The main differences in infrastructure provided by AWS and Azure for DeepSeek-R1 lie in their deployment models, pricing strategies, and security considerations.
AWS Infrastructure for DeepSeek-R1
AWS offers DeepSeek-R1 as a fully managed serverless model through Amazon Bedrock, allowing developers to build and deploy the model without managing underlying infrastructure[7]. This setup simplifies the deployment process and reduces the need for extensive hardware resources. AWS also provides tools like SageMaker and Bedrock to support the model's integration and fine-tuning. However, AWS charges for AI-optimized cloud servers, which can be costly if not used efficiently, with prices reaching up to $124 per hour for certain configurations[3].
AWS's approach focuses on providing a flexible and scalable environment for AI applications, enabling users to leverage DeepSeek-R1's capabilities without worrying about infrastructure complexities. However, users must consider data privacy and security, especially when using models from Chinese startups, and AWS recommends using Amazon Bedrock Guardrails for added protection[7].
Azure Infrastructure for DeepSeek-R1
Azure provides DeepSeek-R1 through Azure AI Foundry, offering a trusted and scalable platform for enterprise users[9]. Unlike AWS's fully managed serverless approach, Azure requires users to manage the underlying computing power, which can lead to variable pricing depending on how efficiently the model is run[3]. Azure does not require dedicated servers for DeepSeek-R1, but users still pay for the computing resources used.
Azure's integration includes extensive safety evaluations and automated security assessments to ensure the model's reliability and security[4]. Additionally, Azure plans to introduce distilled versions of DeepSeek-R1 for local deployment on Copilot+ PCs, expanding its AI capabilities across different devices[4]. This approach allows for more control over the infrastructure and security settings, which is beneficial for organizations with strict compliance requirements.
Key Differences
- Deployment Model: AWS offers a fully managed serverless deployment, while Azure requires users to manage computing resources.
- Pricing: AWS charges for infrastructure usage, while Azure's costs vary based on computing efficiency.
- Security and Compliance: Both platforms emphasize security, but Azure provides more control over infrastructure settings, which can be advantageous for enterprises with strict compliance needs.
- Integration and Tools: AWS integrates DeepSeek-R1 with tools like SageMaker and Bedrock, while Azure uses AI Foundry for deployment and management.
Citations:
[1] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx
[2] https://dev.to/askyt/deepseek-r1-671b-complete-hardware-requirements-optimal-deployment-setup-2e48
[3] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx?admgarea=news
[4] https://www.ctol.digital/news/microsoft-aws-adopt-deepseek-r1-efficiency-vs-openai-claude-investments/
[5] https://apxml.com/posts/gpu-requirements-deepseek-r1
[6] https://www.popai.pro/resources/how-to-use-deepseeks-r1-model-with-third-party-platforms-like-azure-and-aws/
[7] https://virtualizationreview.com/Articles/2025/03/11/AWS-First-Cloud-Giant-to-Offer-DeepSeek-R1-as-Fully-Managed-Serverless-Model.aspx
[8] https://jan.ai/post/deepseek-r1-locally
[9] https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-available-on-azure-ai-foundry-and-github/