Deploying DeepSeek-R1 on AWS involves several options, each with different cost structures compared to other cloud providers. Here's a detailed comparison:
AWS Deployment Options
1. Amazon Bedrock Marketplace: This option allows for quick integration of pre-trained DeepSeek-R1 models via APIs. Pricing is based on usage, so you only pay for the compute resources consumed. This can be cost-effective for intermittent use.
2. Amazon SageMaker JumpStart: Offers a balance between ease of use and customization. Pricing is tied to the underlying EC2 instances used, which can vary significantly depending on instance type and usage duration.
3. Amazon Bedrock Custom Model Import: Provides flexibility and control by allowing you to deploy custom models like DeepSeek-R1-Distill. Costs are based on the number of active model copies and their duration of activity. For example, a DeepSeek-R1-Distill-Llama-8B model might require 2 Custom Model Units, with a cost of $0.0785 per minute per unit, leading to a monthly inference cost of around $282.60 if active for one hour daily[7].
4. Amazon EC2 with AWS Trainium/Inferentia: Offers optimal price-performance by deploying models on specialized hardware. Costs depend on EC2 instance pricing, which can range from a few dollars to over $30 per hour for high-performance instances[5].
Comparison with Other Cloud Providers
- Microsoft Azure: Azure does not require renting dedicated servers for DeepSeek, but costs vary based on computing power usage. This can lead to variable pricing depending on model efficiency.
- DeepSeek Official API: Offers a cost-effective option at $2.19 per million tokens for output, which is significantly cheaper than some Western cloud providers. However, using Chinese servers raises data privacy concerns[2].
- Other Providers: Smaller cloud providers like Together AI and Fireworks AI charge around $7 to $8 per million tokens, which is more expensive than DeepSeek's official API pricing[3].
Key Considerations
- Cost Efficiency: DeepSeek-R1 is positioned as a cost-efficient alternative to proprietary models, especially when accessed via APIs. However, infrastructure costs can add up if you choose to deploy on cloud servers.
- Performance vs. Cost: Larger models like DeepSeek-R1-Distill-Llama-70B offer better performance but at a higher cost. Smaller models, such as the 8B version, can be sufficient for many applications at a lower cost[1].
- Data Privacy and Security: When considering deployment options, especially with non-Western providers, data privacy and security are crucial factors due to potential regulatory issues[2].
In summary, AWS offers a range of deployment options for DeepSeek-R1, each with its own cost structure. While AWS provides flexibility and scalability, other providers may offer more straightforward pricing models or cost savings depending on specific needs and usage patterns.
Citations:
[1] https://repost.aws/questions/QUzC1_jMmESBmpAuOzQh5JcA/guidance-on-aws-deepseek-ai-pricing-and-deployment-options
[2] https://pureai.com/Articles/2025/02/04/Leading-Cloud-Providers-Offer-DeepSeekR1.aspx
[3] https://prompt.16x.engineer/blog/deepseek-r1-cost-pricing-speed
[4] https://aws.amazon.com/marketplace/pp/prodview-kcvgioe2gj33s
[5] https://www.byteplus.com/en/topic/384206
[6] https://www.byteplus.com/en/topic/404875
[7] https://repost.aws/questions/QU-hcixrtFSaSoKH8GL-KogA/pricing-model-of-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import
[8] https://www.cloudexpat.com/blog/deepseek-r1-hosting/