Cloud Platforms for AI: AWS, Azure & GCP

#The Three Giants

Cloud platforms can feel overwhelming with their hundreds of services, but do not worry — by the end of this lesson, you will know exactly which services matter for ML and how to choose between providers. Most of the decision comes down to practical factors, not feature checklists.

The answer is D. All three clouds can handle this workload. The deciding factors are rarely technical — they are organizational. Do you have an existing AWS account with credits? Is your company on Microsoft Enterprise Agreement? Does your team have GCP experience? Start from your constraints, not from a feature comparison chart.

#AWS for AI: The Market Leader

AWS holds ~32% of the cloud market and has the broadest AI service portfolio. Its strength is sheer breadth: if an AI service exists, AWS probably offers it.

#Amazon SageMaker

SageMaker is AWS's end-to-end ML platform. Think of it as a managed Jupyter notebook that grew into an entire ML factory.

Core capabilities

Try it! Go to the free tiers of all three cloud providers (AWS Free Tier, Azure Free Account, GCP Free Trial) and navigate to their AI/ML service pages. Compare how each one organizes its ML tools. You will immediately see the personality differences described above — AWS's massive list, Azure's enterprise polish, and GCP's developer-friendly simplicity.

Training a model on SageMaker

# SageMaker training job — the key abstraction is the Estimator
import sagemaker
from sagemaker.huggingface import HuggingFace

estimator = HuggingFace(
    entry_point="train.py",              # Your training script
    instance_type="ml.p4d.24xlarge",     # 8x A100 GPUs
    instance_count=1,
    transformers_version="4.37",
    pytorch_version="2.1",
    py_version="py310",
    role=sagemaker.get_execution_role(),
    hyperparameters={
        "model_name": "meta-llama/Llama-2-7b-hf",
        "epochs": 3,
        "batch_size": 4,
        "learning_rate": 2e-5,
    },
)

# This launches a managed training job — you pay only while it runs
estimator.fit({"train": "s3://my-bucket/training-data/"})

#Amazon Bedrock

Bedrock is AWS's managed LLM service — think "API gateway to foundation models." You do not train or host anything. You send prompts, get responses, and pay per token.

• Data stays within your AWS VPC (privacy/compliance)
• Unified billing through AWS
• Fine-tuning and RAG built in (Knowledge Bases)
• Guardrails for content filtering
• Model evaluation tools

#AWS Data Layer for AI

• S3: Object storage for training data, model artifacts, logs. The de facto standard.
• Glue: ETL for preparing training datasets at scale.
• Athena: SQL queries on S3 data without loading it into a database.
• Redshift: Data warehouse for structured analytics.

#Azure for AI: The Enterprise Choice

Azure holds ~23% of the cloud market and has a unique advantage: the deepest integration with OpenAI models. If your company uses Microsoft 365, Teams, or has an Enterprise Agreement, Azure is the path of least resistance.

#Azure Machine Learning

Azure ML is Microsoft's end-to-end ML platform. Its key differentiator is tight integration with the Microsoft ecosystem (VS Code, GitHub, Active Directory).

Core capabilities

Training a model on Azure ML

# Azure ML training job using the v2 SDK
from azure.ai.ml import MLClient, command, Input
from azure.identity import DefaultAzureCredential

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

# Define the training job
training_job = command(
    code="./src",                          # Local training script directory
    command="python train.py "
            "--model_name ${{inputs.model_name}} "
            "--epochs 3 --batch_size 4 --lr 2e-5",
    inputs={
        "model_name": "meta-llama/Llama-2-7b-hf",
    },
    environment="AzureML-pytorch-2.1-cuda12@latest",
    compute="gpu-cluster",                 # NC A100 v4 cluster
    instance_count=1,
)

# Submit — Azure ML provisions compute, runs training, saves outputs
returned_job = ml_client.jobs.create_or_update(training_job)

#Azure OpenAI Service

This is Azure's killer feature for AI. You get OpenAI models (GPT-4, GPT-4o, DALL-E, Whisper) hosted within Azure's infrastructure, with enterprise security.

