The deployment process of DeepSeek-R1 on AWS and Azure differs in several key aspects, reflecting the unique features and infrastructure of each cloud platform.
Deployment on AWS
AWS offers multiple ways to deploy DeepSeek-R1 models, each tailored to different needs and preferences:
1. Amazon Bedrock Custom Model Import: This method allows for the deployment of distilled versions of DeepSeek-R1, such as DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill-Llama-70B. Users can import these models from Amazon S3 or an Amazon SageMaker AI model repository and deploy them in a fully managed and serverless environment. This approach eliminates the need for infrastructure management while providing enterprise-grade security and scalability[2][4].
2. Amazon SageMaker JumpStart: This option simplifies the deployment and management of DeepSeek-R1 models with minimal clicks. It is suitable for users seeking a balance between ease of use and customization. Pricing is based on the underlying EC2 instances used for deployment[4].
3. Amazon EC2 with AWS Trainium/Inferentia: For optimal price-performance, DeepSeek-R1-Distill models can be deployed on specific hardware. Costs are determined by EC2 instance pricing and usage duration[4].
4. Fully Managed Serverless Model: AWS offers DeepSeek-R1 as a fully managed serverless model through Amazon Bedrock, allowing developers to build and deploy applications without managing underlying infrastructure. This service accelerates innovation by providing extensive features and tooling with a single API[5].
Deployment on Azure
Azure provides a more customized approach to deploying DeepSeek-R1 models:
1. Azure Machine Learning Managed Online Endpoints: Users can deploy DeepSeek-R1 models using a custom Dockerfile and configuration files. This involves setting up a managed online endpoint with Azure Machine Learning, which supports scalable and secure real-time inference. The deployment process involves creating a custom environment, defining the endpoint, and configuring the deployment settings[3].
2. Azure AI Foundry: Microsoft has made DeepSeek R1 available on Azure AI Foundry, offering enterprises access to its advanced reasoning capabilities. The model undergoes extensive safety evaluations, including automated security assessments. Additionally, Microsoft plans to introduce distilled versions of R1 for local use on Copilot+ PCs, expanding AI integration across its ecosystem[1].
Key Differences
- Infrastructure Management: AWS offers a fully managed serverless deployment option through Amazon Bedrock, which eliminates the need for infrastructure management. In contrast, Azure requires more manual setup using custom Dockerfiles and configuration files through Azure Machine Learning.
- Customization and Flexibility: Azure provides a more customized deployment process, allowing users to define their environment and endpoint settings explicitly. AWS, while offering flexibility through various deployment options, focuses on ease of use and scalability.
- Security and Compliance: Both platforms emphasize security, but AWS advises integrating Amazon Bedrock Guardrails to enhance protection for generative AI applications, particularly due to concerns surrounding Chinese tech[5]. Azure ensures safety through automated security assessments on Azure AI Foundry[1].
Citations:
[1] https://www.ctol.digital/news/microsoft-aws-adopt-deepseek-r1-efficiency-vs-openai-claude-investments/
[2] https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import/
[3] https://clemenssiebler.com/posts/deploying-deepseek-r1-azure-machine-learning/
[4] https://repost.aws/questions/QUzC1_jMmESBmpAuOzQh5JcA/guidance-on-aws-deepseek-ai-pricing-and-deployment-options
[5] https://virtualizationreview.com/Articles/2025/03/11/AWS-First-Cloud-Giant-to-Offer-DeepSeek-R1-as-Fully-Managed-Serverless-Model.aspx
[6] https://community.aws/content/2sEuHQlpyIFSwCkzmx585JckSgN/deploying-deepseek-r1-14b-on-amazon-ec2?lang=en
[7] https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-deepseek
[8] https://aws.amazon.com/blogs/aws/deepseek-r1-models-now-available-on-aws/