Self Hosted AI Infrastructure

Private AI deployment that never touches a cloud API. HIPAA, GDPR, and PCI-DSS compliant by architecture, not by policy. Built on Ollama, vLLM, and Open WebUI, running entirely inside your environment.

If your organization handles protected health information, attorney-client communications, or regulated financial data, this page is for you. Where your AI runs is not a preference decision. For many regulated organizations, it is a legal one.

Why 44% of enterprises still haven't deployed AI#

Data privacy is the #1 blocker, not cost or capability#

The technology is not the obstacle. According to Kong's 2025 Enterprise AI Report, 44% of enterprises cite data privacy and security as the top barrier to LLM adoption, outranking cost, implementation complexity, and lack of skilled staff. The models exist. The use cases are clear. The problem is that most AI infrastructure is designed for someone else's compliance requirements, not yours.

Healthcare organizations cannot send clinical notes to an external API and remain HIPAA-compliant. Law firms cannot process privileged documents through a commercial model without destroying privilege. Financial services firms operating under SEC Regulation S-P and FINRA Rule 3110 cannot route customer information through third-party cloud infrastructure and satisfy their recordkeeping obligations. These are not edge cases. They describe most of the high-value AI use cases in regulated industries.

What "private AI" actually means (and what it doesn't)#

A large portion of what gets marketed as "private AI" is a cloud API behind a branded interface, sometimes with a VPN wrapper, sometimes without. Your data still transits third-party infrastructure. It is still processed under that provider's terms of service. Their data retention policies still apply. This does not satisfy HIPAA's minimum necessary standard, GDPR's data residency requirements, or attorney-client privilege doctrine.

Genuine private AI means the inference happens inside your environment. The model weights sit on hardware you control. Network traffic never leaves your perimeter. No external API call is made. The architecture is the compliance mechanism, not a checkbox in a vendor's settings panel.

The compliance gap that cloud APIs cannot close#

Cloud AI providers offer BAAs. Some have strong security programs. Neither makes them suitable for every workload. A Business Associate Agreement covers liability. It does not change where your data goes, who processes it, or what access it creates.

In February 2026, a U.S. District Court in the Southern District of New York ruled that documents created using commercial generative AI tools and shared with an attorney are not protected by attorney-client privilege. Communications with public AI platforms lack the required confidentiality elements because the data is transmitted to and processed by a third party outside the privileged relationship. This is a binding ruling, not a theoretical risk, and it applies to any legal team using a commercial cloud model for document work.

FINRA's 2025 Regulatory Oversight Report identified recordkeeping, customer information protection, and compliance with Reg BI as primary AI risk areas for financial services firms using generative AI. Self hosted infrastructure, with full audit logging under your control, is the only architecture that satisfies these requirements without structural compromise.

What self hosted AI infrastructure includes#

A full-stack private deployment is not a single tool. It is a coordinated system of components, each of which needs to be correctly configured, integrated, and maintained.

Private LLM inference (Ollama, vLLM, llama.cpp)#

The inference layer is the core of the system, where your prompts are processed and responses are generated, entirely inside your environment. We deploy the right inference runtime for your workload:

• Ollama for team deployments where simplicity, fast startup, and broad model support matter. Ollama runs Llama, Mistral, Qwen, Phi, and dozens of other open-weight models with a straightforward API and minimal infrastructure overhead.
• vLLM for production, high-throughput environments where concurrent users, token throughput, and latency are operational requirements. vLLM's PagedAttention architecture delivers significantly better GPU utilization than naive inference approaches.
• llama.cpp for CPU-only or edge deployments where GPU hardware is not available or appropriate.

Model selection is part of every engagement. We evaluate open-weight models against your specific workload, document analysis, code generation, summarization, question-answering, and size accordingly. We do not deploy a single default model and call it done.

RAG systems built on your documents and internal data#

Retrieval-augmented generation connects your LLM to your organization's knowledge base. Instead of relying only on what the model was trained on, the system retrieves relevant context from your internal documents, contracts, clinical guidelines, compliance policies, operating procedures, and includes it in the prompt at query time.

We build RAG pipelines that ingest your document formats (PDF, DOCX, HTML, plain text), chunk and embed them using appropriate models, store vectors in a database you control, and retrieve accurately at query time. The result is a system that answers questions about your specific data, not generic internet knowledge.

Workflow automation running inside your network#

An LLM sitting in isolation is a chat interface. Integrated into your workflows, it becomes an operational asset. We connect self hosted inference to internal automation pipelines built on n8n (self hosted) or custom Python orchestrators, enabling use cases like:

• Automated document triage and classification
• Prior authorization drafting and review queues
• Contract clause extraction and flagging
• Client intake processing
• Compliance monitoring against internal policy documents

All automation runs inside your network. No data transits external services as part of the workflow.

User interface and access control (Open WebUI, custom portals)#

Your team needs a usable interface. We deploy Open WebUI, a capable, self hosted chat interface, configured with LDAP or Active Directory authentication so access is governed by your existing identity infrastructure. Users log in with their corporate credentials. Permissions are role-based. Every session and query is logged to your audit trail.

