Private LLM Deployment: Running AI Without Sending Data to OpenAI

Every company we talk to wants AI features. Almost none of them can say where their prompts go. If your data flows through a third-party API, your privacy posture is a terms-of-service document. We run models on GPUs we own — here's the architecture and why it's cheaper than it sounds.

The stack is boring, on purpose

Serving: Ollama or vLLM on a dedicated RTX 3090 (24GB VRAM). Models: open-weight — Qwen, Llama, Mistral, DeepSeek — in the 7B–14B range quantized to 4–8 bit. That size class now handles summarization, classification, extraction, support drafting and tool-calling well enough that most business workloads don't need a 400B API model. Your app calls an OpenAI-compatible endpoint; it can't tell the difference, except the endpoint is 10 meters from your database.

Fine-tuning is the actual unlock

A 7B model trained on your documents beats a frontier model guessing at them. We run LoRA/QLoRA pipelines — Soup, PEFT, TRL — on the same GPU. A few hundred good examples (your past tickets, your product docs, your tone) produce an adapter in hours, not weeks. The result: a model that answers in your vocabulary about your business, for a flat monthly cost.

RAG without a SaaS bill

Retrieval over your documents — embeddings, a vector index, re-ranking — runs entirely on the same box. No Pinecone invoice, no "your data will be used to improve our service" clause buried on page 14. Every call is inspectable: you can log, replay and audit every prompt and response, because the whole pipe is yours.

The economics

API pricing is a meter that runs while you sleep. A product making a million short calls a month pays that meter every month, forever, and it grows with usage. Private inference flips it: the GPU is a fixed cost whether it serves a thousand requests or a million. The crossover point where owned inference beats API pricing arrives surprisingly early — usually within the first months of real usage.

When to still use the APIs

Be honest: the frontier models are smarter. If your workload needs top-tier reasoning — long-horizon agents, complex code generation, nuanced analysis — rent the frontier for those calls and keep everything else private. We build exactly that: a router that sends hard queries out and keeps the bulk (and the sensitive bulk) inside. Best of both, auditable either way.

What a deployment looks like

Two weeks, typically: model selection against your actual task, a serving endpoint, a fine-tune run on your data if needed, monitoring, and a runbook. Then a flat monthly number for the hardware and operations. If that sounds like the part of the AI bill nobody quotes you — it is.