Running DeepSeek-R1 on AWS and Azure involves different cost structures, primarily because both platforms offer the model through different pricing models.
AWS Pricing Model:
- AWS allows users to access DeepSeek-R1 by paying for the computing resources consumed, rather than per token. This approach aligns with how open-source AI models are typically priced on AWS. Users can customize their DeepSeek implementation using the Hugging Face platform, which provides flexibility for enterprises needing scale and customization.
- The cost of running DeepSeek-R1 on AWS would depend on the specific server configurations and usage patterns. For example, AWS charges up to $124 per hour for an AI-optimized server, which can translate to significant monthly costs if used continuously.
Azure Pricing Model:
- Azure also provides access to DeepSeek-R1 by charging for the underlying computing power used. However, Azure does not require renting dedicated servers for DeepSeek, which can lead to variable pricing based on how efficiently the model is run.
- As of recent updates, Azure's pricing for DeepSeek-R1 is not explicitly listed in standard pricing calculators, and users are advised to contact the sales or support team for detailed pricing information.
- Some reports suggest that API access to DeepSeek-R1 can start at a low cost, such as $0.14 per million tokens, though this may not reflect the standard pricing structure for Azure's computing resources.
Key Differences:
- Pricing Structure: Both AWS and Azure charge based on computing resources rather than per token for DeepSeek-R1. However, specific per-token pricing is not standard on these platforms, unlike some other providers.
- Customization and Flexibility: AWS offers more flexibility through integration with Hugging Face for customization, which might appeal to enterprises needing tailored AI solutions.
- Cost Efficiency: DeepSeek-R1 is generally positioned as a cost-efficient alternative to proprietary models, but the actual cost on AWS and Azure depends heavily on how efficiently users manage their cloud resources.
In summary, while both AWS and Azure offer DeepSeek-R1 with a focus on cost efficiency, the actual cost differences will largely depend on how users manage their cloud resources and the specific server configurations they choose.
Citations:
[1] https://prompt.16x.engineer/blog/deepseek-r1-cost-pricing-speed
[2] https://learn.microsoft.com/en-us/answers/questions/2152620/price-for-usage-of-deepseek-r1
[3] https://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx
[4] https://bluexp.netapp.com/blog/azure-vs-aws-pricing-comparing-apples-to-apples-azure-aws-cvo-blg
[5] https://artificialanalysis.ai/models/deepseek-r1
[6] https://www.reddit.com/r/LocalLLaMA/comments/1id5179/r1_is_now_on_azure_ai_serverless_great_news_if/
[7] https://windowsforum.com/threads/deepseek-r1-the-affordable-ai-game-changer-on-azure-and-aws.351061/
[8] https://www.byteplus.com/en/topic/385527
[9] https://iot-analytics.com/winners-losers-generative-ai-value-chain/