The performance of DeepSeek-R1 on AWS and Microsoft Azure can be evaluated based on several key factors, including speed, latency, cost efficiency, and integration capabilities.
Speed and Latency
- AWS: DeepSeek-R1 is available on AWS through Amazon Bedrock, allowing enterprises to fine-tune and deploy the model. While specific speed metrics for AWS are not detailed, the model's general performance is characterized by a slower output speed compared to average models, with about 25.5 tokens per second[1]. Latency is higher than average, taking around 9.71 seconds to receive the first token[1]. However, AWS's infrastructure can optimize performance depending on the hardware used.
- Azure: On Azure AI Foundry, DeepSeek-R1 is integrated with built-in model evaluation tools, enabling developers to quickly compare outputs and benchmark performance[10]. While specific latency figures for Azure are not provided, users have reported significant latency issues with Azure's DeepSeek model, with some queries taking over a minute to generate a response[6]. This suggests that Azure's performance might be impacted by similar latency challenges as AWS.
Cost Efficiency
- AWS: Users on AWS pay for computing resources rather than per-token pricing for DeepSeek-R1, aligning with open-source AI pricing models[2]. This approach can be cost-effective for large-scale deployments but may vary based on infrastructure usage.
- Azure: Similarly, Azure users pay for underlying computing power, which can lead to variable pricing depending on how efficiently the model is run[3]. However, Azure's integration with DeepSeek-R1 offers a cost-efficient alternative to proprietary models like OpenAI's GPT-4o[3].
Integration and Development Capabilities
- AWS: AWS provides a universal AI marketplace approach, allowing diverse models like DeepSeek-R1 to be integrated and fine-tuned on its platform[3]. This flexibility is beneficial for enterprises seeking to experiment with different AI models.
- Azure: Azure AI Foundry offers a trusted and scalable platform for integrating DeepSeek-R1, with tools for rapid experimentation and iteration[10]. Microsoft's extensive safety evaluations and security assessments ensure that the model meets enterprise standards[7].
In summary, both AWS and Azure offer DeepSeek-R1 with similar cost efficiency advantages, but performance metrics like speed and latency can vary based on specific infrastructure and usage patterns. Azure's integration with safety evaluations and scalability tools may provide an edge for enterprise deployments, while AWS's diverse model offerings cater to a broader range of AI needs.
Citations:
[1] https://artificialanalysis.ai/models/deepseek-r1
[2] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx
[3] https://www.ctol.digital/news/microsoft-aws-adopt-deepseek-r1-efficiency-vs-openai-claude-investments/
[4] https://aws.amazon.com/blogs/machine-learning/optimize-hosting-deepseek-r1-distilled-models-with-hugging-face-tgi-on-amazon-sagemaker-ai/
[5] https://www.datacamp.com/blog/deepseek-r1
[6] https://learn.microsoft.com/en-us/answers/questions/2155368/performance-issue-with-azure-deepseek-model-high-r
[7] https://techstrong.ai/ai-at-the-edge/deepseek-r1-models-available-through-aws-azure/
[8] https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions
[9] https://iot-analytics.com/winners-losers-generative-ai-value-chain/
[10] https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-available-on-azure-ai-foundry-and-github/