Our Pick AWS Bedrock — Broader model selection and tighter AWS ecosystem integration make Bedrock the better choice for organizations already on AWS with diverse AI needs.
AWS Bedrock vs Azure OpenAI

import ComparisonTable from ’../../components/ComparisonTable.astro’;

For enterprises building AI applications in the cloud, the choice between AWS Bedrock and Azure OpenAI often maps to their existing cloud provider relationship. But the platforms have genuinely different capabilities worth understanding.

Quick Verdict

Choose AWS Bedrock if: You’re on AWS, need multi-model flexibility, or want access to Claude, Llama, and other non-OpenAI models.

Choose Azure OpenAI if: You need GPT-4o access with enterprise SLAs, or your organization runs on Microsoft/Azure infrastructure.

Platform Overview

<ComparisonTable headers={[“Feature”, “AWS Bedrock”, “Azure OpenAI”]} rows={[ [“Model selection”, “Claude, Llama, Titan, Mistral, Cohere”, “GPT-4o, GPT-4, DALL-E, Whisper”], [“OpenAI models”, “No”, “Yes (full OpenAI lineup)”], [“Claude access”, “Yes”, “No”], [“Llama access”, “Yes”, “No (Azure AI Foundry)”], [“Enterprise SLA”, “Yes”, “Yes (99.9% uptime)”], [“Data residency”, “Multiple regions”, “Multiple regions + sovereign”], [“HIPAA/SOC2”, “Yes”, “Yes”], [“Fine-tuning”, “Yes (select models)”, “Yes (GPT-4o, GPT-3.5)”], [“Agents/Workflows”, “Bedrock Agents”, “Azure AI Agent Service”], [“Pricing model”, “Per-token (on-demand or provisioned)”, “Per-token (PTU or pay-as-you-go)”], ]} />

Model Selection

AWS Bedrock offers a multi-model marketplace:

  • Anthropic Claude (3.5 Sonnet, Claude 3 Opus, Haiku) — safety-focused, long context
  • Meta Llama 3/3.1 — open weights, cost-effective at scale
  • Mistral AI — European models, GDPR-friendly
  • Amazon Titan — Amazon’s own models for embeddings and text
  • Cohere — enterprise NLP focus
  • AI21 Labs — specialized language models

Azure OpenAI provides exclusive access to:

  • GPT-4o and GPT-4 Turbo with enterprise SLAs
  • DALL-E 3 for image generation
  • Whisper for transcription
  • OpenAI o1/o3 (preview access)
  • Embeddings (text-embedding-3-large)

If GPT-4o is your required model: Azure OpenAI is the only enterprise path. If you want Claude or Llama: AWS Bedrock.

Integration with Cloud Ecosystem

AWS Bedrock integrates tightly with:

  • AWS Lambda (serverless inference triggers)
  • Amazon S3 (knowledge base storage)
  • Amazon Kendra (enterprise search)
  • AWS IAM (granular access control)
  • CloudWatch (monitoring and logging)
  • SageMaker (model training and deployment)

Azure OpenAI integrates with:

  • Azure Blob Storage and Cognitive Search
  • Azure Active Directory (enterprise auth)
  • Azure Functions
  • Power Platform (Copilot Studio)
  • Azure AI Search for RAG
  • Microsoft 365 integration

Organizations already deep in one cloud ecosystem will find their preferred platform’s AI service integrates more naturally.

Knowledge Base / RAG Setup

AWS Bedrock Knowledge Bases:

import boto3

bedrock_agent = boto3.client('bedrock-agent-runtime', region_name='us-east-1')

response = bedrock_agent.retrieve_and_generate(
    input={'text': 'What is our return policy?'},
    retrieveAndGenerateConfiguration={
        'type': 'KNOWLEDGE_BASE',
        'knowledgeBaseConfiguration': {
            'knowledgeBaseId': 'YOUR_KB_ID',
            'modelArn': 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet'
        }
    }
)
print(response['output']['text'])

Azure OpenAI with Azure AI Search:

from openai import AzureOpenAI

client = AzureOpenAI(
    azure_endpoint="https://YOUR_RESOURCE.openai.azure.com/",
    api_key="YOUR_API_KEY",
    api_version="2024-02-01"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is our return policy?"}],
    extra_body={
        "data_sources": [{
            "type": "azure_search",
            "parameters": {
                "endpoint": "https://YOUR_SEARCH.search.windows.net",
                "index_name": "company-docs",
                "key": "YOUR_SEARCH_KEY"
            }
        }]
    }
)

Both provide solid RAG infrastructure — the difference is which model you’re querying.

Compliance and Security

Both platforms offer enterprise-grade compliance:

  • SOC 2 Type II
  • ISO 27001
  • HIPAA eligibility
  • GDPR data processing agreements

Azure OpenAI advantages:

  • Microsoft’s sovereign cloud regions (US Gov, China, Germany)
  • Purview integration for data governance
  • Microsoft’s compliance portfolio (140+ certifications)

AWS Bedrock advantages:

  • AWS GovCloud for US government workloads
  • AWS Artifact for compliance documentation
  • Tighter integration with AWS security services

Both meet compliance requirements for most regulated industries. Azure has an edge for organizations tied to Microsoft’s compliance frameworks.

Pricing

Both platforms use similar per-token pricing, but model selection affects cost:

AWS Bedrock (Claude 3.5 Sonnet):

  • Input: $3/M tokens
  • Output: $15/M tokens

AWS Bedrock (Llama 3.1 70B):

  • Input: $0.99/M tokens
  • Output: $0.99/M tokens

Azure OpenAI (GPT-4o):

  • Input: $5/M tokens
  • Output: $15/M tokens

Provisioned throughput (PTU/reserved capacity) available on both for predictable pricing at scale.

Bottom Line

If you need GPT-4o with enterprise guarantees: Azure OpenAI. If you want multi-model flexibility including Claude and Llama, or are AWS-native: AWS Bedrock. The decision often comes down to your existing cloud relationship and which AI model family fits your use case best.