Azure AI vs AWS AI vs Google Cloud AI - An Australian Comparison
The cloud AI platform question comes up in almost every new engagement. "Should we use Azure, AWS, or Google Cloud for AI?" And the honest answer is that it depends on things that have nothing to do with AI.
Your existing cloud investments, enterprise agreements, data residency requirements, and team skills matter more than which platform has a slightly better API for image classification. But those aren't the answers people want, so let me give you a proper comparison based on what we see working in Australian enterprises.
The Quick Answer
If you already have a Microsoft Enterprise Agreement and your developers work in .NET, use Azure AI. The integration with your existing Microsoft ecosystem - Entra ID, Office 365, Dynamics, Power Platform - is worth more than any per-unit pricing difference.
If your infrastructure already runs on AWS and your team thinks in Python and Terraform, AWS AI will be the path of least resistance.
If you're a data-heavy organisation that needs best-in-class ML tooling and your team has deep data science expertise, Google Cloud AI has some genuine advantages in model training and MLOps.
Now let me back that up with specifics.
Australian Data Residency
For many Australian businesses, especially those in financial services, healthcare, and government, data residency is the first filter. Your data must stay in Australia, full stop.
| Platform | Australian Regions | AI Service Availability |
|---|---|---|
| Azure | Australia East (Sydney), Australia Southeast (Melbourne) | Azure OpenAI, AI Search, Document Intelligence, Cognitive Services - all available in AU East |
| AWS | Asia Pacific (Sydney) | Bedrock, SageMaker, Comprehend, Textract - most available in ap-southeast-2 |
| Google Cloud | australia-southeast1 (Sydney), australia-southeast2 (Melbourne) | Vertex AI, Cloud Vision, Cloud NLP - most available in Sydney |
All three have adequate Australian presence for most use cases. The differences are in which specific AI services are available in Australian regions, and this changes frequently. Check current availability before making decisions.
Where Azure has an edge: Azure OpenAI Service is available in the Australia East region, meaning you can run GPT-4o with your data staying in Sydney. AWS Bedrock offers Claude and other models in the Sydney region. Google Cloud's Vertex AI with Gemini models is also available in Australia. The gap has narrowed significantly over the past year, but Azure was earliest to market with LLM services in Australian regions.
Large Language Model Access
This is where the platforms diverge most significantly.
Azure AI
- OpenAI models: GPT-4o, GPT-4o-mini, GPT-4, o1, o3 - exclusive cloud partnership with OpenAI
- Other models: Phi (Microsoft's small models), Llama (Meta), Mistral, and others through Azure AI model catalogue
- Differentiator: Only cloud platform with native OpenAI integration. If you want GPT-4o with enterprise security, data residency, and Azure AD integration, this is the only option.
AWS AI
- Anthropic Claude: Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku through Bedrock
- Other models: Llama (Meta), Mistral, Cohere, AI21 Labs, Amazon Titan through Bedrock
- Differentiator: Widest selection of third-party models through a single API. Good if you want to compare models or avoid vendor lock-in to a single model provider.
Google Cloud AI
- Gemini models: Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini Ultra - Google's own models
- Other models: Claude (Anthropic), Llama, Mistral through Model Garden
- Differentiator: Gemini models are competitive on price-performance, especially for multimodal tasks. Google's infrastructure for model serving is excellent.
Our Take
In our experience, model access is less important than people think. GPT-4o, Claude 3.5, and Gemini 1.5 Pro are all capable of handling the vast majority of business use cases. The differences in output quality are marginal for most applications - document processing, classification, summarisation, Q&A - and are usually smaller than the differences caused by prompt quality and system design.
Pick your platform based on your broader infrastructure needs, not which LLM it offers. You can always call a different model's API if needed.
Pre-Built AI Services
Beyond LLMs, each platform offers pre-built AI services for common tasks.
Document Processing
| Capability | Azure | AWS | Google Cloud |
|---|---|---|---|
| OCR | Document Intelligence | Textract | Document AI |
| Invoice extraction | Document Intelligence (prebuilt) | Textract AnalyzeExpense | Document AI (processors) |
| Custom extraction | Custom Document Intelligence models | Textract custom queries | Custom Document AI processors |
| Form processing | Document Intelligence | Textract AnalyzeDocument | Document AI Form Parser |
Winner: Azure Document Intelligence and AWS Textract are both strong. Google Document AI has caught up but has less flexibility for custom document types. For Australian businesses processing Australian document formats (ABN-formatted invoices, Medicare forms, ATO correspondence), Azure has the most mature prebuilt models.
Speech
| Capability | Azure | AWS | Google Cloud |
|---|---|---|---|
| Speech-to-Text | Azure Speech Service | Amazon Transcribe | Cloud Speech-to-Text |
| Text-to-Speech | Azure Speech (Neural) | Amazon Polly | Cloud Text-to-Speech |
| Real-time transcription | Yes | Yes | Yes |
| Australian English model | Yes (tuned) | Yes | Yes |
Winner: Close between all three. Azure's neural voice quality is slightly better in our testing, but the differences are small.
Computer Vision
| Capability | Azure | AWS | Google Cloud |
|---|---|---|---|
| Image classification | Azure Computer Vision | Rekognition | Cloud Vision |
| Object detection | Custom Vision | Rekognition Custom Labels | AutoML Vision |
| Video analysis | Video Indexer | Rekognition Video | Video AI |
Winner: Google Cloud has a slight edge on pure vision capabilities, particularly for custom models. AWS Rekognition is the most straightforward to use. Azure's Video Indexer is the best for video content analysis.
