I will set up your own private ai server on site or in the cloud

Cut your AI bill: run your own models. If your team pays per token to OpenAI, Anthropic, or Google, a private AI server often pays for itself in months, and your prompts, documents, and customer data never leave hardware you control.

Two ways to get there, you pick:

• ON SITE: a workstation or rack server at your office. I size the GPU and RAM for the models you need and help you buy or reuse hardware.
• - IN THE CLOUD: a rented GPU instance on OCI, Hetzner, RunPod, or your preferred provider, with the same private setup and no per-token charges.

What I deploy:

• Ollama or llama.cpp serving open models (Llama, Mistral, DeepSeek, Qwen, Gemma)
• - Open WebUI team chat with user accounts and SSO
• - OpenAI-compatible API endpoint so existing tools keep working
• - Document chat (RAG) over your files, wikis, and PDFs
• - Optional hybrid routing: local first, a cloud API for the hard questions
• - Monitoring, backups, TLS, and a runbook

Honest note: frontier cloud models are still stronger at some tasks. Where I win is self-hosting: private, predictable cost, and no vendor lock-in. The Premium package starts with a cost analysis so you know the numbers before buying anything.