AI Hosting DeepSeek-ready AI hosting

DeepSeek hosting, without the per-query bill.

DeepSeek is an open model built for code generation and hard reasoning problems, with a context window up to 128K tokens in a single pass, at roughly 95% less than what closed models like GPT-4 charge per call. It runs on our AI Hosting plans today: install it yourself through Ollama or vLLM, or call its API from an app you host with us. Self-hosting the full-size weights needs a GPU-backed plan. Talk to us.

View AI plans Plan a DeepSeek deployment

DeepSeek

API or self-hosted model layer

vLLM

Inference server ready

RAG apps

Vector-ready workflows

NVMe

Fast AI storage

AI control plane Live on AI Hosting

DeepSeek

model layer

vLLM

inference server

Open WebUI

interface

Vector DB

RAG memory

Compute

Memory

NVMe

$ ai-hosting route deepseek

$ runtime prepare --vllm --deepseek

$ vector stack attach --qdrant

$ gpu plan --review-required

Why self-host it

An open model that reasons well and costs less.

DeepSeek-R1 handles code generation and complex problem-solving with up to a 128K-token context window in a single pass. It's open-source, so you can self-host it, fine-tune it, or export it, no per-query API lock-in unless you choose one.

Full data sovereignty

Code, datasets, and model outputs stay on infrastructure you control. No third-party model provider ever sees your data.

Predictable costs

One flat hosting bill instead of per-query API billing: roughly 95% less than GPT-4 for similar output, even at high query volume.

Custom model training

Fine-tune DeepSeek on your own datasets and add safeguards specific to your domain, like industry-specific compliance rules.

Zero vendor lock-in

Open weights mean you can export the model and move it whenever you want. You're never stuck behind one provider's API.

Test performance control

Tune NVMe storage for faster inference, allocate dedicated RAM and vCPU cores, and scale resources as your workload evolves.

Regulatory compliance

Run isolated network configurations and keep full audit trails to meet industry-specific AI governance rules.

What it's for

What people build with DeepSeek.

Reasoning power and low cost make DeepSeek a fit for workloads that get expensive fast on closed models.

Automated code review

Flag issues in a pull request before a human reviewer looks at it.

Customer support chatbots

Run support conversations on infrastructure you control, useful when data residency or privacy rules matter.

Predictive maintenance

Read sensor and IoT data to catch equipment problems before they cause downtime.

Product recommendations

Personalize e-commerce recommendations without sending customer behavior data to a third party.

Game analytics

Read player behavior with low-latency inference sitting close to your game backend.

Research sandboxes

Give a research team a private environment to work through confidential datasets.

What's included

The four pieces every DeepSeek build needs.

Inference, interface, retrieval, and background work, all on one plan instead of scattered across separate services.

Inference

Run vLLM or LocalAI for self-hosted DeepSeek weights, or call DeepSeek's own API from your app. Either way, it runs on the same plan.

Interface

Open WebUI, an internal dashboard, or a customer-facing frontend, run it under your own domain and SSL instead of a shared third-party tool.

Retrieval

Pair DeepSeek with a vector database and your own documents so answers stay grounded in real data, not guesses.

Background work

Queues, webhooks, and scheduled jobs run beside the app, on the same fast NVMe storage.

Optional AI stack

Add the tools DeepSeek needs around it.

An inference server, an interface, a vector database, workers, add them as you need them, all on one plan.

vLLM Inference server

LocalAI OpenAI-compatible API

Open WebUI Private AI chat

AnythingLLM Team AI workspace

Ollama Model runtime

Dify AI app builder

Flowise Visual AI flows

OpenClaw Agent automation

Langflow Agent pipelines

n8n AI Workflow automation

Qdrant Vector database

Weaviate AI-native search

Milvus Large vector search

ComfyUI Image workflows

Whisper Speech to text

32TB outbound + unlimited inbound traffic

1 Gbit/s port speed

DDoS protection included

Firewall included

IPv4 + IPv6 included

Getting started

From plan to a running DeepSeek instance.

