisioned to handle expected peak traffic. This meant the platform often ran with more worker capacity than necessary, increasing operational costs.

**Time-Consuming Team Onboarding**

Adding new engineering teams required additional platform administration.

Platform engineers manually created variables, secrets, and dbt configuration before new projects could begin, creating unnecessary onboarding delays.

Together, these challenges shifted engineering focus away from delivering business value and toward maintaining infrastructure.

## Why Modern Data Platforms Need Managed Orchestration

As organizations process larger volumes of data and support more business-critical analytics workloads, workflow orchestration platforms become increasingly central to day-to-day operations.

However, maintaining orchestration infrastructure internally creates an ongoing operational burden.

Engineering teams must continuously balance:

- Infrastructure reliability
- Platform security
- Performance optimization
- Software compatibility
- Cost management
- Developer productivity

Each new Airflow version introduces additional maintenance work, while increasing workloads require ongoing capacity planning and monitoring.

For organizations focused on delivering data products rather than operating infrastructure, managed orchestration services can significantly reduce this operational overhead.

Instead of maintaining the platform itself, engineers can spend more time improving data quality, developing analytics pipelines, and supporting business teams.

Recognizing these benefits, Checkout.com chose to migrate to Google Cloud's Managed Service for Apache Airflow (Cloud Composer 3).

![info-1](https://0a515t3ure77wbvx.public.blob.vercel-storage.com/articles/1784801834854-info-1--50-.webp)

## Migrating to Cloud Composer 3

Rather than continuing to invest engineering effort into infrastructure maintenance, Checkout.com adopted **Managed Service for Apache Airflow (Cloud Composer 3)**.

Cloud Composer 3 is Google Cloud's fully managed Airflow service, designed to automate much of the operational work traditionally associated with running Airflow environments.

Instead of maintaining infrastructure manually, Google Cloud manages:

- Environment provisioning
- Security patching
- Airflow upgrades
- Managed operations
- Dynamic worker scaling

This allows engineering teams to focus on designing, deploying, and improving workflows instead of managing the orchestration platform itself.

For Checkout.com, the migration produced measurable improvements across three critical areas:

- Infrastructure reliability
- Cost efficiency
- Developer productivity

## Dynamic Scaling Reduced Infrastructure Costs

One of the most significant improvements came from Cloud Composer 3's built-in dynamic scaling capabilities.

Previously, Checkout.com's infrastructure was configured around maximum expected demand.

To ensure sufficient processing capacity during busy periods, the environment continuously ran enough workers to handle peak workloads even when those resources were largely idle.

While this approach guaranteed capacity, it also meant paying for infrastructure that wasn't always being used.

Cloud Composer 3 replaced this fixed-capacity model with automatic scaling.

The service continuously monitors workload demand and adjusts the number of workers accordingly.

When workflow activity increases, additional workers are automatically provisioned.

When workloads decrease, unnecessary workers are removed, reducing infrastructure consumption without requiring manual intervention.

This intelligent resource management enables organizations to align infrastructure usage more closely with actual demand.

According to Checkout.com, moving from fixed provisioning to Cloud Composer 3's dynamic scaling model reduced monthly infrastructure costs by an estimated **30%**.

Beyond cost savings, automatic scaling also removes the need for manual capacity planning, allowing platform teams to spend less time monitoring infrastructure utilization and more time improving data workflows.

## Why Dynamic Scaling Matters

Dynamic scaling delivers benefits beyond reducing cloud costs.

Because resources expand and contract automatically, engineering teams no longer need to predict future workloads or overprovision infrastructure to prepare for unexpected traffic spikes.

This improves:

- Resource utilization
- Operational efficiency
- Platform responsiveness
- Infrastructure flexibility

For organizations with variable workloads, automatic scaling ensures that compute resources are available when required while minimizing unnecessary spending during quieter periods.

By removing manual scaling decisions from daily operations, Cloud Composer 3 helps engineering teams focus on delivering reliable data pipelines rather than managing infrastructure capacity.

![info-2](https://0a515t3ure77wbvx.public.blob.vercel-storage.com/articles/1784801855276-info-2--30-.webp)

## Reliability Improved Through DAG Isolation

Improving platform stability was one of Checkout.com's primary goals during the migration. In its previous self-managed environment, a single resource-intensive or malfunctioning Directed Acyclic Graph (DAG) could affect the performance of the entire Airflow environment. This created operational risks and increased the time required to diagnose and resolve issues.

Cloud Composer 3 introduced architectural improvements that isolated workloads and reduced the impact of individual failures.

One of the most significant enhancements is **DAG isolation**. Instead of sharing a common execution environment, each DAG runs independently. If one workflow fails, encounters dependency issues, or consumes excessive resources, it no longer affects other workflows running in the same environment.

This isolation improves platform resilience while allowing engineering teams to troubleshoot individual pipelines without disrupting unrelated workloads.

**Managed Operations Reduce Administrative Overhead**

Another advantage of Cloud Composer 3 is that Google Cloud manages much of the platform's operational maintenance.

Routine tasks such as:

- Security patching
- Airflow version upgrades
- Infrastructure maintenance
- Environment health management

are handled automatically during scheduled maintenance windows.

By removing these responsibilities from internal teams, Checkout.com reduced operational overhead while ensuring its orchestration platform remained current with the latest platform improvements and security updates.

**Better Visibility for Faster Troubleshooting**

Operational visibility also improved after the migration.

Cloud Composer 3 integrates directly with **Google Cloud Monitoring** and **Cloud Logging**, providing centralized visibility into workflow execution, infrastructure health, and task performance.

