Comparing the latency of DeepSeek-R1 on AWS to Azure requires examining available data and deployment strategies for both platforms. While specific latency figures for AWS are not directly mentioned in the search results, we can infer some insights based on general performance characteristics and deployment methods.
Azure Latency:
- DeepSeek-R1 on Azure has been reported to have a higher latency compared to average models, with a time to first token (TTFT) of approximately 9.71 seconds[1]. This latency can be influenced by factors such as resource utilization and network conditions.
- Deployments on Azure often utilize Managed Online Endpoints, which provide scalable and secure environments but may still face issues like timeouts if requests exceed 120 seconds[3].
AWS Latency:
- While specific latency figures for DeepSeek-R1 on AWS are not provided, AWS typically offers robust infrastructure that can support efficient model deployment. However, latency can vary based on factors like instance type, network conditions, and model optimization.
- AWS charges for AI-optimized servers, which can be costly, but these servers are designed to handle high-performance workloads efficiently[4].
Comparison Considerations:
- Infrastructure and Optimization: Both AWS and Azure offer scalable infrastructure, but the actual latency can depend on how well the model is optimized for the specific cloud environment. For instance, using high-throughput engines like vLLM on Azure can improve performance[2].
- Resource Utilization: High resource utilization can lead to increased latency on both platforms. Monitoring and optimizing resource usage are crucial to maintaining low latency[3].
- Network Conditions: Network latency between the application and the cloud endpoint can significantly impact overall performance. Tools like Azure Network Watcher can help diagnose network issues[3].
In summary, while specific latency comparisons between AWS and Azure for DeepSeek-R1 are not detailed in the search results, both platforms can experience latency influenced by similar factors such as resource utilization and network conditions. Optimizing deployments and monitoring performance are key to minimizing latency on either platform.
Citations:
[1] https://artificialanalysis.ai/models/deepseek-r1
[2] https://clemenssiebler.com/posts/deploying-deepseek-r1-azure-machine-learning/
[3] https://learn.microsoft.com/en-us/answers/questions/2201406/deepseek-r1-deployed-in-azure-ai-hub-got-timeout
[4] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx
[5] https://futurework.blog/2025/01/30/deepseek-r1-at-azure-ai/
[6] https://www.linkedin.com/posts/zhu-liang_deepseek-r1-is-better-and-cheaper-wrong-activity-7288814972271280128-vuYU
[7] https://www.datacamp.com/blog/deepseek-r1
[8] https://azure.microsoft.com/en-us/blog/deepseek-r1-is-now-available-on-azure-ai-foundry-and-github/
[9] https://www.oneclickitsolution.com/centerofexcellence/aiml/on-premises-vs-cloud-hosting-llms-deepseek-r1-comparison