GCP vs AWS: Key Differences and When to Use Each

Both GCP and AWS are leading cloud platforms offering computing, storage, and networking services. AWS has a broader service range and global reach, while GCP excels in data analytics and machine learning integration.

Quick Comparison

Here is a quick side-by-side comparison of GCP and AWS on key factors.

Factor	Google Cloud Platform (GCP)	Amazon Web Services (AWS)
Global Reach	24 regions, 73 zones	31 regions, 99 availability zones
Compute Services	Compute Engine, App Engine, Cloud Functions	EC2, Lambda, Elastic Beanstalk
Storage Options	Cloud Storage, Persistent Disk	S3, EBS, Glacier
Data Analytics	BigQuery (serverless data warehouse)	Redshift (data warehouse)
Machine Learning	Vertex AI, TensorFlow integration	SageMaker, extensive AI services
Pricing Model	Per-second billing, sustained use discounts	Per-second billing, reserved instances

Key Differences

AWS is the oldest and largest cloud provider, offering the widest range of services and the most global data centers. It suits enterprises needing extensive options and mature ecosystems. GCP focuses on simplicity, strong data analytics, and AI tools, making it ideal for data-driven projects.

GCP uses sustained use discounts automatically, which can lower costs for long-running workloads, while AWS offers reserved instances for cost savings but requires upfront commitment. GCP also integrates deeply with open-source tools like Kubernetes and TensorFlow.

In networking, GCP provides a global private fiber network for fast data transfer, whereas AWS has more regional edge locations for content delivery. Both support hybrid and multi-cloud setups but differ in tooling and ecosystem support.

Code Comparison

Creating a virtual machine instance using GCP's Python client library.

python

from google.cloud import compute_v1

instance_client = compute_v1.InstancesClient()
project = "your-project-id"
zone = "us-central1-a"
instance_name = "test-instance"

instance = compute_v1.Instance(
    name=instance_name,
    machine_type=f"zones/{zone}/machineTypes/n1-standard-1",
    disks=[compute_v1.AttachedDisk(
        boot=True,
        auto_delete=True,
        initialize_params=compute_v1.AttachedDiskInitializeParams(
            source_image="projects/debian-cloud/global/images/family/debian-11"
        ),
    )],
    network_interfaces=[compute_v1.NetworkInterface(
        network="global/networks/default"
    )],
)

operation = instance_client.insert(project=project, zone=zone, instance_resource=instance)
print(f"Instance creation started: {operation.name}")

Output

Instance creation started: operation-1234567890

AWS Equivalent

Creating a virtual machine (EC2 instance) using AWS SDK for Python (boto3).

python

import boto3

ec2 = boto3.resource('ec2')

instance = ec2.create_instances(
    ImageId='ami-0c94855ba95c71c99',  # Amazon Linux 2 AMI
    MinCount=1,
    MaxCount=1,
    InstanceType='t2.micro',
    KeyName='your-key-pair'
)

print(f"Instance created with ID: {instance[0].id}")

Output

Instance created with ID: i-0abcd1234efgh5678

When to Use Which

Choose GCP when your projects rely heavily on data analytics, machine learning, or you want simple pricing with automatic discounts. It is also great if you prefer open-source integrations and a global private network.

Choose AWS if you need the widest variety of services, extensive global infrastructure, or enterprise-grade features. AWS is better for complex, large-scale applications requiring mature tooling and hybrid cloud support.

Key Takeaways

AWS offers the broadest service range and global presence for diverse enterprise needs.

GCP excels in data analytics, AI, and simple, cost-effective pricing models.

Both platforms support virtual machines, serverless, and container services with strong ecosystems.

Choose GCP for data-driven projects and open-source friendliness.

Choose AWS for mature, large-scale, and complex cloud deployments.