Deploy frontier-grade AI models entirely on your servers — air-gapped, GDPR-compliant, no API bills. We design, deploy, and harden on-premise AI infrastructure for regulated industries that cannot use cloud APIs.
GDPR Article 46 and EU AI Act obligations prohibit sending personal data to non-EEA third parties
Air-gapped environments (defense, critical infrastructure) have no connectivity to external APIs
API cost unpredictability: a usage spike becomes a six-figure invoice overnight
Vendor lock-in: your AI capability depends entirely on a vendor's pricing and availability decisions
Audit requirements: regulated industries need full logs of every model input and output — cloud APIs don't provide this
Six stages from infrastructure audit to production-hardened sovereign AI deployment.
Inventory GPU/CPU resources, network topology, storage, and security requirements. Define the capability ceiling your hardware supports.
Match your use case requirements to available hardware. Balance capability, latency, and throughput — not all use cases need 70B models.
Deploy Ollama for simplicity, vLLM for high throughput, or TGI for Hugging Face ecosystem integration — based on your specific requirements.
Expose OpenAI-compatible REST APIs so existing tools (LangChain, LlamaIndex, OpenAI SDK) work with zero code changes — drop-in replacement.
Network isolation, mTLS, access controls, prompt injection mitigations, audit logging to SIEM, and regular model update procedures.
Prometheus/Grafana dashboards for latency, throughput, and error rates. Runbooks for model updates and capacity scaling.
Our on-premise deployments follow a layered architecture: hardware → inference runtime → API gateway → security layer → application integration. Each layer is independently replaceable and auditable.
You operate in banking, healthcare, defense, or EU public sector where data residency is non-negotiable. You have air-gapped environments. Your cloud AI costs exceed €10K/month and are growing. Or you've been told by legal that cloud AI use cases require DPA amendments you can't get approved.
Minimum: a workstation with an NVIDIA RTX 3090 (24GB VRAM) runs 7B models at 30 tokens/second — sufficient for 10–20 concurrent users. Production: 2–4× A100 80GB or H100 handles 70B models with high throughput. We provide a detailed hardware sizing guide based on your concurrency requirements.
Yes. CPU-only inference with llama.cpp or Ollama works well for 7B models at 3–8 tokens/second. It's adequate for async use cases (document processing, batch analysis) but not real-time chat. AMD ROCm provides GPU acceleration on AMD cards.
We set up a model update pipeline with approval gates — new model versions are staged, benchmarked against your custom evals, then promoted to production via the same runbook as the initial deployment. Zero-downtime model swaps with vLLM.
Yes by design. No data leaves your infrastructure — there are no external API calls once deployed. We document the data flows for your DPO and provide the processing records required under Article 30.
In most cases, yes. We deploy OpenAI-compatible endpoints — the same base URL pattern, same request/response format. You change one line of configuration (the base URL), and your existing LangChain, LlamaIndex, or direct API code works without modification.
For many enterprise use cases, yes. Llama 3.3 70B matches or exceeds GPT-4 on instruction following, coding, and reasoning benchmarks. For your specific use case, we always run a benchmark comparison before recommending a base model.
Let's discuss how this service can address your specific challenges and drive real results.