Private LLM Deployment

Bring large language models into your own data center
Data stays in your domain, intelligence stays at full strength

From on-prem open-source deployment to industry model fine-tuning, we help you run a fully private LLM on your own servers that truly understands your business. Zero leakage of sensitive data, and compared with calling commercial APIs directly, inference costs can typically be reduced by roughly 40–60%—making AI both secure and cost-effective.

Starter · On-Prem Open-Source Deployment

Run a fully private large language model on your own servers, with data that never leaves your domain.

From $3,000one-time
  • Selection and on-prem deployment of mainstream open-source LLMs (Llama / Qwen / DeepSeek, etc.)
  • Single-machine or single-GPU deployment with packaged inference service
  • Private chat interface + basic RAG knowledge base (connected to your internal documents)
  • OpenAI-compatible API for a smooth migration of your existing systems
  • Data stays on your servers end to end, with zero leakage of sensitive information
  • Deployment documentation + one team training session

From $3,000. Pricing depends on model size / hardware environment / knowledge base data volume—most on-prem deployments land in $3,000–$8,000 (final price subject to requirements assessment).

Recommended

Advanced · Industry Model Fine-Tuning

Fine-tune a dedicated model on your own business data so it truly understands your industry and workflows.

From $12,000one-time

On top of private deployment, we perform instruction fine-tuning and alignment on your industry corpus, layered with a high-availability inference cluster, an API gateway, and a complete access-audit, monitoring, and alerting system. The Advanced "from $12,000" is a bundled price covering several core modules together; you can also add only the modules you need individually (see the per-module pricing below).

  • Includes everything in the Starter tier
  • Make the model answer in your industry's terminology and follow your standards (industry fine-tuning / LoRA / domain alignment)
  • Turn internal documents into traceable smart Q&A (enterprise-grade RAG knowledge base: multi-source ingestion, vector retrieval, citation tracing, scheduled incremental updates)
  • Serve more users on fewer GPUs and amortize long-term cost (multi-GPU optimization / quantization)—compared with calling commercial APIs directly, inference costs can typically be reduced by roughly 40–60%
  • Unified API gateway: rate limiting, billing, multi-model routing, and canary releases
  • Fine-grained access control and auditing: access governed by department / role, with full conversation logs that remain traceable
  • Real-time monitoring and alerting: throughput, latency, GPU memory, and anomalous requests on a single screen
  • Post-launch tuning iterations and operations support

From $12,000. Pricing depends on fine-tuning data volume / model parameter size / operations scope—most fine-tuning projects land in $12,000–$40,000 (final price subject to requirements assessment).

Core Modules

A complete private LLM capability stack

From fine-tuning and knowledge bases to deployment optimization, gateway, access control, monitoring, and e-commerce store automation, these modules combine into an enterprise-grade private LLM platform that is ready to launch, operate, and keep compliant.

Make the model answer in your industry's terms (industry fine-tuning / LoRA)

Fine-tune on your own tickets, contracts, and reports so the model speaks your terminology and follows your rules.

BeforeAsk a general model a question about your spec and it invents a plausible-sounding answer that uses the wrong part numbers and ignores your internal SOPs.
With AIWe fine-tune (LoRA / domain alignment) on your real corpus—work tickets, contracts, QC reports—so it uses your exact terms, part codes, and approval steps.
OutputA model that answers in-house questions in your own language and format, with usability noticeably above the off-the-shelf model (gains vary by project).

from $4,000

Let staff get answers with sources (vector retrieval / RAG knowledge base)

Turn scattered internal docs into traceable Q&A, with every answer citing where it came from.

BeforeStaff dig through shared drives, PDF manuals, and old email threads to find one answer, and key know-how walks out the door when a veteran leaves.
With AIWe index your documents, product manuals, and past cases into a vector knowledge base; the model retrieves the relevant passages and answers with a citation back to the source file and page.
OutputEmployees ask in plain language and get a sourced answer in seconds, with the knowledge base auto-updating as new documents land.

from $2,000

Serve more users on fewer GPUs (KV caching / multi-GPU parallelism)

Get more throughput from the hardware you already have, so cost per request stays low.

BeforeEither you pay per token to a commercial API on every call, or your single GPU chokes the moment a few dozen people use the assistant at once.
With AIWe apply quantization, request batching, KV caching, and multi-GPU parallelism so one cluster serves many concurrent users at stable latency.
OutputMore users served on the same hardware; in our projects, inference cost versus calling a commercial API directly is typically reduced by roughly 40–60% (actual savings vary with usage).

from $1,500

Connect existing systems with zero rework (OpenAI-compatible / API gateway)

One gateway for every model call—rate limiting, billing, routing, and canary releases included.

