AWS vs Azure vs GCP for AI — Cloud AI Platform Comparison 2025

Side-by-Side Comparison

Feature	AWS	Azure	GCP
ML Platform	SageMaker	Azure Machine Learning	Vertex AI
Market Share (Cloud)	★★★★★ ~31% (leader)	★★★★☆ ~25%	★★★☆☆ ~11%
GPU Availability	★★★★★ NVIDIA H100, B200	★★★★★ NVIDIA H100, B200	★★★★★ TPUs + NVIDIA H100/B200
Custom AI Chips	Trainium2, Inferentia2	Maia 100 (ramping up)	TPUs v5e/v6 — production-proven
Pre-trained Models	Bedrock (Claude, Llama, Mistral, etc.)	Azure OpenAI (GPT-5.x, DALL-E)	Model Garden (Gemini 3.x, open source)
OpenAI Access	No	★★★★★ Exclusive partnership	No
MLOps Maturity	★★★★★ Most comprehensive	★★★★☆ Strong Azure DevOps integration	★★★★☆ Strong Vertex AI Pipelines
AutoML	SageMaker Autopilot	Azure AutoML	Vertex AutoML
Notebooks	SageMaker Studio	Azure ML Notebooks	Vertex AI Workbench (Colab-based)
Pricing Complexity	★★★★★ Most complex	★★★★☆ Complex	★★★☆☆ Simpler, per-second billing
Free Tier (ML)	SageMaker free tier (limited)	Azure ML free tier	Vertex AI free credits
Enterprise Adoption	★★★★★ Broadest enterprise base	★★★★★ Microsoft enterprise integration	★★★★☆ Growing in ML-heavy orgs
Best For	Broad workloads, mature MLOps	Microsoft shops, OpenAI access	Cutting-edge ML, TPU training

ML Platform

AWS SageMaker

Azure Azure Machine Learning

GCP Vertex AI

Market Share (Cloud)

AWS ★★★★★ ~31% (leader)

Azure ★★★★☆ ~25%

GCP ★★★☆☆ ~11%

GPU Availability

AWS ★★★★★ NVIDIA H100, B200

Azure ★★★★★ NVIDIA H100, B200

GCP ★★★★★ TPUs + NVIDIA H100/B200

Custom AI Chips

AWS Trainium2, Inferentia2

Azure Maia 100 (ramping up)

GCP TPUs v5e/v6 — production-proven

Pre-trained Models

AWS Bedrock (Claude, Llama, Mistral, etc.)

Azure Azure OpenAI (GPT-5.x, DALL-E)

GCP Model Garden (Gemini 3.x, open source)

OpenAI Access

AWS No

Azure ★★★★★ Exclusive partnership

GCP No

MLOps Maturity

AWS ★★★★★ Most comprehensive

Azure ★★★★☆ Strong Azure DevOps integration

GCP ★★★★☆ Strong Vertex AI Pipelines

AutoML

AWS SageMaker Autopilot

Azure Azure AutoML

GCP Vertex AutoML

Notebooks

AWS SageMaker Studio

Azure Azure ML Notebooks

GCP Vertex AI Workbench (Colab-based)

Pricing Complexity

AWS ★★★★★ Most complex

Azure ★★★★☆ Complex

GCP ★★★☆☆ Simpler, per-second billing

Free Tier (ML)

AWS SageMaker free tier (limited)

Azure Azure ML free tier

GCP Vertex AI free credits

Enterprise Adoption

AWS ★★★★★ Broadest enterprise base

Azure ★★★★★ Microsoft enterprise integration

GCP ★★★★☆ Growing in ML-heavy orgs

Best For

AWS Broad workloads, mature MLOps

Azure Microsoft shops, OpenAI access

GCP Cutting-edge ML, TPU training

Detailed Analysis

ML Platform & Services

Depends on priorities

AWS SageMaker is the most comprehensive ML platform — covering the full lifecycle from data labeling through training to deployment and monitoring. It has the most features but also the steepest learning curve. Azure ML benefits from tight integration with the Microsoft ecosystem (VS Code, GitHub, Azure DevOps) and exclusive access to OpenAI models via Azure OpenAI Service. GCP's Vertex AI is the most cohesive and developer-friendly, leveraging Google's ML research directly — and it's the only platform with native TPU access for training large models efficiently.

