What is the best cloud platform for hosting DeepSeek R1?

Question

Hey everyone! I have been diving deep into the world of open-source LLMs lately, and like many of you, I am absolutely blown away by DeepSeek R1. Its reasoning capabilities are just incredible, and honestly, it is giving the big players a real run for their money. I have been running some of the smaller distilled versions locally on my machine using Ollama, but I am starting to hit a wall when it comes to performance and reliability for a small side project I am developing.

I really want to move this to the cloud to get better speeds and maybe even try running the full 671B parameter model if I can afford it. However, when I started looking at the options, I got a bit overwhelmed. Every provider seems to have a different way of doing things. You have the giants like AWS SageMaker and Azure, but then there are all these specialized AI inference platforms like Together AI, DeepInfra, and Groq that claim to be much faster and cheaper for these specific types of models.

My main priorities are low latency for those long reasoning steps and, of course, keeping my costs manageable. I am trying to decide if it is better to go with a pay-per-token API model or if I should just rent a dedicated GPU instance on something like RunPod or Lambda Labs and host it myself. I have heard that some platforms are already offering optimized R1 endpoints that are super snappy, but I am not sure which ones are the most stable for a production environment.

I am also a bit concerned about the setup process. Some platforms make it as easy as one click, while others require a lot of manual configuration with Docker and CUDA drivers. Since I am working on this solo, I would really love to avoid spending three days just debugging environment variables.

For those of you who have already moved your DeepSeek R1 workflows to the cloud, which platform did you settle on and what was the main factor for you? Are you seeing significant performance gains, and how has the pricing looked compared to your initial expectations?

ewiifskulo · Accepted Answer

Hey, saw this earlier and wanted to jump in because I have been down this exact rabbit hole recently. Honestly, moving from distilled models on Ollama to the full DeepSeek R1 671B is a massive leap. I mean, you gotta be REAL careful with your budget here because that 671B monster needs an insane amount of VRAM. You're basically looking at a cluster of NVIDIA H100 80GB Tensor Core GPU or NVIDIA A100 80GB Tensor Core GPU nodes just to get it running without it crawling. In my experience, I would suggest sticking to a serverless API first. If you try to host it yourself on RunPod or Lambda Labs GPU Cloud, you're gonna spend days fighting with CUDA drivers and environment variables. It is literally a nightmare for a solo dev. Plus, the cost of an idle 8-way GPU node will burn through your cash while your sleeping. I think? maybe only do that if you have constant traffic. For your situation, Together AI Inference API is probably the safest bet for production. They have optimized endpoints for R1 that are actually stable and snappy. Another one to look at is Groq if you want that instant-speed feel, tho their context limits can be a bit tight compared to others. Make sure to monitor your costs closely tho!! Those reasoning steps in R1 can generate a LOT of tokens, and if you aren't careful, a small side project can end up costing a fortune. I'd actually suggest testing the DeepSeek R1 Distill Llama 70B on DeepInfra first. It is usually the sweet spot for performance vs cost before you go for the full 671B. Anyway, gl with the project! Cheers.

jmmovezmud · Answer

In my experience, id suggest DeepInfra cuz their H100 inference is fast. Honestly, setting up a RunPod GPU Instance was way too complex for me... check their per-token pricing yet?? just be careful!!

ThomasTew · Answer

Respectfully, I'd consider another option instead of managing your own instance cuz that setup is a nightmare. Honestly, just use Groq Cloud API or Fireworks AI Inference Engine because they're insanely fast and way cheaper for a solo dev. You only pay for what you use, so you wont go broke trying to run the full 671B model!!