For organizations with specific workflow needs, we build custom web portals that expose AI capabilities in the context of a specific task rather than a general-purpose chat interface. This reduces training requirements and improves adoption.

See the OpenClaw deployment case for an example of a custom portal built on top of private LLM infrastructure.

How we build it#

Step 1: infrastructure and compliance assessment#

Before recommending a stack or a model, we need to understand what you are actually required to comply with, what data will be processed, and what infrastructure you currently have. We review your regulatory obligations, HIPAA, GDPR, PCI-DSS, SOC 2, FINRA, or a combination, and map them to architectural requirements. We identify what existing systems the AI infrastructure needs to integrate with, and where the data boundaries must sit.

This step produces a written scope document that defines the deployment architecture, security controls, and compliance posture. Every subsequent decision is made from it.

Step 2: model selection and hardware sizing#

Not every workload requires the same model or the same hardware. We evaluate your use case against available open-weight model options and recommend what is appropriately sized for your workload and budget. A team of 15 doing document summarization has different requirements than a 300-person firm running concurrent clinical note drafting.

We provide hardware specifications, GPU requirements, RAM, storage, network configuration, and work with your infrastructure team or preferred hardware vendor to ensure the environment is correctly provisioned before we begin deployment.

Step 3: deployment, hardening, and integration#

We deploy and configure the inference runtime, RAG pipeline, automation layer, and user interface. We harden the environment: network segmentation, firewall rules, encrypted volumes, audit logging, role-based access control. We integrate with your authentication infrastructure (LDAP, Active Directory, SAML) and connect to the internal systems the AI workflows need to reach.

We do not hand you a Docker Compose file and a README. We deploy into your environment, verify the system is operating correctly, and document what was built and why.

Step 4: user access, monitoring, and handoff documentation#

The deployment is not complete until your team can operate it. We configure monitoring, model health, request latency, error rates, and set up alerting appropriate to your operations posture. We produce handoff documentation that covers the system architecture, configuration decisions, maintenance procedures, and model update process.

We also provide a defined support window after handoff. Most issues surface in the first weeks of production use, so we stay available during that period.

Tech stack#

We are not tied to a single toolchain. The stack below represents our current standard deployment components, the tools we use because they are proven for private AI infrastructure:

Inference layer#

Interface layer#

RAG and retrieval#

Orchestration#

Security#

Every deployment includes network segmentation, encrypted storage volumes, role-based access control, and centralized audit logging. Security is not a post-deployment addition.

Industry deployments#

Healthcare: HIPAA-compliant AI for clinical notes, prior auth, and billing workflows#

The highest-value AI use cases in healthcare, clinical documentation, prior authorization, billing review, all involve PHI. That data cannot leave your network. A BAA with a cloud vendor does not change the underlying data flow. A self hosted deployment eliminates the data flow entirely.

We build HIPAA-compliant deployments that process PHI inside your environment. Clinical note drafting, prior authorization letter generation, billing code review, and clinical guideline question-answering, all running on your infrastructure, auditable, and defensible under your compliance program.

For a detailed breakdown of how we scope healthcare deployments, see the healthcare self hosted AI deployment guide.

Legal: document review and contract analysis without privilege exposure#

The February 2026 SDNY ruling makes the risk concrete: use a commercial cloud model for privileged document work, and you may have destroyed privilege on those documents. The only architectural response is to ensure the AI never touches a third-party system.

We deploy private LLM infrastructure for law firms and legal departments where document review, contract analysis, and internal research require that data stays entirely within the privileged relationship. No external processing. No third-party terms of service. No retention policies outside your control.

For a breakdown of legal industry deployment specifics, see the legal self hosted AI deployment guide.

Financial services: compliant AI under SEC Regulation S-P and FINRA Rule 3110#

FINRA's 2025 Regulatory Oversight Report named customer information protection as a primary AI risk area. SEC Regulation S-P governs how financial services firms handle nonpublic customer data. Running that data through a commercial cloud model creates compliance exposure that a vendor policy document cannot resolve.

Self hosted infrastructure gives financial services firms AI capabilities with a defensible compliance posture: data stays inside your environment, access is logged and auditable, and the system is documented to satisfy examination requests.

For a detailed breakdown of financial services deployments, see the financial services self hosted AI deployment guide.

Self hosted vs cloud AI: the real tradeoffs#

We are not trying to sell you self hosted infrastructure if cloud AI is genuinely the right choice for your workload. Some situations call for it, some do not. Here is an honest breakdown.

When self hosted costs less (and when it doesn't)#

Cloud AI APIs charge per token. At low volumes, the economics strongly favor cloud: no hardware, no ops, no maintenance. For exploratory use, prototyping, or workloads under 10-20 million tokens per month, a cloud API will almost always be cheaper on a total cost basis.

At high volumes, the math reverses. IDC's 2025 data shows organizations processing 100 million or more tokens per month can save $5 million to $50 million annually by moving high-volume regulated workloads to self hosted infrastructure. The break-even point depends on your specific model, hardware costs, and ops overhead, but for any organization with continuous, high-volume AI workflows, it is worth calculating.

