AI on your terms. Your hardware. Your control.

Data Sovereignty

Sensitive data never leaves your network. No third-party API calls.

Cost at Scale

Well-designed self-hosted stacks deliver 60 to 80% cost reduction after 12 months vs public cloud AI.

Performance Control

Dedicated hardware means predictable latency. No rate limits or queue times.

Model Ownership

Your fine-tuned model weights are IP. You own them, version them, and control access.

Hardware

GPU selection (H100, A100, RTX 4090, MI300X), NAS/RAID storage, InfiniBand/100GbE networking.

Kubernetes

Auto-scaling inference endpoints, rolling updates, resource quotas, health checks.

Model Serving

vLLM, Triton Inference Server, Ollama, custom FastAPI/gRPC endpoints.

MLOps

MLflow/W&B experiment tracking, CI/CD for models, DVC for data versioning, drift detection.

Security

RBAC, TLS 1.3 + AES-256, audit logging, HIPAA/GDPR/SOC 2-aligned patterns.

On-Premise

Full hardware ownership. Air-gapped. Maximum control.

Private Cloud (AWS/GCP/Azure)

Isolated within your cloud account.

Hybrid

Training on private hardware, cloud burst for inference.

Infrastructure Discovery

Review environment, workloads, compliance, budget → architecture recommendation.

Environment Setup

Deploy Kubernetes cluster, GPU nodes, storage, networking, model serving.

Model Deployment

Deploy models, inference endpoints, MLOps pipelines, monitoring.

Handoff

Full runbook, architecture diagrams, operational playbooks, team training.