AWS Bedrock vs Azure OpenAI: Which One for Your Business?
AWS Bedrock and Azure OpenAI Service represent two distinct approaches to enterprise AI model access. Both provide managed infrastructure for foundation model inference, but they differ in model availability, architectural philosophy, pricing structure, and ecosystem integration. The right choice depends on your existing cloud investment, model requirements, and operational preferences.
Architectural differences
Azure OpenAI Service provides access to OpenAI's model family (GPT-4, GPT-4o, GPT-4o-mini, DALL-E, Whisper) hosted on Azure infrastructure. The service is tightly integrated with the Microsoft ecosystem — Azure AI Search, Microsoft 365 Copilot, Power Platform, and Dynamics 365 all leverage Azure OpenAI as their underlying model layer. This creates a cohesive experience for organizations already invested in Microsoft's stack.
AWS Bedrock takes a multi-provider approach, offering models from Anthropic (Claude), Meta (Llama), Mistral, Cohere, Stability AI, and Amazon's own Titan family through a unified API. This architectural choice provides model flexibility — organizations can evaluate and switch between providers without changing their application code. Bedrock also offers features like Knowledge Bases (managed RAG), Guardrails (content filtering), and AgentCore (autonomous agent infrastructure) as integrated platform capabilities.
Model availability
The model catalogs differ significantly. Azure OpenAI is the exclusive cloud provider for OpenAI's models — if your application specifically requires GPT-4 or GPT-4o, Azure is the path. Conversely, Anthropic's Claude models are available on Bedrock but not on Azure OpenAI.
For organizations that want access to both OpenAI and Anthropic models, a multi-cloud approach or direct API access to one provider becomes necessary. This is a meaningful architectural decision that affects vendor management, billing, and operational complexity.
Pricing structure
Both platforms charge per token (input and output separately), but rates vary by model and provisioning tier. As of mid-2026, approximate pricing for comparable model tiers:
Azure OpenAI GPT-4o: approximately $5 per million input tokens, $15 per million output tokens on pay-as-you-go. Provisioned Throughput Units (PTUs) offer predictable pricing for sustained workloads.
AWS Bedrock Claude 3.5 Sonnet: approximately $3 per million input tokens, $15 per million output tokens on-demand. Provisioned Throughput is available for predictable capacity.
AWS Bedrock Llama 3 70B: approximately $2.65 per million input tokens, $3.50 per million output tokens — significantly lower than proprietary models for workloads where open-source model quality is sufficient.
The cost differential between platforms is often less significant than the cost impact of prompt engineering and token optimization. Organizations that invest in reducing unnecessary context, optimizing retrieval precision, and caching repeated queries typically see 30–50% cost reductions independent of platform choice.
Selection criteria
Choose Azure OpenAI when: your organization is invested in the Microsoft ecosystem (M365, Azure AD, Power Platform); you specifically require GPT-4 or GPT-4o; your compliance requirements align with Microsoft's certification portfolio; or your team has existing Azure operational expertise.
Choose AWS Bedrock when: your infrastructure runs on AWS; you want model provider flexibility without application code changes; you need access to Anthropic's Claude models; you're building RAG systems (Bedrock Knowledge Bases reduces implementation effort); or you want to evaluate multiple models before committing.
Consider both when: your organization operates in a multi-cloud environment, or when different use cases within the same organization have different model requirements.
Migration and lock-in considerations
Both platforms use standard API patterns (chat completions format), which limits application-level lock-in. However, platform-specific features — Azure AI Search integration, Bedrock Knowledge Bases, Guardrails configurations — create operational dependencies that increase switching costs over time. Organizations should be intentional about which platform-specific features they adopt versus which they implement in a platform-agnostic manner.
Getting help
Our cloud consultant has 12+ years of deployment experience across both AWS and Azure. If you're evaluating platforms for an AI deployment, or need assistance with integration, token optimization, or architecture decisions, book a free 15-minute call and we'll discuss your specific requirements.