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Private and on-premise AI infrastructure for regulated industries

Self-hosted LLM deployments for clients with regulatory, security, or data-control requirements. EU data residency. VPC-isolated. On-prem on your own GPU servers.

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Overview

How we approach this work.

Private AI infrastructure services from Resser cover the full path from procurement of GPU servers to a production-grade LLM stack running entirely inside your environment.

We deploy open-weights models (Llama, Mistral, Qwen, DeepSeek) on vLLM or TensorRT-LLM, behind your auth, inside your VPC or on your own bare metal. We bring the production patterns the closed-source providers have already proven: eval coverage, retry logic, observability, cost telemetry, prompt versioning.

Use cases: regulated industries (healthcare, finance, defense, government), EU data residency under GDPR, IP-sensitive workloads where no prompt or response can leave the perimeter, customers who have already done the procurement and need engineers who can operate the cluster.

What we build

Concrete deliverables.

On-prem LLM stacks

Llama 3 70B, Mistral, Qwen 2.5, DeepSeek on vLLM or TensorRT-LLM. GPU clusters on your bare metal or your private cloud.

VPC-isolated AI deployments

AWS Bedrock in your VPC, Azure OpenAI in your tenant, GCP Vertex in your project. Network egress fully controlled.

EU and Swiss data residency setups

Frankfurt, Dublin, Paris, Zurich, Stockholm. GDPR-compliant from day one. DPA, SCC, sub-processor list, audit log ready.

Air-gapped AI for defense and government

Fully offline deployments. Model weights, eval data, observability, admin tooling, all inside the perimeter. No outbound network calls.

Private RAG over sensitive data

Document stores, knowledge bases, customer data indexed and served entirely from your infrastructure. No data ever sent to a cloud LLM provider.

GPU operations and capacity planning

H100, H200, A100, L40S, AMD MI300X. Quantization, batching, KV cache, speculative decoding. We size the cluster to your throughput target.

Stack

What we build with.

Inference servers

vLLM, TensorRT-LLM, SGLang, llama.cpp, MLC-LLM. Triton Inference Server for multi-model. Ray Serve for multi-tenant.

Open-weights models

Llama 3 (8B, 70B, 405B), Mistral, Mixtral, Qwen 2.5, DeepSeek, Phi, Gemma. Quantized GGUF, AWQ, GPTQ when memory is the constraint.

GPU hardware

NVIDIA H100, H200, A100, L40S. AMD MI300X. On-prem clusters with NVLink. AWS p5 / p4, Lambda Labs, CoreWeave.

Eval, observability, security

Self-hosted LangFuse for traces. Self-hosted Promptfoo for eval. Prometheus + Grafana. HashiCorp Vault for secrets. RBAC + audit log in Postgres.

Outcomes

What we ship.

Fintech compliance: Llama 3 70B on customer-owned GPUs with no cloud egress; reviewer time-per-case sharply reduced.
Healthcare provider EU deployment: full on-prem RAG over patient documents, GDPR-compliant, sub-second retrieval.
Government deployment: air-gapped LLM with internal eval suite, no outbound network calls.

References with names available after a scoping call.

In depth

Four deployment boundaries, from fastest to strictest

Private AI is not one thing. It is a choice about where your data is allowed to be processed, and the cost climbs as the boundary tightens. Most teams pick the loosest option their obligations permit.

Provider API with a data processing agreement and zero retention. Fast, cheap, fine for most commercial content. Your data still transits a third party under contract.
EU region endpoints on Azure OpenAI, AWS Bedrock or Vertex AI. Requests stay on European infrastructure under an EU entity. Usually enough to satisfy a GDPR review.
Managed inference inside your own VPC. The model runs on infrastructure you control, reachable only from your network, with no public endpoint at all.
On premise on your own GPU hardware. Nothing leaves the building. The only option that survives a rule saying data must never touch external infrastructure, and the most expensive to run.

GDPR, the EU AI Act, and what auditors actually ask

The questions that come up in review are narrower than the regulation is, and they are answerable in a sentence each if the build anticipated them.

Where is the data processed and by whom. Is there a processor agreement, and does it permit training on your content. Can you show which prompts and responses were logged, for how long, and who could read them. If the system makes or supports a decision about a person, can you explain how, and can a human overrule it.

Self-hosting answers most of those by construction, because there is no processor and no external log. That is the real argument for it, more than any performance claim. Under the EU AI Act the obligations follow the use case rather than the hosting choice, so a high risk application still needs documentation and human oversight even when the model runs on your own hardware. We build that record as we go rather than reconstructing it afterwards.

When self-hosting is the wrong answer

We talk people out of this more often than into it, because the running cost is real and permanent.

Volume is low. A GPU sitting idle still costs money. Below a certain steady throughput, a provider API under a processing agreement is cheaper by a wide margin and always will be.
Nobody will own the cluster. Self-hosting means someone patches it, watches it and gets paged when it stops. Without that person, you have bought an outage waiting for a deadline.
The requirement is reputational rather than regulatory. If the concern is how it looks rather than what a rule says, an EU region endpoint usually satisfies it at a fraction of the cost.
You need frontier capability. The best open weights models are strong and improving, but if your task genuinely needs the top closed model, self-hosting trades accuracy for control. Sometimes that trade is right. It should be made deliberately.

Related services

Other places this work shows up.

AI integration into existing systems Custom AI software development RAG implementation services

FAQ

Frequently asked.

Why deploy AI on-prem instead of using OpenAI or Anthropic?

Three reasons: data residency / regulatory requirements, IP sensitivity, and predictable unit cost. If your data cannot leave your perimeter or your inference volume is high enough that token pricing exceeds amortized GPU cost, on-prem is the right call. We help you decide in discovery week.

Which open-weights model should we use?

Depends on the task and the hardware. Llama 3 70B for general reasoning. Qwen 2.5 for code and structured outputs. Mistral / Mixtral for cost-efficiency. DeepSeek for math-heavy. We benchmark on your data before locking the choice.

What GPUs do we need?

For a single Llama 3 70B inference instance, an 8× H100 or 8× A100 80GB cluster works well. Quantized variants run on smaller hardware. We do the capacity planning in discovery week and recommend hardware to match your throughput target.

Can you deploy entirely air-gapped?

Yes. We have shipped fully air-gapped deployments for defense and government. Model weights, eval data, observability, admin tooling all inside the perimeter. No outbound network calls. License terms reviewed for each model.

How much does a private AI infrastructure build cost?

Software build (LLM stack, RAG, eval, observability): from €100,000. A narrower on-prem RAG system over one corpus can start at €60,000. Hardware (GPUs) typically procured by the customer; we help with sizing. Annual operations and updates: priced as a monthly retainer.

Want to scope this for your project?

Fill the project-estimate form. We reply within one business day with a preliminary scope and a rough budget bracket.

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