Context
In 2025, two platforms dominate the enterprise managed generative AI market: Microsoft's Azure OpenAI Service and Amazon's AWS Bedrock. Both target teams that want to integrate LLMs into their applications without managing GPU infrastructure — but their philosophies differ fundamentally. This comparison is based on our hands-on experience deploying AI workloads for clients on both platforms.
Model Catalogue: Exclusivity vs Diversity
This is the most structurally important difference between the two services.
- Azure OpenAI provides exclusive access to OpenAI models: GPT-4o, GPT-4o mini, o1 (reasoning), o3, DALL-E 3 for image generation, Whisper for transcription. If you need GPT-4o, this is the only managed enterprise option.
- AWS Bedrock offers a multi-provider catalogue: Claude 3.5 Sonnet/Haiku (Anthropic), Llama 3.1 405B/70B (Meta), Mistral Large/7B, Stable Diffusion (images), Amazon Titan. No OpenAI models available.
Our observation: in 2025 benchmarks, Claude 3.5 Sonnet is competitive with GPT-4o on most reasoning and coding tasks. The absence of GPT-4o on Bedrock is less of a handicap than it was in 2023.
Pricing and Cost Model
| Model | Platform | Input ($/M tokens) | Output ($/M tokens) |
|---|---|---|---|
| GPT-4o | Azure OpenAI | $5.00 | $15.00 |
| Claude 3.5 Sonnet | AWS Bedrock | $3.00 | $15.00 |
| GPT-4o mini | Azure OpenAI | $0.15 | $0.60 |
| Claude 3 Haiku | AWS Bedrock | $0.25 | $1.25 |
| Llama 3.1 70B | AWS Bedrock | $0.72 | $0.72 |
Both platforms offer provisioned throughput (reserved monthly capacity) for predictable, high-volume workloads — typically 30–60 % cheaper than on-demand for stable loads.
Security and Compliance
Both services provide enterprise-grade guarantees:
- Azure OpenAI: ISO 27001, SOC 2 Type II, GDPR, HIPAA. Data processed in your Azure tenant, explicit policy of non-use for training. Azure EU Sovereign Cloud for European data residency requirements.
- AWS Bedrock: ISO 27001, SOC 2, GDPR, HIPAA, PCI-DSS. Invocation logs in your own S3 (full control). Native Guardrails (PII filter, content policy). VPC Endpoints for 100 % private traffic.
DevOps Integration
Azure OpenAI (Terraform)
resource "azurerm_cognitive_deployment" "gpt4o" {
name = "gpt-4o-prod"
cognitive_account_id = azurerm_cognitive_account.openai.id
model {
format = "OpenAI"
name = "gpt-4o"
version = "2024-08-06"
}
scale {
type = "Standard"
capacity = 50
}
}AWS Bedrock (Python SDK)
import boto3, json
bedrock = boto3.client("bedrock-runtime", region_name="eu-west-1")
response = bedrock.invoke_model(
modelId="anthropic.claude-3-5-sonnet-20241022-v2:0",
body=json.dumps({
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Analyse this code..."}]
})
)
result = json.loads(response["body"].read())Advanced Features: RAG and Agents
- Azure OpenAI: native integration with Azure AI Search for RAG. Azure AI Studio for prompt engineering and evaluation. Copilot Studio for no-code agents.
- AWS Bedrock: Knowledge Bases (managed RAG on OpenSearch), Bedrock Agents for multi-step orchestration, Bedrock Flows for visual workflows. More technical flexibility but higher learning curve.
When to Choose Azure OpenAI
- Your primary infrastructure is on Azure
- You use Microsoft 365 and want AI integrated into Teams, SharePoint, Power Platform
- You specifically need GPT-4o or o1 from OpenAI
- Your data teams already work with Azure Machine Learning
When to Choose AWS Bedrock
- Your stack is AWS-centric (Lambda, ECS, EKS, S3)
- You want freedom to choose and switch models without vendor lock-in on OpenAI
- You need to fine-tune your own models (Llama, Titan) on your data
- You value full invocation traceability in your own S3 account
Our Recommendation
For AWS-first teams, Bedrock is the obvious choice — native ecosystem integration and model catalogue diversity are hard to beat. For Azure-first organisations or those already on Microsoft 365, Azure OpenAI integrates without friction into the existing environment.
In both cases, our architectural recommendation is the same: isolate your LLM calls behind an abstraction layer (a common client interface). This lets you switch providers or models without application refactoring — a modest investment that protects against the rapid obsolescence of this market.