BeforeEach app wires straight to its own model with hard-coded keys, so there's no way to throttle abuse, see who spent what, or swap models without a rewrite.
With AIWe put all calls behind one OpenAI-compatible gateway that handles rate limiting, per-team billing, multi-model routing, and canary releases.
OutputExisting tools point at the new endpoint with effectively zero code changes, and you can route, meter, and roll out new models from a single control point.

from $1,200

Control who can ask what (access control / audit logging)

Govern access by department and role, with every conversation and file lookup logged.

BeforeOnce the assistant is live, anyone can ask anything and nobody can prove later who saw which sensitive document.
With AIWe enforce access by department and role, restrict which knowledge bases each group can query, and log every conversation and file access.
OutputA full, traceable audit trail that satisfies compliance and internal-control reviews, so sensitive-data usage stays manageable and controlled.

from $1,000

Catch faults early (real-time monitoring / alerting)

Watch throughput, latency, GPU memory, and anomalies live, with alerts before users notice.

BeforeThe first sign of trouble is a colleague complaining the assistant is slow or down, hours after it actually broke.
With AIWe surface throughput, latency, GPU memory, and anomalous requests on one dashboard, with threshold-based alerts pushed to your team automatically.
OutputFaults get caught and handled early instead of after the fact, keeping the private LLM running stably around the clock.

from $800

Turn product launches into a one-click pipeline (e-commerce store automation)

From your own product data, auto-generate listing copy, images, titles, and SEO—on-prem.

BeforeListing a new SKU means a person hand-writing copy and selling points, cutting out product photos, and crafting titles plus SEO descriptions—often a few hours per item, repeated per language.
With AIWe fine-tune a private model on your catalog so that from one product's internal data it drafts multilingual copy and selling points, cuts out the product image and builds scene shots, and writes the listing title plus SEO description.
OutputA near one-click pipeline that turns hours of manual listing work per item into minutes, multiplying launch speed—and because it runs on-prem, your catalog data never leaves your servers.

from $2,500

Architecture Overview

A private architecture that keeps data in your domain

All models, knowledge bases, and inference services are deployed inside your internal network, where external networks cannot reach the data layer directly. Requests are uniformly authenticated and rate-limited through the API gateway, audited end to end, and sensitive information remains on your servers throughout.

Private LLM deployment architecture diagram: user requests pass through the API gateway and access auditing into an internal inference cluster and RAG knowledge base; data never leaves the domain and runs in a closed loop entirely within the customer's own servers
Implementation Process

From diagnosis to stable operations, four steps to launch

A private LLM isn't install-and-leave—we use verifiable small steps to fit it to your hardware, data, and compliance requirements.

Diagnose

Weeks 1–2

Inventory use cases, data assets, and hardware/compliance constraints, decide on model selection and deployment shape, and produce a feasibility and ROI assessment.

Pilot

Weeks 3–6

Complete on-prem deployment and basic RAG, fine-tune 1–2 high-value scenarios on your industry corpus, and validate accuracy and response experience with real Q&A.

Launch

Weeks 7–12

Stand up a multi-GPU inference cluster and API gateway, wire in access auditing and monitoring/alerting, and migrate existing systems to production smoothly via the OpenAI-compatible interface.

Iterate

Ongoing

Keep fine-tuning and expanding the knowledge base from real call feedback, optimize throughput and cost, and keep it running stably 24/7.

Project Example (anonymized)

An internal knowledge assistant for a regional manufacturer

A manufacturing client had process and QC documents scattered across systems, with veteran know-how hard to capture and new hires often searching several systems for answers. We deployed an open-source model inside their network, connected an enterprise-grade RAG knowledge base, and fine-tuned on their process documents—sensitive data never left the internal network. After launch, the team reported a noticeable improvement in how quickly they could find information, and new hires got up to speed faster.

  • Industry: discrete manufacturing (anonymized)
  • Deployment: on-prem open-source model + enterprise RAG + industry fine-tuning, data never leaves the domain
  • Improvement: noticeably faster internal document lookup (in our projects, search time can commonly be cut substantially)
  • Cost: compared with calling commercial APIs directly, inference costs can typically be reduced by roughly 40–60%

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