AWS vs Azure vs GCP for AI — Which Cloud for Your AI Workload?
Status: 🟩 COMPLETE 🟦 LIVING Section: decision-frameworks Tags: cloud, aws, azure, gcp, comparison, AI-infrastructure, decision
The short answer
For most Australian businesses building on AI:
- Already heavy AWS user? Stay on AWS — use Bedrock for managed AI models.
- Already on Microsoft / Office? Use Azure — Azure OpenAI Service has GPT-4 with enterprise compliance.
- Already on Google? Use Vertex AI on GCP — Gemini, plus Claude and others.
- Starting fresh and care most about cost? Azure tends to be cheapest for OpenAI models; AWS Bedrock for Claude.
- Need data residency in Australia? All three have Sydney regions.
The cloud you should use is usually the one you (or your organisation) already use. The cost of switching clouds rarely justifies it for AI workloads.
The core AI offerings
AWS — Bedrock + SageMaker
Bedrock: Managed access to multiple AI models via one API:
- Claude (Anthropic) — Amazon is a major Anthropic investor; Claude has first-class support here
- Llama (Meta)
- Mistral
- Titan (Amazon’s own models)
- Stable Diffusion (image generation)
- Cohere Command
- Various others
SageMaker: Full ML platform for building and training your own models. More technical; for ML engineers.
Other AI services: Amazon Q (AI assistant), Comprehend (text analysis), Rekognition (image/video), Polly (TTS), Transcribe (STT), Translate.
Azure — Azure OpenAI Service + Azure AI Foundry
Azure OpenAI Service: Microsoft’s exclusive enterprise hosting for OpenAI models:
- GPT-4o, o3, o4 — OpenAI’s frontier models in Azure data centres
- DALL·E 3 — image generation
- Whisper — speech-to-text
- Embeddings models
Azure AI Foundry: Microsoft’s broader AI platform:
- Multiple models (Llama, Mistral, custom Microsoft models)
- Model deployment and management
- Build agents and AI applications
Other AI services: Cognitive Services (vision, speech, language), AI Search (vector + traditional search), Bot Service.
GCP — Vertex AI
Vertex AI: Google’s managed AI platform:
- Gemini models (1.5/2.0/2.5 Flash and Pro)
- Claude models (Anthropic partnership)
- Llama and other open-weights models
- Imagen image generation
- Veo video generation
- Lyria music generation
- Model Garden — hundreds of models
Other AI services: Document AI (extract from PDFs/forms), Speech-to-Text, Translation, Vision AI, Recommendations AI.
Where each shines
AWS is best for…
- Claude access: Amazon’s Anthropic investment means Claude is well-supported on Bedrock; often new Claude features arrive on Bedrock alongside Anthropic’s direct API
- Bedrock’s model variety: Multiple model providers in one consistent API
- AWS-heavy organisations: If your data is already in S3, your databases are in RDS, your apps run on EC2 — Bedrock fits naturally
- Mature ecosystem: AWS has the most third-party tools, integrations, and consulting expertise
Azure is best for…
- Microsoft 365 organisations: Direct integration with Office, Teams, SharePoint
- OpenAI access: Azure OpenAI Service is the enterprise way to use GPT-4 (with stronger compliance than direct OpenAI API)
- Hybrid scenarios: Azure’s strong story for on-premise + cloud hybrid deployments
- Regulatory compliance: Strong industry-specific compliance (banking, government, healthcare)
GCP is best for…
- Gemini access: Vertex AI is the way to use Gemini at enterprise scale
- TPU access: Google’s custom AI chips can be cheaper for specific workloads
- Cutting-edge research models: Google’s research output often appears on Vertex first
- Data analytics + AI: BigQuery + Vertex AI is a very strong combination for analytics-driven AI
Pricing considerations
AI inference pricing varies by model, but rough comparisons (per million tokens, mid-2026):
| Model | Cheapest on |
|---|---|
| Claude Sonnet | AWS Bedrock (~3 in/out) or direct Anthropic |
| GPT-4o | Azure OpenAI (10) or direct OpenAI |
| Gemini Pro | GCP Vertex (10) or direct Google |
| Llama | Together AI, Groq, or RunPod (cheapest); also available on all three clouds |
| Open-weights generally | Cheapest on dedicated AI clouds (Groq, Cerebras, Together) not AWS/Azure/GCP |
Key insight: Direct APIs (Anthropic, OpenAI, Google AI Studio) are often cheaper than the cloud-managed versions. The cloud-managed versions are worth the premium when you need:
- Enterprise compliance certifications
- Data residency control
- Integration with the cloud’s other services
- Bulk volume discounts
Data residency for Australian users
All three clouds have Australian regions:
| Cloud | Australian regions | Notes |
|---|---|---|
| AWS | Sydney (ap-southeast-2); Melbourne (ap-southeast-4, opened 2024) | Bedrock available in Sydney for Claude |
| Azure | Australia East (Sydney); Australia Southeast (Melbourne); Australia Central (Canberra — government) | Azure OpenAI Service in Sydney |
| GCP | Sydney (australia-southeast1); Melbourne (australia-southeast2) | Vertex AI available; Gemini availability varies by region |
For most Australian workloads, choosing the Sydney region for whichever cloud you use satisfies data residency requirements.
