Azure AI Foundry vs Amazon SageMaker - Which Platform Fits
If you're choosing between Azure AI Foundry and Amazon SageMaker for your next AI project, you're comparing two fundamentally different approaches to AI development. Both are capable platforms, but they reflect different philosophies about how AI applications should be built.
We've built production systems on both platforms for Australian clients. Here's an honest comparison based on what we've seen in practice, not vendor marketing.
The Core Difference in Philosophy
Azure AI Foundry is built around pre-trained models and AI application development. It assumes you want to use existing models (GPT-4o, Llama, Mistral) and build applications on top of them. The workflow is: pick a model, test it, fine-tune if needed, build an application, deploy.
Amazon SageMaker is built around the machine learning lifecycle. It assumes you might train models from scratch, and it provides a full pipeline from data preparation through training, evaluation, and deployment. The workflow is: prepare data, experiment with algorithms, train models, optimise, deploy.
Neither philosophy is wrong. The right choice depends on what you're building.
Feature Comparison
Model Access and Catalogue
| Capability | Azure AI Foundry | Amazon SageMaker |
|---|---|---|
| OpenAI models (GPT-4o, o1, o3) | Native, first-party access | Available through Amazon Bedrock (separate service) |
| Open-source models (Llama, Mistral) | Model catalogue with one-click deploy | SageMaker JumpStart model hub |
| Anthropic Claude models | Not available | Available through Amazon Bedrock |
| Model marketplace breadth | 100+ models | 300+ models through JumpStart |
| Custom model hosting | Managed endpoints | SageMaker endpoints with more configuration options |
Our take: If you need OpenAI models (GPT-4o, o1), Azure AI Foundry has the advantage - you get direct, first-party access with the tightest integration. If you want Anthropic Claude models or the broadest possible model selection, AWS gives you more options across SageMaker and Bedrock. For open-source models, both platforms are capable.
Development Experience
| Feature | Azure AI Foundry | Amazon SageMaker |
|---|---|---|
| Visual IDE | AI Foundry portal with playground and prompt flow | SageMaker Studio with notebook-centric interface |
| Prompt engineering tools | Built-in playground, prompt flow designer | Available through Bedrock, not native to SageMaker |
| Notebook support | Jupyter notebooks available | Deeply integrated Jupyter experience |
| Code-first development | Azure SDK, Python SDK, REST API | SageMaker Python SDK, Boto3, REST API |
| Low-code options | Prompt flow visual designer | SageMaker Canvas for no-code ML |
| CI/CD integration | Azure DevOps, GitHub Actions | CodePipeline, CodeBuild, GitHub Actions |
| Local development | VS Code extension with AI toolkit | SageMaker local mode, Docker integration |
Our take: Azure AI Foundry provides a better experience for LLM-based application development. SageMaker provides a better experience for traditional ML model training. If your team is primarily building applications that use pre-trained models, AI Foundry's prompt flow and playground tools are more productive. If your team is training models from scratch with custom algorithms, SageMaker's notebook and training infrastructure is more mature.
MLOps and Production Operations
| Capability | Azure AI Foundry | Amazon SageMaker |
|---|---|---|
| Model registry | Basic versioning | Mature model registry with lineage |
| Experiment tracking | Evaluation-focused | Full MLflow-compatible tracking |
| A/B testing | Manual endpoint splitting | Built-in traffic routing for endpoints |
| Model monitoring | Azure Monitor integration | SageMaker Model Monitor (data drift, bias) |
| Auto-scaling | Azure autoscale for endpoints | SageMaker auto-scaling policies |
| Pipeline orchestration | Prompt flow for AI apps | SageMaker Pipelines for ML workflows |
| Feature store | Not available | SageMaker Feature Store |
Our take: SageMaker has more mature MLOps tooling, especially for traditional ML workflows. Model monitoring, experiment tracking, feature stores, and pipeline orchestration are all more developed. For LLM applications specifically, AI Foundry's evaluation and prompt flow tools are sufficient, and the MLOps gap matters less because LLM deployment patterns are simpler than traditional ML deployment.
