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Local AI Deployment Services
Run production AI behind your firewall. No data leaves your building. No per-query API bills. No vendor holding you hostage. We design, deploy, and harden local LLM infrastructure for organizations that treat their data as a competitive asset.
Why organizations move AI in-house
Cloud AI APIs were convenient in 2022. By 2026, the math has changed. A company doing 50,000 LLM calls a day on GPT-4-class cloud APIs is spending $15K–$40K per month — and handing the entire call stream to a third party. Every HIPAA-covered record, every privileged communication, every piece of proprietary IP is sent to someone else's data center to be logged, rate-limited, and silently retrained against.
A one-time hardware spend of $15K–$30K runs comparable inference load on-premise with zero recurring API cost, full data custody, and no rate limits. The break-even on most deployments is under six months.
What a local AI deployment actually includes
Every engagement starts from the same premise: the system has to run in production, on your hardware, with your security posture, under your team's control. That shapes the deliverables.
01. Model selection and sizing
We benchmark candidate open-weight models (Llama, Qwen, Mistral, Gemma, DeepSeek) against your actual workloads — not synthetic leaderboard tasks. Quantization strategy (4-bit, 8-bit, FP16) is chosen per model and per GPU; the tradeoff between latency, throughput, and quality gets measured, not guessed.
02. Inference infrastructure
Ollama for operational simplicity, vLLM for high-throughput production workloads, llama.cpp for edge and CPU deployments. We pick based on what you're actually doing, not what's trending. GPU allocation, batching, KV-cache sizing, context-window policy — all of it instrumented from day one.
03. Integration layer
OpenAI-compatible API wrapper so your existing code using the OpenAI SDK keeps working — you swap a base URL and you're done. Authentication via your existing SSO. Rate limiting per team or per user. Audit logging that survives a compliance review.
04. Hardening and handoff
CI/CD for model updates. Observability stack (Prometheus + Grafana, or your existing tooling). Load testing against your realistic traffic profile. Runbooks for the two things that will go wrong first: GPU memory exhaustion and model-loading race conditions. Your team owns it after handoff; we're here for 30 days of stabilization, then optional retainer.
What you need to bring
- Hardware. One RTX 4090 (24GB VRAM) for 7B–13B models; A100/H100 or multi-GPU rigs for 30B–70B. Apple Silicon (M-series Ultra) works for Mac shops. We'll spec exactly what fits your load in discovery. See our local LLM hardware guide.
- A real use case. "We want AI" is not a use case. "Automate our tier-1 support triage with 65% deflection and human escalation" is. The readiness assessment exists to find the second one.
- An integration target. Helpdesk, document store, CRM, internal chat — whatever system the AI actually needs to move the needle in.
Who this is for
We do our best work with organizations that have already tried cloud AI and run into a wall: compliance blocked them, the API bill got absurd, the vendor's uptime became their problem, or the data-governance review killed the project. If you've been there, the local-deployment conversation is short and productive.
If you're pre-first-prototype, start with the free AI Readiness Assessment instead. We'll tell you honestly whether local deployment is the right answer or whether you should be running something simpler.
How we price it
Fixed fee from $25K–$60K for a POC (single well-defined use case, 4–6 weeks, real integrations, runs on your infrastructure). $75K+ for a production build (hardening, auth, audit logging, CI/CD, observability, load + adversarial testing, 30-day post-launch support). No retainers you didn't ask for. No hour-padding. No $300K discovery phases.
Frequently asked questions
What does "local AI deployment" actually mean?
Running large language models and supporting AI infrastructure on hardware that you own and control — inside your data center, office, or private cloud. All inference happens behind your firewall. No prompts, completions, or embeddings are sent to OpenAI, Anthropic, or any third-party service. You keep full custody of every byte.
How is this different from a cloud AI API with a "private" plan?
Cloud vendors' "private" and "enterprise" plans still execute inference on the vendor's hardware. Your data is encrypted in transit and at rest, but it is processed in their data center by their software. For genuinely sensitive workloads — HIPAA, SOC 2, attorney-client privilege, classified work — that is legally or contractually insufficient. Local deployment means the model weights and the inference engine both run on machines under your physical and network control.
What hardware do we need?
For most small-to-mid deployments running 7B–13B parameter models, a single workstation with an RTX 3090 or 4090 (24GB VRAM) handles real production load. For 30B–70B models, you need enterprise GPUs (A100, H100) or a multi-GPU rig. Apple Silicon M-series Ultra works well for Mac-first organizations. We size hardware in discovery based on your actual throughput, latency targets, and concurrency requirements — not a spec-sheet guess.
How long does a deployment take?
Most local AI deployments are production-ready within two to four weeks. Week one: infrastructure assessment, model selection, environment setup. Week two: deployment, integration, fine-tuning. Weeks three–four: testing, optimization, team training. Multi-agent systems and enterprise-wide document intelligence platforms typically take four to eight weeks. We don't quote timelines we haven't shipped against before.
What happens if the model is wrong or hallucinates?
Every production deployment we ship includes an evaluation harness — a test set of real prompts with known-good responses, scored automatically on every model or config change. For RAG-based deployments, we enforce citation requirements and refuse-to-answer thresholds. We treat LLM error modes as engineering problems with measurable rates, not as mystical properties of the model. If a use case's tolerable error rate is too low for current open models, we tell you that in discovery rather than after the contract is signed.
Ready to start?
Book a free 30-minute AI Readiness Assessment. No pitch deck. No retainer ask. Just a working session to map your stack and surface the two or three highest-ROI AI interventions for your situation.