Important caveats:
- AI model inference availability in Australian regions varies by model and changes over time. Check current availability.
- Even with Australian regions, some model providers may process inference in other regions. Read each model’s residency documentation.
- For sensitive data (health, government), confirm with each provider what specifically processes in Australia.
When to choose AWS
Strong fit if you:
- Already use AWS for hosting, storage, or databases
- Want broad model selection (Bedrock has the most diverse model catalogue)
- Need Claude with the strongest enterprise integration (Bedrock has it)
- Have AWS expertise in your team
- Use AWS-specific services (Lambda, DynamoDB, S3) extensively
Examples:
- Existing SaaS hosted on AWS adding AI features → AWS Bedrock for Claude or other models
- Data lake on S3 needing AI analysis → Bedrock + SageMaker
- Computer vision workload → Bedrock + AWS Rekognition
When to choose Azure
Strong fit if you:
- Microsoft-heavy organisation (Office 365, Teams, Active Directory, etc.)
- Need GPT-4 with enterprise compliance (Azure OpenAI Service)
- Already use Azure for hosting
- Need strong regulatory compliance for Australian banking, government, healthcare
- Want Microsoft Copilot integration with custom AI
Examples:
- Australian bank wanting GPT-4 with strong compliance → Azure OpenAI Service
- Government department needing AI on sovereign infrastructure → Azure Australia Central
- M365 organisation adding custom AI features → Azure AI Foundry
When to choose GCP
Strong fit if you:
- Google Workspace organisation
- Want Gemini at scale (the natural choice)
- Heavy data analytics workload (BigQuery + Vertex AI is strong)
- Want access to multiple frontier models in one platform (Vertex offers Gemini + Claude + Llama + open-weights)
- Want TPU-based training for custom models
Examples:
- Google Workspace business adding AI → Vertex AI
- Analytics-driven AI on big datasets → BigQuery + Vertex AI
- Training custom large models → TPU access via GCP
Hybrid and multi-cloud
Some organisations use multiple clouds for AI:
- AWS for hosting + OpenAI direct API for AI
- Azure for Office + AWS Bedrock for Claude
- GCP for analytics + direct Anthropic API
This is increasingly common. The reasons to consolidate to one cloud (volume discounts, simpler management) compete with the reasons to use the best tool for each job.
For most Australian small/mid-size businesses, pick one cloud and stick with it. Multi-cloud complexity is rarely worth the marginal cost savings.
Switching costs
Switching cloud providers for AI is moderate-to-high cost:
- Data migration: Moving training data, embeddings, vector databases
- API differences: Each cloud has different API patterns (similar but not identical)
- Integration rebuilds: Connections to other cloud services
- Compliance re-audits: New cloud means new compliance certifications
Don’t switch lightly. The right time to choose carefully is at the start.
When NOT to use the hyperscale clouds for AI
For some workloads, dedicated AI clouds are better than AWS/Azure/GCP:
- High-volume LLM inference: Groq, Cerebras, Together AI are faster and cheaper
- GPU training: Lambda Labs, RunPod, CoreWeave are cheaper than AWS/Azure/GCP GPUs
- Simple developer use: Direct APIs (OpenAI, Anthropic, Google AI Studio) are simpler and cheaper than cloud-managed versions
- Open-source models for hobbyists: Replicate, Together, Hugging Face Inference API
The hyperscale clouds are best when you need integration with broader cloud services, enterprise compliance, or you’re already on their platform.
What to do next
- Identify what cloud you (or your organisation) already use
- Use that cloud’s AI services as your default starting point
- Read aws-bedrock, azure-openai-service, or vertex-ai for deeper detail on your chosen platform
- For specific model needs, check open-weights-vs-closed and pricing-snapshot
- For Australian data sovereignty concerns: confirm Sydney/Melbourne region availability for your specific models and services
See also
- aws, aws-bedrock
- azure, azure-openai-service, azure-ai-foundry
- gcp, vertex-ai
- oracle-cloud, ibm-watsonx — enterprise alternatives
- australian-privacy-considerations
- pricing-snapshot
Sources
- AWS Bedrock, Azure OpenAI Service, GCP Vertex AI official documentation (verified June 2026)
- Gartner Magic Quadrant for Cloud AI (2024–2026)
- IDC cloud AI market analysis (2024–2026)
- Australian data residency information from each cloud provider