Governance and Security
| Feature | Azure AI Foundry | Amazon SageMaker |
|---|---|---|
| Identity management | Entra ID (Azure AD) | IAM roles and policies |
| Network isolation | Private endpoints, managed VNet | VPC configuration, PrivateLink |
| Data encryption | AES-256 at rest, TLS in transit | AES-256 at rest, TLS in transit |
| Responsible AI tools | Content safety, groundedness detection | SageMaker Clarify (bias and explainability) |
| Compliance certs (Australia) | IRAP PROTECTED, ISO 27001, SOC 2 | IRAP PROTECTED, ISO 27001, SOC 2 |
| Audit logging | Azure Monitor, Activity Logs | CloudTrail, CloudWatch |
| RBAC granularity | Hub/project-level roles | IAM policy-level (more granular but more complex) |
Our take: Both platforms meet enterprise security requirements. Azure's advantage is simpler RBAC through Entra ID groups and the hub/project model. AWS's advantage is more granular IAM policies (at the cost of complexity). For responsible AI specifically, Azure AI Foundry's content safety tools are better for LLM applications, while SageMaker Clarify is better for traditional ML fairness and explainability.
For Australian organisations, both platforms have Australian data centres assessed to IRAP PROTECTED level.
Pricing Comparison
This is where the comparison gets interesting because the pricing models are quite different.
LLM Inference Costs
For GPT-4o and similar models:
| Model | Azure AI Foundry | AWS (via Bedrock) |
|---|---|---|
| GPT-4o | ~$2.50/$10.00 USD per 1M tokens (in/out) | Not available on AWS |
| Claude 3.5 Sonnet | Not available on Azure | ~$3.00/$15.00 USD per 1M tokens |
| Llama 3.1 70B (serverless) | ~$0.27 USD per 1M tokens | ~$0.27 USD per 1M tokens (Bedrock) |
| Mistral Large | ~$2.00/$6.00 USD per 1M tokens | ~$2.00/$6.00 USD per 1M tokens |
For the same open-source models, serverless pricing is similar across both platforms. The main pricing difference is which proprietary models each platform exclusively offers.
Compute Costs for Training and Hosting
| Resource | Azure (AUD/hour approx) | AWS (AUD/hour approx) |
|---|---|---|
| Single V100 GPU | ~$4.50 | ~$4.60 |
| Single A100 GPU | ~$5.50 | ~$5.80 |
| 4x A100 (training) | ~$22.00 | ~$23.20 |
| 8x A100 (large training) | ~$44.00 | ~$46.40 |
Compute pricing is broadly similar. AWS tends to be 5-10% more expensive for equivalent GPU instances, but this varies by region and instance type. Neither platform has a clear pricing advantage for compute.
AI Search / Knowledge Retrieval
| Service | Monthly Cost (AUD approx) |
|---|---|
| Azure AI Search (Standard S1) | ~$370 |
| Amazon OpenSearch Serverless | ~$350-$500 (depends on usage) |
| Amazon Kendra (enterprise search) | ~$1,200+ |
Azure AI Search is generally more cost-effective for RAG applications than Amazon's equivalent services, particularly compared to Kendra.
Total Cost of Ownership - Real Example
For a typical mid-market RAG application (internal knowledge assistant, 50,000 queries/month):
| Component | Azure AI Foundry (AUD/month) | AWS SageMaker + Bedrock (AUD/month) |
|---|---|---|
| LLM inference | $180 (GPT-4o mini) | $200 (Claude Haiku) |
| Knowledge retrieval | $370 (AI Search S1) | $450 (OpenSearch Serverless) |
| Storage | $15 | $15 |
| Monitoring | $30 | $35 |
| Total | $595 | $700 |
The cost difference is modest. Your choice should be driven by technical fit and team capabilities rather than pricing alone.
When Azure AI Foundry Is the Better Choice
Based on our project experience, Azure AI Foundry wins in these scenarios:
You're already on Microsoft infrastructure. If your organisation uses Microsoft 365, Entra ID, Azure DevOps, and other Microsoft services, AI Foundry integrates naturally. Identity management, networking, and billing all flow through your existing setup. We've seen AWS AI projects at Microsoft-heavy organisations spend weeks just setting up cross-platform identity federation.
Your primary use case involves OpenAI models. If you want GPT-4o, o1, or o3, Azure is the only cloud provider with direct, first-party access. Running these models through Azure means tighter integration, lower latency, and access to features before they hit other platforms.
You're building LLM applications, not training ML models. AI Foundry's prompt flow, playground, and evaluation tools are designed for LLM application development. If your project is a chatbot, document processor, content generator, or similar LLM-based application, AI Foundry provides a more streamlined workflow.