GPU & Hardware Access

GCP (for TPUs); comparable for NVIDIA GPUs

GCP holds a unique advantage with TPUs — Google's custom AI accelerators that offer superior price-performance for large-scale training (they're what Google uses to train Gemini). All three clouds offer NVIDIA A100 and H100 GPUs, but availability and pricing vary by region. AWS's Trainium chips are competitive for training specific workloads. For inference, AWS Inferentia offers strong price-performance. GPU availability can be constrained during high demand — GCP's TPU availability gives it an edge for consistent large-scale training access.

Foundation Model Access

Azure (for OpenAI); AWS (for model variety)

Azure's exclusive partnership with OpenAI gives it a clear advantage if you need GPT-4, GPT-4o, or DALL-E with enterprise security and compliance. AWS Bedrock offers a marketplace approach — access Claude (Anthropic), Llama (Meta), Mistral, and others through a unified API. GCP provides Gemini natively plus a Model Garden with open-source and third-party models. The trend is toward multi-model access on all platforms, but Azure's OpenAI integration remains the deepest and most mature.

Pricing & Cost Optimization

GCP (simplicity); AWS (flexibility)

Cloud ML costs can escalate quickly. GCP generally offers the simplest pricing with per-second billing. AWS has the most pricing options (spot instances, reserved capacity, savings plans) but the most complexity. Azure pricing is comparable to AWS. For training workloads, GCP TPUs often provide the best price-performance. For inference, AWS Inferentia chips are very cost-effective. All three offer free tiers and credits for getting started. The most important cost factor is usually your engineering team's productivity — choose the platform where your team is most efficient.

The Verdict

Our Recommendation

AWS is the safe default for enterprises with existing AWS infrastructure. Azure is the right choice for Microsoft-centric organizations and anyone who needs OpenAI model access with enterprise controls. GCP is the best choice for ML-focused teams, especially those doing large-scale training or cutting-edge research.

Enterprise with existing AWS

AWS SageMaker

Deepest feature set, most mature MLOps, broadest enterprise adoption

Microsoft / OpenAI shop

Azure ML

Exclusive OpenAI access, Microsoft ecosystem integration, enterprise compliance

Large-scale model training

GCP Vertex AI

TPU access, best price-performance for training, Google ML heritage

Multi-model LLM applications

AWS Bedrock

Broadest model marketplace — Claude, Llama, Mistral, and more through unified API

Startup / small team

GCP

Simplest pricing, generous free credits, most developer-friendly ML platform

Key AI Concepts

Frequently Asked Questions

Which cloud is cheapest for AI?

It depends on your workload. GCP TPUs offer the best price-performance for large-scale training. AWS Inferentia is very cost-effective for inference. Azure and AWS have similar GPU pricing. For most startups, GCP's free credits and per-second billing make it the most affordable starting point.

Can I use OpenAI models on AWS or GCP?

Not directly. OpenAI has an exclusive cloud partnership with Azure — GPT-4 and DALL-E with enterprise features are only available through Azure OpenAI Service. On AWS, you can access Claude (Anthropic) and Llama (Meta) through Bedrock, which are competitive alternatives. On GCP, you get Gemini natively.

Which cloud has the best GPU availability?

GPU availability fluctuates. GCP's TPUs provide an alternative when NVIDIA GPUs are scarce. All three clouds have added capacity, but high-demand GPUs (H100, A100) can still be hard to get in popular regions. Reserved instances or capacity reservations help guarantee access.

Should I use a managed ML platform or self-host?

Managed platforms (SageMaker, Azure ML, Vertex AI) are recommended for most teams. They handle infrastructure, scaling, and MLOps tooling. Self-hosting (Kubernetes + custom setup) gives more control but requires significant engineering investment. Start managed, migrate to self-hosted only if you hit specific limitations.