AWS Bedrock vs Azure AI Foundry: Which Platform for Enterprise AI Access?

Mar 6, 2026

Problem

When I started evaluating cloud platforms for enterprise AI access, I faced a common decision: AWS Bedrock or Azure AI Foundry?

My requirements were straightforward:

Access to multiple frontier models (Claude, GPT-4, Llama)
Enterprise-grade security and compliance
Integration with existing infrastructure
Predictable pricing for production workloads

I spent weeks testing both platforms. Here’s what I learned.

Environment

AWS account with existing VPC and IAM setup
Azure subscription with Entra ID configured
Python 3.11 for SDK testing
Primary models tested: Claude 3.5 Sonnet, GPT-4o

Model Access Comparison

I first looked at what models each platform offers natively.

AWS Bedrock Models

AWS Bedrock Model Catalog:
├── Anthropic Claude
│   ├── Claude Opus 4
│   ├── Claude Sonnet 4
│   ├── Claude 3.5 Sonnet v2
│   ├── Claude 3.5 Haiku
│   └── Claude 3.7 Sonnet
├── Meta Llama
│   ├── Llama 3.1 (8B, 70B, 405B)
│   ├── Llama 3.2 (11B, 90B)
│   └── Llama 3.3 70B
├── Mistral AI
├── Cohere
├── AI21 Labs
├── Amazon Nova
└── DeepSeek-R1

Azure AI Foundry Models

Azure AI Foundry Model Catalog:
├── OpenAI (Native)
│   ├── GPT-4o
│   ├── GPT-4o-mini
│   ├── o1-preview, o1-mini
│   ├── GPT-image-1
│   ├── DALL-E
│   └── Whisper, TTS
├── Microsoft Models
│   ├── MAI-DS-R1
│   ├── Phi series
│   └── Healthcare AI
├── Meta Llama
│   ├── Llama-4-Maverick-17B
│   └── Llama-3.3-70B
├── Mistral AI
└── Cohere

The key difference I found: Bedrock has native Claude access, Azure has native OpenAI access. Both have Llama, but the primary model providers differ.

Testing Both Platforms

I wrote similar code to test each platform.

AWS Bedrock Test

import boto3
import json

# Initialize Bedrock client
bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name="us-east-1"
)

# Invoke Claude 3.5 Sonnet
response = bedrock_runtime.invoke_model(
    modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 2048,
        "messages": [
            {"role": "user", "content": "Explain enterprise AI security best practices in 3 points"}
        ]
    })
)

result = json.loads(response["body"].read())
print(result["content"][0]["text"])

Running this:

$ python bedrock_test.py

Here are three key enterprise AI security best practices:

1. **Data Governance**: Implement strict data classification and access controls...

2. **Model Security**: Use private endpoints and VPC integration...

3. **Audit and Compliance**: Enable comprehensive logging...

Azure AI Foundry Test

from openai import AzureOpenAI

# Initialize Azure OpenAI client
client = AzureOpenAI(
    azure_endpoint="https://your-resource.openai.azure.com/",
    api_key="your-api-key",
    api_version="2024-02-15-preview"
)

# Invoke GPT-4o
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Explain enterprise AI security best practices in 3 points"}
    ],
    max_tokens=2048
)

print(response.choices[0].message.content)

Running this:

$ python azure_test.py

Here are three essential enterprise AI security best practices:

1. **Access Control**: Implement role-based access control (RBAC)...

2. **Data Protection**: Encrypt data at rest and in transit...

3. **Monitoring**: Set up real-time monitoring and alerting...

Both worked, but the authentication and SDK patterns differ significantly.

Security and Authentication

This is where I noticed the biggest difference.

AWS Bedrock Security

I configured IAM policies for Bedrock access:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": [
        "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-*"
      ]
    }
  ]
}

I also set up Guardrails for content filtering:

{
  "contentPolicyConfig": {
    "filtersConfig": [
      {
        "type": "PII",
        "inputAction": "BLOCK",
        "outputAction": "BLOCK"
      }
    ]
  },
  "sensitiveInformationPolicyConfig": {
    "piiEntitiesConfig": [
      {
        "type": "CREDIT_CARD_NUMBER",
        "action": "MASK"
      },
      {
        "type": "EMAIL",
        "action": "ANONYMIZE"
      }
    ]
  }
}

Azure AI Foundry Security

Azure uses Entra ID (formerly Azure AD) for authentication:

from azure.identity import DefaultAzureCredential
from openai import AzureOpenAI

# Use managed identity
credential = DefaultAzureCredential()
token = credential.get_token("https://cognitiveservices.azure.com/.default")

client = AzureOpenAI(
    azure_endpoint="https://your-resource.openai.azure.com/",
    azure_ad_token=token.token,
    api_version="2024-02-15-preview"
)

For our team already using Microsoft 365, Entra ID integration was seamless.

Pricing Comparison

I compared costs for typical production workloads.

Pricing Aspect	AWS Bedrock	Azure AI Foundry
Model	On-demand + Provisioned	Standard + Serverless + Managed
Claude 3.5 Sonnet (input)	$3.00/1M tokens	Via partnership (varies)
GPT-4o (input)	Via partnership	$2.50/1M tokens
Commitment discounts	1-month, 6-month	Reserved capacity
Best for	Variable + high-volume	Production + fine-tuning

I found both platforms competitive for production use. The key is understanding your workload patterns:

Variable workloads: Use on-demand/serverless
Consistent high-volume: Commit to provisioned throughput
Fine-tuning needs: Consider managed compute options

Decision Framework

After testing both, I created a simple decision guide:

Choose AWS Bedrock if:
├── Your infrastructure runs on AWS
├── You need native Claude access
├── Your team knows AWS IAM/CloudTrail
└── You want Bedrock Knowledge Bases for RAG

Choose Azure AI Foundry if:
├── Your org uses Microsoft 365/Entra ID
├── You need native GPT-4 access
├── Your team develops with Microsoft tooling
└── You want Azure AI Search integration

What I Chose

For my use case (AWS-native infrastructure, Claude-first model strategy), I chose AWS Bedrock. Here’s why:

Native Claude access without partnership overhead
Existing IAM policies and VPC setup worked immediately
Guardrails integrated well with our compliance requirements
Unified billing with other AWS services

But if I were in a Microsoft-centric organization, Azure AI Foundry would be the obvious choice for the same reasons in reverse.

The Reason

The choice between AWS Bedrock and Azure AI Foundry isn’t about which platform is objectively better. Both are enterprise-grade solutions with similar capabilities.

The decision comes down to three factors:

Primary model preference: Claude-first or GPT-first?
Existing infrastructure: AWS-native or Microsoft-centric?
Team expertise: Which tooling does your team know?

I think most enterprises should pick the platform that aligns with their existing cloud provider. The integration benefits, reduced latency, and unified billing outweigh minor feature differences.

Summary

In this post, I compared AWS Bedrock and Azure AI Foundry for enterprise AI access. I tested both platforms with real code, evaluated security features, and analyzed pricing.

The key point is: choose based on your existing cloud ecosystem. AWS Bedrock for AWS-native teams needing Claude access; Azure AI Foundry for Microsoft-centric organizations prioritizing GPT models.

Both platforms offer robust security, multi-model access, and enterprise compliance. Start with a proof-of-concept on your preferred platform to validate latency, throughput, and cost for your specific workload.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!