Why pay for hosting when AWS Bedrock or Azure OpenAI already does this?

Because Bedrock and Azure host the model, not the system around it. They do not tune the inference infrastructure for your actual load, do not integrate with your auth and audit stack, do not write your fine-tuning pipeline, and do not operate an eval framework. The platform vendors sell raw GPU time or API calls; this service builds the production system that sits around it.

Will our data ever leave our VPC?

No, by default. The architecture keeps model inputs and outputs inside your environment. If you choose a managed model API (Bedrock, Azure OpenAI, Vertex AI), the call routes through that vendor's network and data residency follows their enterprise terms. If you choose a self-hosted model (Llama 4, Mistral, or similar), data stays entirely on your own infrastructure with zero egress.

Self-hosted vs managed API: which should we pick?

It depends on your traffic and your data rules; see the comparison table above. Self-hosted tends to win once data must never leave your infrastructure, or once traffic is high and steady enough that GPU cost beats per-token pricing. Managed API tends to win when traffic is low or spiky and you want zero infrastructure to operate. We run a cost comparison against your actual numbers in the scoping call rather than applying a generic rule of thumb.

How is this different from AI Agents and Workflow Automation?

This service is the model layer: hosting the LLM, fine-tuning it on your data, and running the inference infrastructure. AI Agents and Workflow Automation is the layer above it, building workflows that use those models. Many engagements include both. Start here if you need the model foundation in place first; start with Agents if you already have a model and need workflows built on top of it.

What about EU AI Act, HIPAA, or SOC 2 compliance?

Those are covered by a separate AI Compliance and Governance service. This service includes baseline security (authentication, audit logs, output guardrails), but full regulatory compliance work (model cards, bias testing, incident response, conformity assessments) is a different engagement with different deliverables and a different scope.

Can you work alongside our existing engineering team?

Yes. For a project like this, staffing is a lead architect plus 1 to 2 senior engineers brought in for the engagement, working alongside your 2 to 4 engineers. This is preferred over a fully outsourced build because knowledge transfer happens as the work happens, not at the end. Your team owns the system after handoff.

Private LLM Hosting and Custom Model Training

Private LLMs in your environment, not ours

Public AI APIs such as ChatGPT, Claude, and Gemini are convenient. They are the wrong choice once your data includes regulated customer records, source code, board documents, or anything that would trigger a CISO veto. This service builds the foundation: private LLM deployment in your VPC or on-prem environment, plus optional fine-tuning on your own data.

This is the model layer only. For agent workflows built on top of it, see AI Agents and Workflow Automation. For regulatory frameworks such as HIPAA, the EU AI Act, or SOC 2, see AI Compliance and Governance. For connecting AI to your existing platforms (Epic, Clio, Yardi, and similar), see AI Integration Depth.

When to talk to us

Consider this service when one of these situations fits:

Your CISO has blocked or restricted public AI tools, and your team is shadow-using them anyway.
You hold proprietary data (customer records, internal docs, code, research) that must not leave your VPC or your jurisdiction.
You are evaluating self-hosted Llama 4, Mistral, or other open-weight models and want vendor-neutral architecture advice.
You need a model fine-tuned on your own data: industry jargon, internal terminology, domain-specific reasoning.
You work in a regulated industry (BFSI, healthcare, defense, legal, government) where a public API is a non-starter.

What you get

Four options: three fixed-scope project tiers, plus an ongoing hosting retainer once a system is live.

Tier	Price	Duration	What it gets you
Private LLM deployment	$50,000 to $150,000	4 to 8 weeks	An open-weight model running securely in your environment
Custom model fine-tuning	$80,000 to $250,000	8 to 16 weeks	The above, plus a model trained on your own data
Multi-model platform	$200,000 to $500,000	16 to 24 weeks	Several models running in parallel, routed by use case
Ongoing hosting retainer	$5,000 to $25,000 per month	Ongoing	We operate what was deployed, after handoff

Private LLM deployment: $50,000 to $150,000, 4 to 8 weeks

Deploy an open-weight model (Llama 4, Mistral, or similar) in your environment. An optional managed-API path (AWS Bedrock, Azure OpenAI, Vertex AI) is available when self-hosting is not the right fit.

What is included:

Model selection and architecture review: which model, why, and what hardware it needs.
Deployment to your VPC, on-prem cluster, or air-gapped environment.
Inference infrastructure (vLLM, TGI, Triton, or similar), tuned for your latency and throughput targets.
Authentication, audit logging, and basic guardrails (prompt injection defense, output validation).
Sandbox testing and a parity check against production.
A 2-week shadow period with your engineering team, plus a runbook and architecture-decision-record handoff.

Custom model fine-tuning: $80,000 to $250,000, 8 to 16 weeks

Fine-tune an open-weight model on your data. The result is a model that understands your industry jargon, internal terminology, and domain-specific reasoning patterns.

Everything in Private LLM deployment, plus:

A training-data pipeline: document ingestion, cleaning, deduplication, PII redaction.
Fine-tuning runs (LoRA, QLoRA, or a full fine-tune, depending on data volume and target quality).
An eval framework: a golden test set plus automatic regression checks.
A model card and datasheet (NIST AI RMF compliant).
An A/B framework for switching between the fine-tuned model and the base model.
4 weeks of post-launch tuning included.

Multi-model platform: $200,000 to $500,000, 16 to 24 weeks

For organizations running multiple LLMs in parallel: a small fast model for classification, a large model for synthesis, a specialist model for one vertical.

Everything in Custom model fine-tuning, plus:

A model routing layer (each request goes to the right model for its use case).
A multi-model eval framework.
Model lifecycle operations: versioning, rollback, deprecation.
Centralized observability across every model.
6 weeks of post-launch tuning included.

Ongoing hosting retainer: $5,000 to $25,000 per month

For the private LLMs deployed under one of the tiers above. Optional: your team can run its own operations after handoff instead.

What is included:

Managed model serving: we operate the inference infrastructure.
A monthly performance report covering latency, throughput, cost per token, and drift.
Model upgrade support when better open-weight models ship.
Re-tuning when your data shifts (typically quarterly).
A same-week response when something breaks.

Self-hosted or managed API

Both are legitimate choices. Which one fits depends on your traffic, your data rules, and how much infrastructure you want to run yourself.

Factor	Self-hosted tends to win	Managed API tends to win
Data residency	Data must never leave your infrastructure	The vendor's enterprise contract already covers your residency requirement
Traffic pattern	High and steady, where GPU cost undercuts per-token pricing	Low or spiky, where per-token pricing beats running idle GPUs
Fine-tuning need	You need to train on proprietary data	An off-the-shelf model is already good enough
Operations appetite	Your team wants full control of the stack	You want zero infrastructure to operate yourself

We run a cost comparison for both paths in the scoping call before you commit to either.

Guarantee

Every model has to hit its agreed eval target before the final invoice goes out. The eval set is co-defined in the contract, typically 200 to 500 of your own real production samples. If the target is missed, we re-tune or rebuild at our cost.

Payment terms

50 percent on contract signing, 25 percent at the project midpoint, 25 percent on acceptance. INR pricing is available on request for India-based clients.