• Data does not leave your Azure tenant (critical for regulated industries)
• Virtual network integration, private endpoints
• Content filtering and abuse monitoring built in
• SLA guarantees (99.9% uptime)
• Pay with existing Azure commitment

#Azure Data Layer for AI

• Blob Storage: Azure's equivalent of S3 for training data and artifacts.
• Cosmos DB: Globally distributed NoSQL database, excellent for AI application state and vector search.
• Synapse Analytics: Unified analytics platform (data warehouse + Spark + SQL).
• AI Search: Vector and hybrid search for RAG applications.

#GCP for AI: The Research-Driven Choice

GCP holds ~11% of the cloud market but punches above its weight in AI. Google invented the Transformer, built TensorFlow, and designed custom TPU hardware. If cutting-edge AI research matters to you, GCP often gets new capabilities first.

#Vertex AI

Vertex AI is Google's unified ML platform. Its differentiator is the deepest integration with Google's AI research — Gemini models, TPU hardware, and research-grade tools.

Core capabilities

Training a model on Vertex AI

# Vertex AI custom training job
from google.cloud import aiplatform

aiplatform.init(project="my-project", location="us-central1")

# Define a custom training job
job = aiplatform.CustomContainerTrainingJob(
    display_name="llama-7b-finetune",
    container_uri="us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.2-1:latest",
    command=["python", "train.py"],
    args=[
        "--model_name", "meta-llama/Llama-2-7b-hf",
        "--epochs", "3",
        "--batch_size", "4",
        "--lr", "2e-5",
    ],
)

# Run the job — Vertex provisions a2-highgpu-1g (A100) machines
model = job.run(
    replica_count=1,
    machine_type="a2-highgpu-1g",        # 1x A100 GPU
    accelerator_type="NVIDIA_TESLA_A100",
    accelerator_count=1,
)

#Cloud TPUs

TPUs (Tensor Processing Units) are Google's custom AI accelerators. They are not GPUs — they are purpose-built for matrix multiplication, the core operation in neural networks.

TPU generations

#BigQuery ML

BigQuery ML lets you train models directly in SQL. No Python, no infrastructure management.

-- Train a classification model in SQL
CREATE OR REPLACE MODEL `my_project.my_dataset.churn_model`
OPTIONS(
  model_type='BOOSTED_TREE_CLASSIFIER',
  input_label_cols=['churned'],
  max_iterations=50
) AS
SELECT * FROM `my_project.my_dataset.customer_features`
WHERE split = 'train';

-- Predict on new data
SELECT * FROM ML.PREDICT(
  MODEL `my_project.my_dataset.churn_model`,
  (SELECT * FROM `my_project.my_dataset.customer_features` WHERE split = 'test')
);

#GCP Data Layer for AI

• Cloud Storage: Object storage for training data and model artifacts (GCS buckets).
• BigQuery: Petabyte-scale data warehouse with built-in ML capabilities.
• Dataflow: Stream and batch data processing (Apache Beam).
• Firestore: NoSQL document database for AI application state.

#Head-to-Head: What Each Cloud Does Best

#Real Cost Example: Fine-Tuning a 7B Model

Training a 7B parameter model for 1 epoch on 100GB of data costs approximately:

#How to Choose — Follow the Data

The biggest mistake teams make is choosing a cloud based on a blog post or conference talk. The right cloud is determined by your constraints, not by feature lists. Use this decision tree:

• If your data is in S3 (AWS), the egress cost to move it elsewhere is $0.09/GB. For 50TB, that is $4,500 just to move data. Start with AWS.
• If your data is in BigQuery (GCP), it is already optimized for Vertex AI. Start with GCP.
• If your data is in Azure Blob Storage, Azure ML can access it directly. Start with Azure.

• Need GPT-4 with enterprise security and data residency? Azure is the only option.
• Need Claude, Llama, and Mistral via a single API? AWS Bedrock.
• Need Gemini with tight platform integration or TPU training? GCP.

• A team with 3 years of AWS experience will ship 2-3x faster on AWS than on a "technically superior" platform they have never used. Do not underestimate the cost of context switching.

• A Microsoft Enterprise Agreement can give you 30-50% off Azure. An AWS Savings Plan or GCP Committed Use Discount can similarly reduce costs. Check with your finance team before choosing.