No GPU required to get started. DeepSeek's smaller distilled models run on CPU.

Pick a plan sized for your model

Smaller distilled DeepSeek models run comfortably on 4–8 vCPU / 16–32GB RAM plans. Larger weights need more headroom.

Install DeepSeek, or connect its API

Self-host through Ollama or vLLM for full control, or point your app at DeepSeek's hosted API and skip the inference layer entirely.

Start building

Code generation, chat, RAG, automation, once self-hosted, DeepSeek runs fully offline with no ongoing API calls.

Sizing it right

Most of this runs on CPU. Full-size weights don't.

Distilled DeepSeek models are light enough to skip GPU hosting entirely.

GPU planning

Self-hosting the full-size weights?

Distilled DeepSeek models are CPU-only. No GPU required. Minimum spec is 16GB RAM, a 4-core CPU, and 500GB storage; 32GB RAM, 8 cores, and 1TB+ storage is the comfortable starting point for larger datasets. For the commonly-used deepseek-r1:14b model, our DeepSeek Compute M plan and up covers it. Full-size DeepSeek weights are a different story: they need dedicated GPU infrastructure. Contact us so we can scope it.

Discuss GPU hosting

AI hosting FAQ

Questions before you build on DeepSeek?

How this fits with other AI tools, self-hosting versus API use, GPU needs, and support.

What are the system requirements? 16GB RAM minimum, 32GB+ recommended.

Minimum: 16GB RAM, a 4-core CPU, and 500GB storage. Recommended: 32GB RAM, 8 cores, and 1TB+ storage for larger datasets. No GPU required. DeepSeek is optimized for CPU-only processing. For the commonly-used deepseek-r1:14b model, our DeepSeek Compute M plan and up covers it comfortably.

Do I need to self-host DeepSeek, or can I just call its API? Either path runs on this hosting.

You can host an app that calls DeepSeek's API directly. That works well on the current plans. Self-hosting the model weights through an inference server such as vLLM or LocalAI is also possible, but larger weights need more resources and may need a GPU-backed plan.

Does DeepSeek need a GPU to run? Not for smaller models. CPU works fine.

Smaller distilled DeepSeek models run fine on CPU with enough RAM. No GPU required to get started. Full-size model weights are the exception and need dedicated GPU infrastructure; contact us if that's your use case.

Can DeepSeek run without an internet connection? Yes, fully offline once installed.

Once installed, a self-hosted DeepSeek instance runs fully offline with no ongoing calls back to DeepSeek's servers, useful for air-gapped environments or workloads that can't depend on external connectivity.

Does this include GPU hosting? Not by default. Larger self-hosted models need a GPU-backed plan.

The plans on this page cover the app, interface, storage, and worker layers around DeepSeek. If you plan to self-host larger DeepSeek weights directly, contact us so we can scope a GPU-backed option.

How does DeepSeek compare to closed models like GPT-4? Open-source, self-hostable, and typically far cheaper to run.

DeepSeek is open-source, so you can self-host it, fine-tune it, and avoid per-query API costs entirely: roughly $0.55 vs. $15 per million tokens compared to GPT-4, with a context window up to 128K tokens and an MIT license instead of a closed API. Real-world performance varies by task, so it's worth evaluating against your specific use case.

Can this scale to large datasets? Yes. Scale storage and compute horizontally.

A DeepSeek-based deployment can scale horizontally to handle terabyte-level datasets as your indexes, documents, or training data grow. Add compute and storage to the plan as the workload needs it.

Can I run this under my own domain? Yes. That is the preferred production path.

You can connect your own domain or subdomain, add SSL, and run the DeepSeek-powered interface behind your own access rules. That's better for trust, team access, and customer-facing AI products.

Start building

Build your DeepSeek app on a plan that's ready now.

Pick an AI Hosting plan, connect your DeepSeek app or interface, and add a vector database or workers when you need them.

Compare AI plans Talk to sales