Private / On-Prem LLM Deployment
Private LLM Deployment Services to Enhance Your Applications with Powerful AI Capabilities
Our private LLM deployment services put a production-grade large language model inside your own infrastructure — no data leaving your environment, no third-party API dependency, no compliance compromise — so you get the capability of frontier AI on your terms.
Book a Free Architecture Audit →
The Problem With SaaS LLM APIs for Regulated Industries
For most applications, calling a managed LLM API is the right answer — fast, scalable, and low-overhead. For regulated industries, it's frequently not an option. Healthcare data can't leave a HIPAA-compliant boundary. Financial data has jurisdictional residency requirements. Defense and government workloads operate in air-gapped environments. Legal matters carry strict confidentiality obligations.
The alternative isn't going without AI. It's deploying the model inside your own infrastructure, where your security and compliance controls apply to it the same way they apply to everything else you run.
Managed API vs. Private LLM Deployment
Managed LLM API (OpenAI, Anthropic, Google)
Data residency: Data processed on provider infrastructure — jurisdiction varies
Compliance control: Limited — subject to provider's compliance posture
Availability dependency: Dependent on provider uptime and API continuity
Cost at scale: Per-token pricing — costs grow linearly with usage
Customization: Prompt engineering and fine-tuning via API only
Best for: Applications without strict data residency or compliance requirements
Private LLM Deployment
Data residency: Fully within your infrastructure — no data leaves your environment
Compliance control: Complete — your security, access, and audit controls apply
Availability dependency: Self-managed — no external API dependency
Cost at scale: Fixed infrastructure cost — marginal cost per inference approaches zero
Customization: Full — fine-tune, quantize, and configure the model directly
Best for: Regulated industries, air-gapped environments, high-volume applications where per-token costs are prohibitive
What We Deploy
Open-source model selection — Llama 3, Mistral, Gemma, Qwen, and others evaluated against your use case, hardware constraints, and commercial licensing requirements
Quantization and optimization — model quantization (GGUF, AWQ, GPTQ) to fit your available hardware without unacceptable quality degradation
Inference server setup — production-grade serving with vLLM or Ollama, configured for your throughput and latency requirements
SSO and access control integration — model API access gated behind your existing identity provider (Okta, Azure AD, or equivalent)
Audit logging — every query and response logged with user identity, timestamp, and retention policy matching your compliance requirements
GPU infrastructure provisioning — on-premise GPU server selection and setup, or private cloud (AWS VPC, Azure Private, GCP VPC) with no public egress
Air-gapped deployment — for environments with no internet access, full offline deployment including model weights and inference dependencies
Ongoing managed retainer — model updates, infrastructure patching, and performance monitoring without requiring your team to maintain deep LLM infrastructure expertise
Who This Is For
Healthcare
HIPAA-compliant LLM deployment for clinical documentation, patient data analysis, and internal tools — model running inside your existing HIPAA-compliant infrastructure boundary, not calling an external API with patient data in the payload.
Financial Services
Data residency compliance for LLM features processing customer financial data, transaction records, or regulated documents — deployed within your existing compliant infrastructure, subject to your existing access controls and audit logging.
Legal
Client confidentiality requirements that prevent sending matter content to a third-party API. Private deployment means privileged data never leaves your environment.
Defense and Government
Air-gapped LLM deployment for environments with no external network access. Full offline deployment with no dependency on internet connectivity or external services.
High-Volume Applications
For applications processing millions of inferences per month, the per-token cost of a managed API often exceeds the cost of self-hosted infrastructure within 6–12 months. Private deployment becomes the economically correct choice at scale.
Trusted Across 50+ Countries
Codersarts maintains a 4.9/5 client satisfaction rating across hundreds of engagements. Clients working on complex, high-stakes infrastructure consistently highlight thoroughness and reliability — Vivek (India) described the team's technical depth as making a genuinely difficult infrastructure project manageable, while Li (China) noted the team's patience and follow-through across a long and technically demanding engagement.