Developer Experience
Azure AI
- SDKs: Excellent .NET SDK, good Python SDK. TypeScript/JavaScript support is solid.
- Documentation: Good but spread across multiple sites. Azure AI Studio simplifies experimentation.
- Local development: Azure AI CLI and VS Code integration are strong. If your team uses Visual Studio, the integration is tight.
- Best for teams that: Work in .NET, use Visual Studio, and are already familiar with Azure.
AWS AI
- SDKs: Strong Python SDK (boto3). Good .NET and Java support.
- Documentation: AWS documentation is thorough but can be hard to search. Bedrock documentation is improving.
- Local development: AWS CLI and SAM CLI work well. Good Terraform support.
- Best for teams that: Work in Python, use infrastructure-as-code heavily, and prefer a wide model selection.
Google Cloud AI
- SDKs: Excellent Python SDK. Vertex AI SDK is well-designed.
- Documentation: Google's AI documentation is the cleanest of the three. Colab integration for experimentation is excellent.
- Local development: gcloud CLI is good. Strong Jupyter notebook integration.
- Best for teams that: Have data science backgrounds, work in Python, and value ML experimentation workflows.
Enterprise Integration
This is where the decision often gets made for Australian enterprises.
Microsoft Ecosystem
If your organisation uses Microsoft 365, Entra ID (Azure AD), Dynamics 365, or Power Platform, Azure AI integrates natively with all of these. Single sign-on, consistent identity management, and data flowing between services without custom integration work.
This isn't a small advantage. We've seen projects where 30-40% of the development effort goes into authentication, authorisation, and data integration. If Azure eliminates most of that work, the total project cost is significantly lower, even if the per-unit AI pricing is identical.
AWS Ecosystem
If you run your infrastructure on EC2, use RDS for databases, S3 for storage, and Lambda for compute, AWS AI services slot in naturally. IAM policies, VPC networking, and CloudWatch monitoring extend to AI services.
Google Cloud Ecosystem
If you use BigQuery for analytics, Google Workspace for productivity, and GKE for container orchestration, Google Cloud AI connects directly. The BigQuery ML integration for running ML models directly on your data warehouse is genuinely useful if you're already a BigQuery shop.
Pricing Comparison
Detailed pricing varies by service and changes frequently. Here are the broad patterns for Australian regions:
| Factor | Azure | AWS | Google Cloud |
|---|---|---|---|
| LLM inference (GPT-4o class) | ~$3.75-$7.50 / 1M input tokens (AUD) | ~$4.50-$7.50 / 1M input tokens (AUD, Claude 3.5 equivalent) | ~$2.60-$5.25 / 1M input tokens (AUD, Gemini 1.5 Pro) |
| Enterprise Agreement discounts | Strong (Microsoft EA) | Available (AWS EDP) | Available (Google CUD) |
| Free tier for experimentation | Moderate | Good | Good |
| Committed use discounts | PTU for Azure OpenAI (20-40% savings) | Provisioned throughput for Bedrock | Provisioned throughput for Vertex AI |
Key insight: List price comparisons are misleading for enterprise buyers. Your Microsoft EA, AWS Enterprise Discount Programme, or Google committed-use discounts can change the effective pricing by 20-40%. Check with your account manager before comparing on price alone.
For a detailed breakdown of Azure AI costs, see our Azure AI pricing guide.
Decision Framework
Answer these five questions and the right platform usually becomes clear:
1. Where Does Your Infrastructure Already Live?
If 80%+ of your cloud workloads are on one platform, use that platform for AI. The operational overhead of managing a second cloud provider almost never justifies a marginally better AI service.
2. What Does Your Team Know?
A team of .NET developers will be productive on Azure AI in days. Putting them on Google Cloud means weeks of ramp-up time. Skills alignment beats feature comparison.
3. Do You Have an Enterprise Agreement?
Microsoft EAs, AWS EDPs, and Google CUDs all include AI service consumption. If you have committed spend on one platform, using that platform for AI is essentially "free" up to your commitment.
4. What Are Your Data Residency Requirements?
All three have Australian regions, but check that the specific AI services you need are available in those regions. This changes frequently and varies by service tier.
5. Do You Need a Specific Model?
If you specifically need GPT-4o, you need Azure. If you specifically need Claude, AWS Bedrock is the most mature option (though Azure is adding Claude through its model catalogue). If you want Gemini, you need Google Cloud. For most business use cases, any of the top-tier models will work.
Our Recommendation for Australian Businesses
We work primarily with Azure AI, and here's why: most Australian mid-market and enterprise businesses already have Microsoft relationships. They use Office 365, Entra ID, and often have Azure infrastructure. For these organisations, Azure AI is the obvious choice because it minimises integration work and maximises the value of existing investments.
That said, we've built successful AI solutions on AWS and Google Cloud too. The platform matters less than the implementation. A well-architected solution on any of these platforms will outperform a poorly designed one on the "best" platform.
If you're genuinely cloud-agnostic and starting fresh, here's our opinionated ranking for AI workloads in 2026:
- Azure AI - best for organisations with Microsoft ecosystem, enterprise compliance requirements, and teams with .NET skills
- Google Cloud AI - best for data-intensive organisations with Python-skilled teams who want the tightest ML tooling
- AWS AI - best for organisations that value model choice and are already running production workloads on AWS
Getting Help with the Decision
If you're evaluating cloud AI platforms and want an informed second opinion, talk to our team. We can run a half-day workshop to assess your current environment, team skills, and requirements, and give you a clear recommendation.
For organisations that have already chosen Azure, our Microsoft AI consulting team can help you plan and execute your first project. See our Azure AI Foundry consulting services for details on how we work.