Cloud-Grade Performance for Your Own Hardware
We bring cloud-like automation to private data centers with GPU clusters, Kubernetes on bare metal, VMware, Proxmox, and hybrid setups. For organizations where data must stay on-premises.
Infrastructure We Build & Manage
GPU Cluster Management
NVIDIA GPU provisioning, VMware Bitfusion, Proxmox GPU passthrough. Purpose-built for AI training and inference workloads.
On-Prem Kubernetes
Production-grade K8s on bare metal with automated provisioning, autoscaling, failover, and disaster recovery.
Hybrid Cloud
Smooth workload orchestration between on-prem and cloud. Use on-prem for sensitive data, burst to cloud for peak demand.
PDPL & GDPR Compliance
Infrastructure where data never leaves your premises or jurisdiction. Built for PDPL, HIPAA, and GDPR requirements.
Automated Provisioning
IaC with Terraform and Ansible. Spin up new environments in minutes, not weeks. Version-controlled infrastructure.
24/7 Monitoring
Prometheus, Grafana, and custom alerting. Hardware health, capacity planning, and proactive incident response.
Data Sovereignty for Regulated Industries
For organizations where data must stay on-premises due to regulatory requirements (PDPL, HIPAA, GDPR), we design and operate private cloud infrastructure that delivers cloud-like performance without cloud dependencies.
We've deployed AI platforms on-prem for clients who needed full data sovereignty: managing hardware resources, spinning up Kubernetes clusters, and provisioning GPU compute via AI agents. All within the client's own facility.
Sovereign Multi-Cloud AI Compute Platform with GPU Pooling
Rebuilt the platform infrastructure for a UK sovereign AI cloud that orchestrates GPU compute across 15+ providers (AWS, Azure, GCP, Civo, Runpod, Hetzner) and 700 data centers with a single control plane. Backed by SFC Capital, Rule30.vc, and NVIDIA Inception. CNCF and Linux Foundation member.
FAQ
InfraOps Questions, Answered
What does InfraOps mean here?
InfraOps is on-premises and hybrid infrastructure work: bare-metal Kubernetes, GPU clusters, VMware and Proxmox virtualization, and data-sovereign deployments where the cloud is not an option. Common for clients in healthcare, KSA, MENA, and any regulated industry where data cannot leave the facility.
Do you do on-prem Kubernetes?
Yes. We deploy production-grade Kubernetes on bare metal using kubeadm, RKE2, or Talos depending on use case. We pair it with MetalLB or kube-vip for load balancing, Rook-Ceph for storage, and ArgoCD for GitOps. PDPL, GDPR, and HIPAA-compliant setups are a specialty.
Can you manage GPU clusters on premises?
Yes. NVIDIA GPU Operator on Kubernetes with MIG partitioning. VMware Bitfusion for shared GPU pools. Proxmox GPU passthrough for VM-based teams. We have set up everything from a single 8-GPU training node to multi-rack inference clusters with topology-aware scheduling.
What about hybrid (on-prem plus cloud)?
Common pattern. On-prem for sensitive data and steady-state workloads, cloud (AWS, Azure, or GCP) for burst capacity, AI training spikes, or geographic edge serving. We connect them with site-to-site VPN or Direct Connect / ExpressRoute, and use Karpenter or KEDA to drive burst behavior.
Do you handle data sovereignty requirements?
Yes. KSA PDPL, EU GDPR, US HIPAA. We deploy AI workloads where the data never leaves the client facility, including AI agents that provision GPU compute on demand within the on-prem cluster. Our AOT (Saudi Arabia) and Stack8s (UK) engagements include data sovereignty design.
How much does InfraOps cost?
On-prem and hybrid InfraOps work is delivered through our two engagement patterns: Managed Engineering Pod from $10,000 per month (full team for greenfield cluster setup and multi-environment rollouts) or Embedded Senior DevOps from $2,500 per month (senior engineer for steady monitoring, patching, capacity planning, and incident response). Free infrastructure assessment included before any retainer.
Keep going
Related InfraOps work and writing
On-Prem GPU Kubernetes: 5 to 100 Node Reference Architecture
As a research lab head or an AI-native founder, you need GPU compute your team can actually use in minutes, without shipping data to a public cloud. This is the exact reference architecture we deploy: 5 nodes on day one, scales to 100+, NVIDIA GPU Operator, KServe, Ray, and MIG partitioning included.
Read case study Case studyProduction Kubernetes for IoT: LoRaWAN Things Stack On-Prem
A healthcare analytics platform needed production-grade LoRaWAN infrastructure for IoT gateways and applications. Eprecisio deployed and configured a self-hosted Kubernetes-hosted Things Stack with plug-and-play gateway onboarding.
Read case study InfraOpsSovereign AI Cloud: Running GPU Workloads Under GDPR, PDPL, and the EU AI Act
Under oath before the French Senate in 2025, Microsoft France's Legal Director admitted he could not guarantee EU customer data would never be handed to US authorities. That single sentence is the sovereign-cloud thesis. Here is what actually meets EU AI Act, GDPR, and PDPL for GPU workloads in 2026, with real vendor pricing.
Read post MLOpsvLLM Autoscaling On Kubernetes: The Metric CPU-Based HPA Cannot See
On a Google Cloud G2 instance with an NVIDIA L4, the vLLM waiting queue went from 0 to 64 requests while host CPU stayed at 2.8% median and peaked at 22.2%. A CPU-based Kubernetes HPA would never have fired. The metric to alarm on is vllm:num_requests_waiting, plus a sharper by-reason variant most people miss. First-party evidence from the Eprecisio hardware baseline pilot.
Read postOwn your infrastructure without owning the headache
On-prem Kubernetes, GPU clusters, and data-sovereign deployments for regulated industries. Free infrastructure assessment before any retainer.
Pattern A
Managed Engineering Pod
Full delivery team from $10,000 per month
Pattern B
Embedded Senior DevOps
Senior engineer from $2,500 per month
See full pricing patterns.