The answer is C. This is a textbook case of "follow the data." Your 10TB is already in Azure Blob Storage (moving it would cost ~$900 in egress fees alone). Your company is already paying for Office 365 (likely has an Enterprise Agreement with Microsoft discounts). And GPT-4 with enterprise security is only available through Azure OpenAI. Every constraint points to Azure. Choosing AWS or GCP here would mean paying more, moving data, and fighting organizational inertia — all for no technical advantage.

#The Grand Comparison

This is the table you will reference every time you pick a cloud for an AI project:

#When to Choose Each

Choose AWS when

• You need the broadest service selection
• Your organization is already AWS-heavy
• You want model choice (Bedrock offers Claude, Llama, Mistral)
• You need the most mature ecosystem (most tutorials, most community support)

Choose Azure when

• Your company has a Microsoft Enterprise Agreement
• You specifically need OpenAI models with enterprise security
• You want tight integration with Microsoft 365, Teams, Power BI
• Regulatory requirements demand Microsoft's compliance certifications

Choose GCP when

• You use JAX or TensorFlow and want TPU access
• Data analytics is central to your ML workflow (BigQuery)
• You want Gemini models with tight platform integration
• You value developer experience and clean API design

#Multi-Cloud and Hybrid Strategies

Most mature AI teams do not use a single cloud. They use multi-cloud strategies:

#Strategy 1: Best-of-Breed

Use each cloud for what it does best:

• GCP for training (TPUs, cost-effective GPUs)
• AWS for serving (broadest deployment options, Lambda for lightweight inference)
• Azure for enterprise LLM access (Azure OpenAI)

#Strategy 2: Primary + Overflow

Pick one primary cloud for 90% of workloads. Use a second cloud for:

• Burst capacity during training spikes
• Access to specific models (Azure OpenAI, Bedrock)
• Disaster recovery

#Strategy 3: Cloud-Agnostic Tooling

Use tools that abstract away the cloud provider:

• Kubernetes (EKS/AKS/GKE) for compute orchestration
• MLflow for experiment tracking (works everywhere)
• Hugging Face for model hosting (provider-agnostic)
• DVC for data versioning (storage-agnostic)

#Common Mistakes

1. Choosing a cloud based on a blog post — Pick based on your organization's existing investments, compliance requirements, and team skills. Not on which cloud had the best marketing this quarter.
2. Ignoring data gravity — If your 500TB data lake lives in S3, moving it to GCS costs $45,000+ in egress fees alone. Start your cloud decision from where your data lives.
3. Using managed services for everything — SageMaker endpoints cost 2-3x more than self-managed inference on EC2. Managed services are worth it early on; self-managed saves money at scale.
4. Forgetting networking costs — Cross-region data transfer, VPC peering, and egress fees add up silently. A model serving system that fetches embeddings from a different region can cost thousands in hidden networking fees.
5. Not negotiating — At scale ($50K+/month), every cloud provider offers custom pricing. Enterprise Discount Programs (AWS), Enterprise Agreements (Azure), and Committed Use Discounts (GCP) can save 30-50%.

#Quick Check

#Key Takeaways

• Data gravity drives cloud choice more than features. If your data already lives in AWS S3, egress fees and migration effort often outweigh any advantage a competing cloud's ML services offer; optimize within your existing ecosystem first
• Each cloud has a distinct AI identity. AWS Bedrock for the broadest managed LLM selection, Azure OpenAI Service for enterprise GPT-4 access, GCP Vertex AI + TPUs for training-intensive JAX/TensorFlow workloads; pick the one that matches your stack
• Cloud-agnostic tooling is the escape hatch. MLflow for tracking, Kubeflow for orchestration, and Docker for packaging keep you portable without sacrificing convenience; build on managed services but abstract the critical interfaces
• Multi-cloud adds capability and complexity in equal measure. Best-of-breed (three clouds for three purposes) requires a dedicated platform team and cross-cloud egress costs; primary + overflow is usually the right balance for teams under 50 engineers
• TPUs beat GPUs on cost-per-FLOP for specific workloads. At scale, TPU v5p can be 2-3× more cost-effective than H100 for large matrix multiplication in JAX; but PyTorch workloads belong on NVIDIA unless you can rewrite them

#Recap