For a detailed analysis, see our self hosted AI vs cloud AI comparison.

When the compliance requirement makes the decision for you#

If your data is governed by HIPAA, is subject to attorney-client privilege, or is nonpublic customer financial information under Reg S-P, the question may not be economic at all. The architecture is determined by the regulatory requirement. A cloud API with a BAA is not the same as data that never leaves your perimeter, and for some workloads, only the latter is compliant.

The SDNY ruling is instructive here. The court did not evaluate the security posture of the AI provider. It evaluated whether the communication was private by construction. A cloud API is not private by construction. A self hosted deployment is.

What ongoing ops actually look like after deployment#

Self hosted infrastructure requires real maintenance, and it helps to go in with clear expectations. Models need updating as better versions are released. Hardware needs monitoring. Security patches need applying. This operational overhead is genuine and cloud APIs do eliminate it.

We build deployments designed to be maintained by a single technically capable person, a senior IT administrator, DevOps engineer, or internal developer, without specialized AI infrastructure knowledge. We document the operations runbook, define the update cadence, and stay available for support. It is manageable, but only if the initial deployment is done properly.

Read the self hosted AI setup guide for a practical view of what ongoing maintenance involves.

Pricing#

Self hosted AI infrastructure is scoped per engagement. The variables that drive cost are:

• Number of users and concurrent load requirements
• Volume and complexity of documents for RAG ingestion
• Number of workflow automations to build
• Whether hardware procurement is in scope
• Regulatory requirements and compliance documentation needed

Infrastructure assessment and architecture scoping: Fixed fee. This is the starting point for every engagement, a defined document that establishes the architecture, compliance posture, and deployment plan before any build work begins.

Deployment and integration: Project-based, scoped from the architecture document. Most full-stack deployments run 6 to 14 weeks depending on integration complexity and organizational readiness.

Support and maintenance retainers: Available after deployment for teams that want ongoing coverage for model updates, system health, and operational changes.

We do not publish a pricing table because scope varies significantly by organization. The infrastructure audit is the right first step: a bounded engagement with a fixed fee that produces a written architecture document you own regardless of what you decide next.

FAQ#

What is self hosted AI infrastructure and when do you need it?

Self hosted AI infrastructure is a private LLM deployment running entirely within your own environment, on your hardware, inside your network, with no external API calls. You need it when your data is subject to regulatory requirements that prohibit or constrain third-party processing (HIPAA, attorney-client privilege, SEC Reg S-P), when your token volumes make cloud API costs unsustainable, or when your organization requires full data sovereignty for legal, contractual, or security reasons.

Is self hosted AI HIPAA compliant?

A correctly architected self hosted deployment satisfies HIPAA's requirements for PHI handling by design. Because PHI never leaves your controlled environment, there is no third-party data transmission to govern, no BAA dependency, and no external retention policy to manage. The architecture is the compliance mechanism. That said, HIPAA compliance requires more than the right infrastructure. You also need appropriate access controls, audit logging, and administrative safeguards, all of which we build into every healthcare deployment.

What tools are used to deploy a self hosted LLM?

Our standard stack uses Ollama or vLLM for inference (depending on workload), Open WebUI for the user interface, LangChain with ChromaDB or pgvector for RAG pipelines, and n8n (self hosted) or custom Python agents for workflow automation. All components are open source and run entirely within your environment.

How much does it cost to deploy a private on-premise LLM?

Hardware costs vary significantly based on the models you need to run and the concurrent load you need to support. A team deployment running a mid-size model (7B-13B parameters) on a single GPU server can be provisioned for $10,000-$25,000 in hardware. Production deployments requiring higher throughput or larger models scale from there. Software and deployment costs are scoped per engagement; the infrastructure assessment is the right first step for getting a real number.

When does self hosted AI become cheaper than cloud AI APIs?

The break-even point depends on your token volume, the specific model, and your hardware and ops costs. As a general reference point, IDC's 2025 data shows organizations processing 100 million or more tokens per month can save $5 million to $50 million annually by moving to self hosted infrastructure. For most teams processing fewer than 10-20 million tokens per month, cloud APIs remain cheaper on a total cost basis.

What open-weight models can you deploy?

We deploy any open-weight model that runs on Ollama or vLLM, including the Llama family, Mistral, Qwen, Phi, Gemma, DeepSeek, and others. Model selection is part of the engagement scoping process. We evaluate your use case, context window requirements, and performance needs against the current model options and make a specific recommendation.

Do you handle the hardware procurement?

We can. For clients without an existing GPU procurement process, we work with hardware vendors to specify and source the right configuration. For clients with existing IT procurement relationships, we provide detailed hardware specifications and advise on configuration. Either way, hardware readiness is confirmed before deployment begins.

If your workload involves regulated data and you need to understand what a compliant private deployment looks like for your organization, the infrastructure audit is the right starting point.

Request an infrastructure audit

The audit is a bounded engagement: a defined scope, a fixed fee, and a written output, an architecture document you own regardless of what you decide to build next.