For self-hosted AI
Your AI stack. Your server. Your data.
Run Ollama, n8n, vector databases, Stable Diffusion, and agent workflows on hardware sized for the workload. No per-token fees, no rate limits, no data leaving your server. Predictable monthly cost so a runaway loop doesn't drain your account.
- 14 years in business
- 99.99% uptime
- Trustpilot reviewed
- 5 global regions
There are reasons people are moving AI workloads off the API.
OpenAI bills you per million tokens. Claude bills you per million tokens. Run a few autonomous agents in a loop and you have a $400 invoice and a story about that one time. Self-hosted AI flips the model: pay a fixed monthly cost for the server, run as many tokens as the hardware can chew through, keep your prompts and your outputs on infrastructure you control. Open-weight models in 2026 (Llama 3.x, Qwen, Mistral, DeepSeek) are good enough for the majority of practical agent and RAG workloads. The blocker has been finding hosting that's actually priced for it.
RAM that fits the model
Quantized 7B and 8B models run comfortably on a 16GB VPS. Quantized 13B models on 24GB. 70B models on Dedicated CPU VDS. We don't gate RAM behind a "premium tier" upsell.
NVMe storage that loads fast
Model weights live on disk and get loaded into RAM at startup. NVMe means a 7B model is ready in seconds, not minutes. Important when you're iterating.
Predictable cost, no token billing
A 16GB Premium NVMe is around $48/month. Run Ollama 24/7, serve a thousand chat completions, train a LoRA, embed your entire document library into a vector DB. Same bill.
Recommended plan
Premium NVMe VPS
From $14/moNVMe storage, generous RAM tiers, full root. Hourly billing.
Pick the tier that matches the model: 8GB for quantized 7-8B, 16GB for 14B, 32GB+ for Mixtral-class. Step up to Dedicated CPU VDS for 70B-class quantized inference.
Dedicated CPU VDS
Higher tierDedicated CPU cores for sustained inference workloads.
When you need predictable tokens/second under load, dedicated cores beat shared every time. Best fit for production agent or RAG workloads.
Which plan runs which model?
| Plan | RAM | Llama 3.1 8B Q4 | Llama 3.1 8B Q8 | Qwen 14B Q4 | Mixtral 8x7B Q4 | Llama 3.3 70B Q4 |
|---|---|---|---|---|---|---|
| Premium NVMe 4GB ($14) | 4 GB | ~ | ||||
| Premium NVMe 8GB ($28) | 8 GB | ~ | ||||
| Premium NVMe 16GB ($48) | 16 GB | ~ | ||||
| Premium NVMe 32GB ($96) | 32 GB | |||||
| Dedicated CPU VDS 64GB | 64 GB | ~ |
Inference is CPU-only on these plans. For latency-sensitive workloads, use quantized models (Q4 or Q5) for best tokens per second. Verify exact pricing on the pricing page.
What people are actually building
n8n + Ollama + Telegram bot. Ask questions, get answers from your local Llama 3.1 8B, no OpenAI account needed.
Qdrant or ChromaDB for vectors, Ollama for embeddings and inference, n8n for the orchestration. Search your own knowledge base privately.
LangChain or AutoGen agents calling local models for routine tasks and only hitting paid APIs for hard ones. Cuts API costs by 90%.
Featured deployment guides
Frequently asked
Stop renting tokens. Start owning your stack.
Premium NVMe with 16GB RAM, NVMe storage, full root. Predictable monthly pricing.