Hosting DeepSeek-R1: A Guide to Platform Options in 2025
As organizations look to leverage DeepSeek’s powerful language models, choosing the right hosting solution becomes crucial. This guide examines various platforms for hosting DeepSeek-R1, comparing costs, technical requirements, and deployment considerations across different providers.
Introduction
DeepSeek-R1, with its impressive 671B parameters, represents a significant advancement in language model capabilities. Using innovative approaches like PTX programming and optimized training on Nvidia H800 GPUs, DeepSeek has achieved remarkable efficiency in model training and deployment. This guide explores various hosting options, from cloud providers to specialized AI platforms, helping you make an informed decision for your specific needs.
Platform Comparison
Together.ai
Together.ai offers the most straightforward path to getting started with DeepSeek:
| Model | Price per 1M tokens |
|---|---|
| DeepSeek-V3 | $1.25 |
| DeepSeek-R1 | $7.00 |
| DeepSeek LLM Chat 67B | $0.90 |
Key Benefits:
- No infrastructure management required
- Quick integration through API
- Predictable pricing
- Real-world cost averaging $0.07-0.10 per typical call
Vultr
Vultr provides competitive infrastructure pricing with various GPU options. Through our referral link, new users can access significant benefits:
Available GPU Options:
- NVIDIA H100 (8x 80GB SXM) starting at $2.30/GPU/hour
- NVIDIA L40S (8x 48GB) starting at $0.848/GPU/hour
- AMD MI300X (8x 192GB) starting at $1.841/GPU/hour
- NVIDIA A100 (8x 80GB SXM) starting at $1.490/GPU/hour
New User Benefits:
- $300 credit (valid 30 days)
- $100 coupon (valid 1 year)
- Additional $3 credit for social media sharing
AWS Deployment
AWS offers flexible deployment options through EKS:
Recommended Setup:
- Initial Testing: g5.2xlarge ($1.25/hr)
- Production Environment:
- API Gateway for request handling
- EKS for orchestration
- Serverless GPU options with TensorFuse integration
- L40s (g6e.xlarge) pricing at ~$1.8/hr
Coming Soon: Our detailed Terraform deployment scripts for AWS will be available in our GitHub repository.
Azure Machine Learning
Azure provides a comprehensive solution for secure enterprise deployment:
Key Features:
- Managed Online Endpoints
- vLLM optimization
- Automatic scaling capabilities
- Integration with existing Azure services
- Regional deployment options (US, EU, etc.)
Coming Soon: Check out our upcoming Terraform templates for Azure deployment.
GPU Requirements and Optimization
DeepSeek’s innovative approach to GPU utilization includes:
- Custom PTX programming optimizations
- Efficient use of Nvidia architecture
- Support for various GPU types including H100, A100, and L40S
- Optimized memory management
Cost Analysis Example
Let’s compare the costs for a typical use case processing 50M input tokens + 10M output tokens:
| Platform | Calculation | Total Cost |
|---|---|---|
| Together.ai | (50M × $7.00/M) + (10M × $7.00/M) | $420.00 |
| Vultr (L40S) | ~48 hours at $0.848/GPU/hr × 8 GPUs | $325.63 |
| AWS (g5.2xlarge) | ~30 hours at $1.25/hr | $37.50 |
| Azure ML | Similar to AWS pricing | ~$40.00 |
Deployment Recommendations
For Experimentation & POC
Recommended: Together.ai
- Fastest time to market
- No infrastructure management
- Predictable costs
- Excellent for validation and testing
For Production Deployment
Options based on scale:
Small to Medium Scale
- Together.ai remains viable
- Predictable costs
- No operational overhead
Large Scale
- AWS or Azure deployment with Terraform (coming soon)
- Benefits from custom infrastructure
- Better cost optimization at scale
- Full control over deployment
Enterprise Requirements
- Azure ML for security compliance
- AWS for existing infrastructure integration
- Vultr for cost-effective bare metal options
Infrastructure as Code
We’re currently developing comprehensive Terraform templates for both AWS and Azure deployments. These templates will provide:
- Automated infrastructure setup
- Best practice security configurations
- Scalable architecture patterns
- Cost optimization strategies
Stay tuned for our upcoming repository release!
Conclusion
For most organizations starting with DeepSeek-R1, Together.ai provides the optimal balance of ease of use, cost, and reliability. As your usage scales, transitioning to cloud providers like AWS, Azure, or Vultr becomes more attractive, offering greater control and potential cost savings through optimization.
When making your decision, consider:
- Initial setup complexity
- Ongoing maintenance requirements
- Total cost of ownership
- Security and compliance needs
- Integration requirements
- Scaling expectations
Start with Together.ai for experimentation and proof of concept, then evaluate cloud provider options as your needs evolve and usage patterns become clear. Watch for our upcoming Terraform templates to simplify your deployment process.