Instead of relying on fragmented monitoring tools, engineers can investigate failures using a unified observability platform.

This improved visibility allows developers to identify issues independently, reducing the need to escalate routine troubleshooting tasks to the platform engineering team.

## Accelerating Developer Workflows

Beyond infrastructure improvements, the migration significantly enhanced the day-to-day experience for Checkout.com's data engineers.

By eliminating many manual operational tasks, developers could iterate faster, deploy workflows more efficiently, and onboard new projects with less administrative effort.

**Near-Instant DAG Synchronization**

One of the most noticeable improvements involved deployment speed.

Previously, synchronizing DAGs from Amazon S3 to the Airflow scheduler typically required around six minutes after each deployment.

After migrating to Cloud Composer 3, DAGs stored in **Cloud Storage** synchronize much faster, enabling faster development, testing, and deployment iterations.

Faster synchronization reduces waiting time during development, enabling quicker debugging, testing, and deployment cycles.

**Simplified Team Onboarding**

The migration also streamlined onboarding for new engineering teams.

Previously, platform engineers manually created secrets, variables, and dbt configuration before teams could begin building workflows.

Cloud Composer 3 simplified this process, allowing teams to become productive more quickly without depending on platform administrators for routine setup tasks.

Reducing manual onboarding not only improves developer productivity but also lowers the operational workload for platform engineering teams.

**Modernizing dbt Execution with Containers**

Another significant improvement involved how Checkout.com executes **dbt (data build tool)** workloads.

In the previous environment, engineers manually installed and maintained separate virtual environments to support different dbt versions. Managing dependencies across multiple projects became increasingly complex as the platform evolved.

Cloud Composer 3 enables **containerized dbt execution**, allowing each workload to run with its own isolated dependencies.

This approach offers several advantages:

- Complete dependency isolation
- Support for multiple dbt versions
- Reduced compatibility issues
- Simplified maintenance
- Minimal infrastructure overhead

Rather than maintaining multiple virtual environments, engineering teams can package each dbt workload into a container with its required dependencies, making deployments more predictable and easier to manage.

**Easier Environment Updates**

Updating the orchestration platform also became significantly simpler.

Previously, adding new Python packages or modifying user roles often required redeploying the entire Airflow environment.

Cloud Composer 3 eliminates much of this complexity by allowing routine environment updates without rebuilding the platform.

This flexibility enables teams to introduce new capabilities more quickly while minimizing operational disruption.

## AI-Powered Troubleshooting with Gemini Cloud Assist

Troubleshooting production failures is often one of the most time-consuming aspects of operating workflow orchestration platforms.

In traditional self-managed environments, engineers typically investigate failures by manually searching through logs, metrics, and infrastructure events across multiple monitoring systems.

Cloud Composer 3 enhances this process through **Gemini Cloud Assist**, Google's AI-powered operational assistant.

When a task fails, engineers can initiate a Gemini investigation directly from the Airflow DAG interface within the Google Cloud console.

Rather than simply displaying error messages, Gemini analyzes available operational data and generates a structured investigation.

Gemini generates an investigation scorecard that evaluates multiple hypotheses using both supporting and contradictory evidence gathered from the available operational data. This helps engineers understand potential causes more quickly and can significantly reduce mean time to recovery (MTTR).

This guided investigation helps engineers identify likely causes more quickly and can significantly reduce mean time to recovery (MTTR), particularly when diagnosing complex workflow failures.

![info-3](https://0a515t3ure77wbvx.public.blob.vercel-storage.com/articles/1784801885454-info-3--26-.webp)

## Key Results of the Migration

Checkout.com's migration to Cloud Composer 3 delivered measurable operational and engineering improvements across its data platform.

Before Migration

After Cloud Composer 3

Self-managed Apache Airflow

Fully managed Cloud Composer 3

Manual infrastructure maintenance

Google-managed operations

Fixed worker provisioning

Automatic dynamic scaling

Approximately six-minute DAG synchronization

Near-instant DAG synchronization

Shared execution environment

Isolated DAG execution

Manual dependency management

Containerized dependency isolation

Manual onboarding processes

Simplified team onboarding

Fragmented troubleshooting

Gemini Cloud Assist with AI-powered investigations

Higher infrastructure costs

Estimated 30% reduction in monthly infrastructure costs through dynamic scaling

---

## Conclusion

Checkout.com's migration to **Managed Service for Apache Airflow (Cloud Composer 3)** represents more than a platform upgrade. It demonstrates how managed cloud services can reduce operational complexity while enabling engineering teams to focus on delivering business value instead of maintaining infrastructure.

By replacing a self-managed Airflow deployment with Google Cloud's fully managed service, Checkout.com improved reliability through DAG isolation, reduced infrastructure costs with dynamic autoscaling, accelerated developer workflows, and simplified ongoing platform maintenance. Features such as containerized dbt execution, faster DAG synchronization, integrated observability, and Gemini Cloud Assist further enhanced the efficiency of its data engineering operations.

As organizations continue to modernize their analytics platforms, the Checkout.com migration highlights the advantages of adopting managed orchestration services that combine scalability, operational resilience, and AI-assisted troubleshooting. Rather than spending valuable engineering time maintaining orchestration infrastructure, teams can focus on building data pipelines, delivering insights, and supporting business innovation with a more reliable and cost-effective foundation.

## Original source

https://cloud.google.com/blog/products/data-analytics/how-checkout-com-tallies-data-with-cloud-composer-3

## Tags

`#GoogleCloud` · `#CloudComposer3` · `#ApacheAirflow` · `#DataEngineering` · `#DataAnalytics` · `#CloudComputing` · `#CheckoutCom` · `#DevOps`

---

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