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

Bedrock and Azure host the model. They don't tune the inference infrastructure for your actual load, don't integrate with your auth and audit stack, don't write your fine-tuning pipeline, and don't operate the eval framework. The platform vendors sell raw GPU time. We build the production system around it.

Will our data ever leave our VPC?

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

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

Self-hosted wins when: your data must never egress, your traffic is high enough that per-token API costs exceed GPU costs (usually 10M+ tokens/day), or you need to fine-tune on proprietary data. Managed API wins when: traffic is low/spiky, you want zero infrastructure ops, and the vendor's enterprise contract covers your residency requirements. We do a TCO analysis for both in the scoping call.

How is this different from AI Agents + Workflow Automation?

This service is the model layer (host the LLM, fine-tune it on your data, run the inference infrastructure). AI Agents and Workflow Automation is the layer above that builds workflows using LLMs. Many engagements include both. Start here if you need the foundation in place first; start with Agents if you already have a model and need workflows on top.

What about EU AI Act / HIPAA / SOC 2 compliance?

Those are covered by our separate AI Compliance and Governance service. This service includes baseline security (auth, audit logs, output guardrails) but full regulatory compliance (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. Typical model: lead architect plus 1 to 2 senior engineers from our side, working alongside your 2 to 4 engineers. We prefer this over fully outsourced builds because the knowledge transfer is built in. Your team owns the system after handoff.

Private LLM Hosting and Custom Model Training

Private LLMs in your environment, not ours

Public AI APIs (ChatGPT, Claude, Gemini) are convenient. They are also the wrong answer when your data includes regulated customer records, source code, board documents, or anything that triggers 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 on top, see AI Agents and Workflow Automation. For regulatory frameworks (HIPAA, EU AI Act, SOC 2), see AI Compliance and Governance. For connecting AI to your existing platforms (Epic, Clio, Yardi, etc.), see AI Integration Depth.

When to talk to us

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

What you get

Three project tiers plus an ongoing hosting retainer.

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. Optional managed-API path (AWS Bedrock, Azure OpenAI, Vertex AI) when self-hosting doesn't make sense.

What's included:

Model selection and architecture review. Which model, why, 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 parity check vs production.
2-week shadow period with your engineering team. 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. Result: a model that knows your industry jargon, internal terminology, and domain-specific reasoning patterns.

Everything in Private LLM deployment, plus:

Training-data pipeline. Document ingestion, cleaning, deduplication, PII redaction.
Fine-tuning runs (LoRA, QLoRA, or full fine-tune depending on data volume and target quality).
Eval framework. Golden test set + automatic regression checks.
Model card and datasheet (NIST AI RMF compliant).
A/B framework for swapping between fine-tuned and base models.
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. Different models for different use cases (a small fast model for classification, a large model for synthesis, a specialist model for one vertical).

Everything in Custom training, plus:

Model routing layer (request → right model based on use case).
Multi-model eval framework.
Model lifecycle ops (versioning, rollback, deprecation).
Centralized observability across all models.
6 weeks of post-launch tuning included.

Ongoing hosting retainer ($5,000 to $25,000 per month)

For the private LLMs we deploy for you. Optional. Your team can run its own ops after handoff.

What's included:

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

Guarantee

Every model has to hit its agreed eval target before we send the final invoice. Eval set is co-defined in the contract (typically 200 to 500 of your real production samples). If we miss the target, we re-tune or rebuild on our dime.

Payment terms

50 percent on contract signing. 25 percent at midpoint. 25 percent on acceptance. INR pricing on request for India-based clients.