AWS vs Azure vs GCP: Choosing the Right Cloud for ML Workloads

Published on November 20, 2025

Choosing a cloud platform for machine learning involves trade-offs between features, pricing, ecosystem, and your team's expertise. Having deployed ML solutions on all three major clouds, here's my practical comparison.

ML Platform Overview

AWS SageMaker

Strengths

• Mature ecosystem: Most extensive ML service catalog
• Built-in algorithms: 17+ optimized algorithms
• Spot training: Up to 90% cost savings
• Ground Truth: Data labeling at scale

Example: Training on SageMaker

import sagemaker
from sagemaker.pytorch import PyTorch

# Configure estimator
estimator = PyTorch(
    entry_point='train.py',
    source_dir='./src',
    role=sagemaker.get_execution_role(),
    instance_count=1,
    instance_type='ml.p3.2xlarge',
    framework_version='2.0',
    py_version='py310',
    hyperparameters={
        'epochs': 50,
        'batch-size': 64,
        'learning-rate': 0.001
    }
)

# Start training
estimator.fit({'training': 's3://my-bucket/train-data'})

Azure Machine Learning

Strengths

• Enterprise integration: Seamless with Microsoft ecosystem
• Designer: No-code ML pipeline builder
• Responsible AI: Built-in fairness and explainability tools
• Hybrid support: Arc-enabled ML for on-premises

Example: Training on Azure ML

from azure.ai.ml import MLClient, command
from azure.identity import DefaultAzureCredential

# Initialize client
ml_client = MLClient(
    DefaultAzureCredential(),
    subscription_id="your-sub-id",
    resource_group_name="your-rg",
    workspace_name="your-workspace"
)

# Define training job
job = command(
    code="./src",
    command="python train.py --epochs 50 --lr 0.001",
    environment="AzureML-pytorch-2.0-cuda11.8@latest",
    compute="gpu-cluster",
    experiment_name="crack-detection"
)

# Submit job
ml_client.jobs.create_or_update(job)

Google Cloud Vertex AI

Strengths

• TPU access: Best for large-scale training
• BigQuery ML: SQL-based ML for analysts
• Unified platform: Clean, consistent experience
• Research heritage: TensorFlow, JAX first-class support

Example: Training on Vertex AI

from google.cloud import aiplatform

# Initialize
aiplatform.init(project='my-project', location='us-central1')

# Create training job
job = aiplatform.CustomTrainingJob(
    display_name='crack-detection-training',
    script_path='train.py',
    container_uri='us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.2-0:latest',
    requirements=['torchvision', 'pillow']
)

# Run training
model = job.run(
    replica_count=1,
    machine_type='n1-standard-8',
    accelerator_type='NVIDIA_TESLA_V100',
    accelerator_count=1,
    args=['--epochs', '50', '--lr', '0.001']
)

Pricing Comparison

For a typical training workload (8 vCPU, 32GB RAM, V100 GPU, 100 hours/month):

When to Choose Each Cloud

Choose AWS SageMaker when:

• You need the most mature MLOps tooling
• Your organization is already on AWS
• You want extensive pre-built algorithms

Choose Azure ML when:

• You're in a Microsoft enterprise environment
• You need strong hybrid/on-premises support
• Responsible AI features are a priority

Choose GCP Vertex AI when:

• You're training very large models (TPU access)
• Your data is in BigQuery
• You prefer TensorFlow/JAX ecosystems

Conclusion

All three clouds offer capable ML platforms. The "best" choice depends on your existing infrastructure, team skills, and specific requirements. For most projects, start with your organization's primary cloud—the ecosystem integration benefits outweigh minor feature differences.