Your governance requirements favour simplicity. The hub-and-project model in AI Foundry is easier to set up and manage than the equivalent IAM configuration in AWS. For organisations without deep AWS IAM expertise, this can save weeks of configuration.
You need strong content safety out of the box. Azure AI Foundry's content safety features are more developed and easier to configure than the equivalent on AWS. For customer-facing AI applications where inappropriate outputs are a real risk, this matters.
When Amazon SageMaker Is the Better Choice
SageMaker wins in these scenarios:
You're already on AWS. The same integration argument applies in reverse. If your data, applications, and team expertise are all AWS, adding Azure for AI creates unnecessary complexity.
You need to train custom ML models from scratch. SageMaker's training infrastructure, experiment tracking, hyperparameter optimisation, and model registry are more mature than what AI Foundry offers for custom model training. If you're building predictive models on tabular data, computer vision models, or custom NLP models, SageMaker is the stronger platform.
You want Anthropic Claude models. If Claude is your preferred model family, AWS (through Bedrock) is the natural home. Claude 3.5 Sonnet and Claude 3 Opus are available natively on Bedrock with tight SageMaker integration.
Your team has deep ML engineering expertise. SageMaker gives you more control over every aspect of the ML pipeline. Custom training containers, distributed training, spot instance training, and bring-your-own-algorithm support are all more flexible than AI Foundry's equivalents. Teams with ML engineering skills can extract more value from SageMaker's flexibility.
You need a feature store. SageMaker Feature Store has no direct equivalent in Azure AI Foundry. If feature management is a significant part of your ML workflow, SageMaker has the advantage.
What About Using Both?
We occasionally see organisations use both platforms, typically when:
- Different business units have existing relationships with different cloud providers
- A specific model is only available on one platform (GPT-4o on Azure, Claude on AWS)
- The organisation has a genuine multi-cloud strategy with workloads on both
This is viable but adds operational complexity. You need expertise in both platforms, separate governance frameworks, and potentially duplicated infrastructure. For most mid-market Australian organisations, picking one platform and going deep is more effective than spreading across both.
The Decision Framework
Here's how we help clients decide:
Question 1 - Where does your existing infrastructure live?
If you're primarily on Azure, start with Azure AI Foundry. If you're primarily on AWS, start with SageMaker. Switching clouds for AI alone is rarely justified.
Question 2 - What are you building?
| Project Type | Recommended Platform |
|---|---|
| LLM application (chatbot, RAG, document processing) | Azure AI Foundry |
| Custom ML model (prediction, classification, forecasting) | Amazon SageMaker |
| Computer vision (custom training) | Amazon SageMaker |
| Content generation application | Either - depends on preferred model |
| Enterprise knowledge base | Azure AI Foundry (AI Search integration) |
| Real-time ML serving at scale | Amazon SageMaker |
Question 3 - Which models do you need?
If you specifically need GPT-4o or o1, you need Azure. If you specifically need Claude, you need AWS. If you're model-agnostic and open-source models work, either platform serves you well.
Question 4 - What does your team know?
The platform your team already knows will be 2-3x more productive than the platform they need to learn. Training costs (time and money) are real. If your data engineers are comfortable with AWS, putting them on Azure AI Foundry will slow your project by weeks or months.
Question 5 - What are your governance requirements?
Both platforms meet enterprise standards. Azure's governance model is simpler to configure; AWS's is more granular. If you have a small platform team, Azure's simplicity is an advantage. If you have a dedicated security team with IAM expertise, AWS's granularity might be preferred.
How We Work Across Both Platforms
At Team 400, our primary expertise is Azure AI Foundry - it's where we've built the most production systems and where we see the strongest fit for most Australian enterprises. That said, we understand the AWS ecosystem well enough to give honest advice about when SageMaker is the better choice.
Our recommendation process starts with your specific situation: what you're building, where your infrastructure lives, what your team knows, and what your governance requirements look like. From there, we recommend the platform that gets you to production fastest with the lowest ongoing operational burden.
If you're evaluating Azure AI Foundry for your organisation, our Azure AI Foundry consulting can help you move from evaluation to production in weeks. We handle architecture, implementation, and the governance framework that keeps your compliance team happy.
For broader AI strategy questions, explore our AI consulting services or learn about our work as Microsoft AI consultants.
Ready to discuss which platform fits your project? Get in touch and we'll give you a straight answer based on your specific situation. No platform agnosticism for its own sake - just practical advice on what works for your business.