Results
A multi-location hospital network deployed a private LLM for clinical documentation summarization within their existing HIPAA-compliant infrastructure, achieving processing throughput that would have cost roughly 4x as much via managed API at their query volume.
A financial services firm met jurisdictional data residency requirements for an LLM-powered document analysis tool by deploying a fine-tuned 13B model on private cloud infrastructure, passing a compliance audit that a managed API implementation would have failed.
A legal services company deployed an air-gapped LLM for internal contract analysis — matter content never leaves their network, satisfying client confidentiality requirements that had previously blocked any AI adoption.
(Client names withheld under NDA; case studies available on request.)
Pricing
Starter
Scope: Single-model deployment, inference server setup, basic access control
Price: $30,000–$45,000 + $1,500/mo retainer
Production
Scope: Optimized model serving, SSO integration, audit logging, monitoring dashboards
Price: $45,000–$65,000 + $2,000/mo retainer
Enterprise / Air-Gapped
Scope: Full air-gapped deployment, GPU infrastructure provisioning, compliance documentation, multi-model support
Price: $65,000–$80,000+ + $3,000/mo retainer
For context: enterprise on-premise LLM implementations in the US market typically run $150,000–$500,000+ for comparable scope. Our pricing reflects high-quality offshore delivery at 35–55% of those rates, with the same production-grade engineering standards.
How We Work
Compliance and infrastructure audit (Week 1) — map your compliance requirements, existing infrastructure, and hardware constraints
Model selection and optimization (Week 2) — select and benchmark candidate models against your use case and hardware
Deployment build (Weeks 3–6) — inference server, access control, audit logging, monitoring
Compliance validation (Week 7) — test against your compliance requirements, produce documentation for audit
Handover + retainer — team trained on operational procedures; ongoing retainer for model updates and infrastructure management
Why Codersarts
As an on-premise LLM deployment company, we've navigated the intersection of compliance requirements and LLM infrastructure across healthcare, financial services, and legal — three industries where the compliance stakes are real and the margin for error is low. Every deployment includes audit logging, access control, and compliance documentation from day one, because retrofitting these into a running system is significantly harder and more expensive than building them in.
Related Services
LLM Fine-Tuning — the most common complement to private deployment, for domain-adapting the self-hosted model to your specific use case
MLOps / LLMOps Infrastructure — for production monitoring and model CI/CD on top of the private deployment
AI Document Intelligence — for regulated industries that need document processing entirely within their own infrastructure
AI Strategy & Architecture Audit — if you're unsure whether private deployment is the right approach vs. a compliant managed API option
Get Started
Book a Free Architecture Audit →
FAQ
Which open-source models do you support? Llama 3, Mistral, Gemma, Qwen, and other leading open-source models. We recommend based on your use case, hardware, and whether you need a commercially licensable model. We stay current as the open-source model landscape evolves.
What hardware do we need? It depends on the model size and your throughput requirements. We provide hardware recommendations during scoping — ranging from a single A100 GPU server for moderate workloads to multi-GPU configurations for high-throughput production use cases. Cloud-based private deployment (AWS, Azure, GCP private infrastructure) is also available if on-premise hardware isn't feasible.
Can you deploy in an air-gapped environment with no internet access? Yes — air-gapped deployment is a standard offering. All model weights, inference dependencies, and monitoring tooling are packaged for offline installation. No internet connectivity is required during or after deployment.
How do model updates work after deployment? Model updates are managed as part of the retainer — we test new model versions against your use case before updating, with rollback available if the update degrades performance. You're never forced to update on the model provider's timeline.
What compliance documentation do you produce? We produce a deployment architecture document, access control configuration documentation, and audit log specification suitable for presenting to a compliance team or external auditor. Specific compliance frameworks (HIPAA, SOC 2, ISO 27001) can be mapped on request.