Azure AI Foundry vs AWS Bedrock for Latin American Enterprises
A practical comparison of the two leading enterprise AI cloud platforms for Latin American organizations — models, pricing, RAG capabilities, governance and the scenarios where each is the right choice
For Latin American enterprises deploying generative AI, the two dominant enterprise cloud AI platforms are Microsoft Azure AI Foundry and Amazon Web Services Bedrock. Both provide access to leading foundation models as managed APIs within your cloud account, both are enterprise-grade with strong data governance, and both are viable for production AI workloads. Choosing between them is not an either/or decision based on which is “better” — it is a contextual decision based on your existing cloud investments, required model access, data governance constraints and specific use cases.
GLADiiUM has implemented both platforms for Latin American clients and is model-agnostic in our recommendations. This guide provides the honest comparison that helps you make the right decision for your specific situation.
Model Catalog Comparison
Azure AI Foundry Models
- OpenAI GPT-4o and GPT-4o mini — Exclusive to Azure AI Foundry among major cloud AI platforms (not available on Bedrock). The most capable multimodal models for complex reasoning, vision and creative tasks.
- OpenAI o1 and o1-mini — Advanced reasoning models for mathematical, scientific and logical tasks. Azure-exclusive.
- OpenAI Whisper — State-of-the-art speech-to-text in Spanish, English and Portuguese.
- DALL-E 3 — OpenAI’s image generation model.
- Microsoft Phi-3 family — Small language models (3.8B to 14B) for edge deployment and cost-sensitive applications.
- Azure AI Model Catalog: Llama, Mistral and other third-party models also available via Model Catalog, but with a smaller selection than Bedrock.
AWS Bedrock Models
- Anthropic Claude 3.5 Sonnet, Claude 3 Haiku, Claude 3 Opus — Available on Bedrock but NOT on Azure AI Foundry. Organizations specifically needing Claude must use Bedrock.
- Meta Llama 3.1 (8B, 70B, 405B) — The leading open-weight models, available for fine-tuning on Bedrock.
- Amazon Titan Text and Embeddings — Cost-efficient Amazon-developed models for high-volume tasks.
- Mistral AI (7B, 8x7B, Large) — European AI models for organizations preferring European AI providers.
- Stability AI Stable Diffusion XL — Image generation.
- No GPT-4o or OpenAI models. Organizations needing GPT-4o must use Azure AI Foundry.
RAG Architecture Comparison
RAG (Retrieval-Augmented Generation) — connecting AI models to your enterprise documents and databases — is the most common enterprise AI use case in Latin America. Both platforms provide managed RAG:
- Azure AI Foundry + Azure AI Search: Azure’s managed vector search (formerly Cognitive Search) with tight Foundry integration. Indexes documents from SharePoint, Azure Blob and on-premise sources. Native connector to Microsoft 365 data. Easier if you are already in the Microsoft ecosystem.
- AWS Bedrock Knowledge Bases: Managed RAG with Amazon OpenSearch Serverless or Amazon Aurora as the vector store. Native integration with S3 for document storage. More flexible vector store selection than Azure.
For Latin American organizations already running Microsoft 365 and Azure, Azure AI Foundry RAG with SharePoint integration is the faster path to a working knowledge base assistant. For organizations on AWS or those wanting flexibility in vector store selection, Bedrock Knowledge Bases is the right choice.
GLADiiUM’s Recommendation
- Choose Azure AI Foundry if: you are on Microsoft/Azure, you need GPT-4o or o1 specifically, or you want Copilot Studio integration
- Choose AWS Bedrock if: you are on AWS, you need Claude specifically, or you want maximum multi-vendor model flexibility
- Use both if: you have dual-cloud infrastructure or different use cases optimally served by different models (e.g., GPT-4o for customer-facing Spanish AI on Azure, Claude on Bedrock for document analysis)
Choose the Right Enterprise AI Platform
GLADiiUM will evaluate your existing cloud infrastructure, specific AI use cases and model requirements to recommend the optimal Azure AI Foundry, AWS Bedrock or dual-platform architecture.
English
Español