Private LLM Deployment: Your Own Model, Inside Your Own Infrastructure
Most vendors sell you an API key and call it "AI." We do the opposite — we stand up a private LLM inside your VPC or on-prem hardware, fine-tune it on your data, and hand you a model your security team actually signs off on. No prompts leaving your firewall, no surprise token bills, no vendor lock-in.
Your data never leaves your network
The model runs where your data already lives — your AWS, Azure, or GCP VPC, or your own racks. Prompts, documents and embeddings stay inside your perimeter, which is the only version of "AI" most finance and healthcare compliance teams will approve.
A custom AI model tuned on your domain
We fine-tune an open-weight base (Llama, Mistral, Qwen and similar) on your tickets, contracts, claims or knowledge base. You get a custom AI model that speaks your domain — not a generic chatbot pretending to understand your business.
Predictable cost, no per-token meter
Once deployed on your infrastructure, inference cost is your compute — not a usage meter that spikes the month a feature goes viral. We size the hardware honestly and tell you the real GPU bill before you commit a dollar.
You own the weights and the pipeline
At handoff you keep the model weights, the fine-tuning scripts, the eval harness and the deployment config. No black box. If you ever fire us, nothing breaks and nothing walks out the door with us.
What a private LLM deployment actually includes
A private LLM is a language model that runs entirely on infrastructure you control instead of a third-party API. We handle the full build: selecting an open-weight base model sized to your use case, provisioning GPU inference in your VPC or on-prem cluster, and fine-tuning it on your own data so answers are grounded in your reality. We wire in retrieval (RAG) over your documents, add guardrails and logging your auditors can read, and ship an evaluation harness so you can prove accuracy before it touches a customer. The deliverable is a working enterprise AI deployment — internal copilots, document analysis, support drafting, structured extraction — not a slide deck about what's possible.
Why most "AI" vendors underperform on this
Plenty of shops call themselves an AI ML development company but quietly route everything to a public API and bill you the markup. That's fine for a marketing demo and a real problem for regulated data — your contracts, patient records and transaction logs leave your control the moment a prompt is sent. The other failure mode is the opposite: an over-engineered research project that never ships. As an llm company that lives in production, we keep the scope tight, deploy on infrastructure you already trust, and measure the model against your tasks instead of public benchmarks. Fewer demos, more systems that survive a security review and a real workload.
How engagements work and what they cost
We start with a paid scoping sprint, typically $4,000–$8,000, where we map your data, pick the base model, and confirm your VPC or on-prem setup can carry it — you leave with a concrete architecture and a fixed quote either way. Full private LLM deployments generally run $25,000–$90,000+ depending on model size, fine-tuning depth, and integration surface, billed in milestones (scope, deploy, fine-tune, handoff). Ongoing model tuning and monitoring is an optional retainer from roughly $3,000/month. We invoice in USD and accept Stripe, Wise, or ACH, so US, UK and EU teams can pay the way their finance department prefers. No retainer is required to get a real number.
Common Questions
FAQ
A paid scoping sprint runs about $4,000–$8,000 and gives you the architecture plus a fixed quote. Full deployments typically land between $25,000 and $90,000+ depending on model size, how deep the fine-tuning goes, and how many systems we integrate with. Optional ongoing tuning and monitoring starts around $3,000/month. We bill in USD via Stripe, Wise, or ACH, and you get the real number before committing.
The scoping sprint is usually 1–2 weeks. A typical first production deployment — model stood up in your VPC or on-prem, fine-tuned on your data, with retrieval and guardrails in place — takes about 6–10 weeks. Heavier fine-tuning, multiple data sources, or strict compliance sign-off can push that to 12+ weeks. We work in milestones, so you see a running model long before final handoff.
No. That's the entire point of a private LLM. The model, the fine-tuning, and inference all run inside your VPC or on your own servers. Your prompts, documents, and training data stay within your network perimeter, which is what makes this approach workable for finance, healthcare, and other data-sensitive industries that can't send records to a public API.
Our AI automation and chatbot work usually runs on top of hosted APIs and is ideal when data sensitivity is low and speed matters. A private LLM is the heavier, higher-ticket option for teams who need a custom AI model running on infrastructure they control — full ownership of the weights and pipeline, no data leaving the building. Different problem, different engagement. We'll tell you honestly which one you actually need.
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