When using DeepSeek-R1 on AWS or Azure, there are several hidden costs and considerations that organizations should be aware of:
Hidden Costs on AWS
1. Infrastructure Costs: While DeepSeek-R1 itself does not incur direct model usage fees, users pay for the computing resources consumed. AWS charges up to $124 per hour for an AI-optimized cloud server, which can amount to nearly $90,000 per month for continuous usage[1][2]. This cost is significantly higher than accessing models via APIs, such as Meta's Llama 3.1, which costs $3 per 1 million tokens[1].
2. Storage Costs: For models deployed using Amazon Bedrock Custom Model Import, there is a monthly storage cost per Custom Model Unit. For example, if a model like DeepSeek-R1-Distill-Llama-8B requires two CMUs, the monthly storage cost would be approximately $3.90[6].
3. Inference Costs: The cost of running inference on custom models also varies based on usage patterns. For instance, if a DeepSeek-R1-Distill-Llama-8B model is active for one hour per day, the estimated monthly inference cost could be around $282.60[6].
Hidden Costs on Azure
1. Variable Pricing: Microsoft Azure customers do not need dedicated servers for DeepSeek-R1, but they still pay for the underlying computing power. This leads to variable pricing depending on how efficiently the model is run[1][2]. As of early 2025, DeepSeek-R1 use on Azure was reported to be free but subject to rate limits, which may change[3].
2. Lack of Clear Pricing Documentation: Since DeepSeek-R1 is not yet fully integrated with Azure's pricing calculators, users must contact the sales or support team for detailed pricing information[3]. This lack of transparency can make budget planning challenging.
3. Compliance and Security Considerations: While not a direct cost, deploying AI models like DeepSeek-R1 on cloud platforms requires careful consideration of compliance and security. For instance, using cloud services in regions with different data privacy laws can add complexity and potential costs related to ensuring compliance[5].
General Hidden Costs
1. Data Privacy and Security Concerns: If organizations choose to use DeepSeek-R1 via its parent company's API, which is cheaper at $2.19 per million tokens, they must consider data privacy and security risks. Routing workloads through Chinese servers may subject sensitive data to local regulations, potentially requiring additional compliance measures[1][2].
2. Training and Deployment Costs: While DeepSeek's reported training costs for the R1 model are relatively low, the actual expenditure likely includes significant investments in infrastructure, data acquisition, and personnel, which are not fully disclosed[8]. This can affect long-term operational costs and efficiency evaluations.
In summary, while DeepSeek-R1 offers cost efficiency in processing, the overall cost of using it on AWS or Azure includes infrastructure rental fees, potential storage costs, and variable pricing based on usage efficiency. Additionally, compliance and security considerations can add complexity and potential expenses.
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://campustechnology.com/Articles/2025/02/04/AWS-Microsoft-Google-Others-Make-DeepSeek-R1-AI-Model-Available-on-Their-Platforms.aspx?admgarea=news
[3] https://learn.microsoft.com/en-us/answers/questions/2152620/price-for-usage-of-deepseek-r1
[4] https://prompt.16x.engineer/blog/deepseek-r1-cost-pricing-speed
[5] https://www.findai.app/comparative-analysis-of-deepseek-model-deployment-costs-across-cloud-platforms/
[6] https://repost.aws/questions/QU-hcixrtFSaSoKH8GL-KogA/pricing-model-of-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import
[7] https://www.byteplus.com/en/topic/408956
[8] https://www.n-ix.com/deepseek-explained/