AWS vs Azure for AI Applications: What They Actually Offer
Both cloud platforms have made major bets on AI infrastructure. Here's what AWS and Azure actually provide for AI workloads, the managed services, the tradeoffs, and when to use each.
Most teams spend more time on this decision than it deserves.
AWS and Azure are both mature, capable platforms for AI workloads. The differences that matter come down to two things: which model providers you need access to, and what cloud infrastructure you're already running. Everything else is roughly equivalent, and the energy you'd spend becoming a deep expert in one platform's specific AI services is better spent on the product.
That said, the differences are real. Here's what each platform actually provides.
AWS: Better Breadth, More Model Choices
Amazon Bedrock is AWS's managed access to foundation models. Through a unified API, you can call Claude (Anthropic), Llama (Meta), Mistral, Amazon's own Titan and Nova models, and a handful of others. You don't manage inference infrastructure; you call the API, Bedrock handles the rest.
The enterprise case for Bedrock is data governance. Requests to models through Bedrock stay within your AWS environment; they don't route through Anthropic's servers or Meta's servers. They stay in AWS, which means they fall under your existing AWS data residency agreements, your IAM policies, and your audit logs. For industries with data residency requirements, this is often the deciding factor for not using model providers' direct APIs.
SageMaker handles the heavier ML work: training custom models, fine-tuning foundation models on proprietary data, serving models you host yourself. It provisions the GPU instances, manages containers, and scales endpoints. The product has matured considerably since 2017. For teams doing actual model development rather than just calling APIs, it's the standard choice.
Inferentia and Trainium are Amazon's custom AI chips (inference and training respectively). Both are cheaper than equivalent Nvidia GPU instances for sustained large-scale workloads. The catch is a real one: your code needs to work with AWS's Neuron SDK, which is a meaningful migration effort. For teams already at scale running significant inference budgets, the cost reduction justifies this; for teams not yet at that scale, it's premature.
For vector search, AWS offers OpenSearch with vector support and Aurora with pgvector. Neither is best-in-class for pure vector search, but both keep your data within AWS, which matters if your data residency situation requires it.
Azure: Better Depth on OpenAI
Azure OpenAI Service is Azure's strategic advantage. Microsoft's investment in OpenAI gave Azure exclusive rights to host OpenAI's models in a managed enterprise service. GPT-4o, GPT-4, o1, o3, and DALL-E run on Azure infrastructure, with the same data governance story as Bedrock.
The practical difference from using OpenAI's API directly: your data stays in Azure, within your organization's compliance perimeter. Model versions available on Azure sometimes trail the public API by weeks, which is the standard tradeoff. For production systems where stability matters more than access to the latest experimental model, this is often acceptable.
Azure AI Foundry (formerly Azure ML) is the broader platform: model catalog, fine-tuning, evaluation pipelines, deployment management. The functionality is comparable to SageMaker. The integration with the rest of Azure's developer tooling (VS Code extensions, GitHub integration, Azure DevOps) is tighter than AWS's equivalents.
Azure AI Search with vector and hybrid search is the standard RAG implementation choice on Azure. The hybrid approach, combining semantic vector search with traditional keyword search, is well-implemented and performs well in most retrieval benchmarks. For teams building document Q&A or knowledge base systems on Azure, this is the natural starting point.
Azure Cognitive Services covers the capabilities that some applications need alongside LLMs: speech-to-text, translation, form recognition, computer vision. These are consumption APIs with per-request pricing. If you need any of them, having them in the same platform simplifies billing and access management.
The Differences That Actually Matter
Model choice is the most important difference. AWS Bedrock gives you access to Claude, Llama, Mistral, and Amazon's own models. Azure OpenAI gives you GPT-4 series and DALL-E with enterprise support. If your application is built on OpenAI's models and needs enterprise data governance, Azure has a clear advantage. If you want to switch between model providers or run experiments across multiple, AWS is more flexible.
Microsoft ecosystem integration is real. Organizations running on Active Directory, Microsoft 365, and Azure DevOps will find Azure AI services slot in more naturally. Permissions, identity, and audit tooling are unified. AWS is a better fit for organizations outside the Microsoft ecosystem; it has more services, more regions, and deeper integrations with third-party tools.
GPU availability has historically favored AWS. Azure has been adding capacity, but for large training runs or very high throughput inference, AWS typically has better access to high-end instances when you need them. This matters for teams doing serious model training; it doesn't matter much for teams calling managed APIs.
How to Actually Choose
Startups without existing cloud commitments: follow your model provider. Using Claude primarily? Both platforms work well; Anthropic's direct API is simpler. Using OpenAI? Azure gives you enterprise compliance with minimal friction. Running open-source models? AWS Bedrock or self-hosting on either platform.
Enterprises with existing cloud commitments: use what you already have. The integration overhead of switching clouds outweighs the differences in AI services for most workloads. Azure shops should use Azure. AWS shops should use AWS.
Teams doing serious ML work (custom training, high-scale inference, complex retrieval systems): the tooling on both platforms is mature enough that it's not the bottleneck. Pick based on existing expertise and commit to it.
The AI infrastructure layer is becoming commodity faster than most vendors want to admit. What matters in eighteen months is whether the AI features you built actually solve real problems. That's where the time should go.