AI Infrastructure Abstraction · Multi-OEM · Self-hosted

One platform.
Any application.
Any infrastructure.

VibOps is the infrastructure abstraction layer that deploys and operates any AI/ML application — regardless of hardware, cloud, or edge site. Custom model, Slurm workload, or third-party AI platform: VibOps handles the infrastructure underneath, in plain English, across your entire fleet.

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NVIDIA H100 · H200 · Blackwell
·
AMD MI300X
·
Intel Gaudi 3
·
AWS Trainium
·
Google TPU
·
Groq LPU
🌐 Any application — any hardware, any site
🔒 Fully self-hosted — your data stays in your infra
📋 Immutable audit trail — every action logged
🏢 Multi-tenant — datacenter, CSP, enterprise-ready
🌍 Air-gap ready — on-prem LLM supported
🔓 MIT License  ·  vibops-mcp on GitHub ↗
✅ ~3,843 automated tests
🔐 pip-audit + Trivy CVE scanning on every commit
📦 Weekly releases

See it in action

From conversation to production in minutes

Live cluster inspection, model deployment, pipeline promotion — every step audited across any application and infrastructure.

The platform

Everything your team needs to operate a GPU workload

Fleet view, agent console, FinOps, multi-tenant isolation, pipelines, and audit — in one place.

VibOps console showing GPU fleet status, agent conversation, and pipeline promotion

The problem

AI infrastructure runs on a patchwork of incompatible tools

Every accelerator vendor ships its own CLI, its own dashboard, its own metrics stack. Every application — custom model, third-party AI platform, Slurm workload — adds another layer. Managing a heterogeneous fleet means maintaining parallel workflows, parallel runbooks, and parallel skill sets.

NVIDIA

kubectl + DCGM + MIG scripts

Custom bash scripts for partitioning, DCGM exporter for metrics, manual MIG profiles per node. Works — but only for NVIDIA engineers.

AMD

ROCm stack + custom exporters

Different driver model, different metric names, different partitioning (SPX/DPX/QPX). Separate documentation, separate on-call runbook.

Intel / AWS / Google

Vendor SDKs, each its own API

Gaudi SynapseAI, Neuron SDK for Trainium, TPU topology flags — each accelerator speaks a different language to the control plane.

FinOps

Spreadsheets + manual export

No unified cost model across vendors. Chargeback is a monthly copy-paste exercise. Waste is invisible until the invoice arrives.

One interface, one vocabulary, one audit trail
across every GPU vendor, every cluster, every customer.


The solution

One abstraction layer for any AI application, any infrastructure

VibOps sits between your operators and your infrastructure. Deploy a custom model, a third-party AI platform, or a Slurm workload — the same interface works identically across NVIDIA, AMD, Intel, cloud, and edge. One conversation replaces an entire toolchain.

🌐

Infrastructure abstraction layer

Deploy any AI application — custom model, third-party platform, Slurm workload — on any hardware. The same interface that manages NVIDIA H100s works on AMD MI300X, Intel Gaudi, and edge clusters without rewriting a script.

🤖

Natural language operations

Operators describe what they want in plain language. VibOps translates every intent into the right kubectl, Helm, or vendor SDK call — with confirmation gates before anything destructive runs.

💸

FinOps across all vendors

Unified cost model with per-vendor pricing, idle GPU detection, budget alerts, and per-tenant chargeback reports — automatically, from the same platform that runs operations.

🏢

Multi-tenant by default

Full tenant isolation at the row level. One VibOps instance serves multiple customers without data leakage. Per-customer FinOps, quotas, rate limiting, and audit trails out of the box.

⚙️

Automation pipelines

Multi-step workflows with rollback guards — triggered by events, schedules, or alerts. Staging → health check → production → verify: orchestrated in a conversation, audited at every step.

🔌

Connect Gateway

Lightweight agent deployed at each site. Supports air-gapped clusters, remote datacenters, and client-managed infrastructure — all visible from one control plane.


Why it's safe to run critical workloads on VibOps

Running GPU workloads in production means every mistake is expensive. VibOps is designed with five independent safety layers so that no single misconfiguration or misrouted command can damage your fleet.

🛡

Confirmation before destruction

Every destructive action — scale-down, delete, partition — requires explicit operator confirmation. The platform shows a dry-run preview first. You can't break production by accident.

📋

Immutable audit trail

Every operation is logged: who ran it, when, what parameters, what the outcome was. Tamper-proof. SOC 2 ready. Exportable for compliance review.

🔐

Policy engine — default deny

Every action must be declared in the tool catalog before it can execute. Unknown operations return a 403. A new vendor connector adds zero attack surface unless explicitly registered.

🏠

Sovereign — data never leaves your perimeter

Deployed inside your own infrastructure. Cluster state, credentials, and operator conversations stay in your network. On-prem LLM supported for fully air-gapped operation.

🔒

Tenant isolation

Every read and write is scoped to the authenticated organization — enforced at the service layer, not just at the API boundary. Cross-tenant data access is structurally impossible.

🧪

~3,843 automated tests + CVE scanning

Connector tests, behavioral tests, security tests. pip-audit and Trivy scan every dependency on every commit — HIGH and CRITICAL CVEs block the build before they reach production.

Multi-OEM by design

The only orchestration platform that speaks natively to every accelerator

Most GPU operations tools are NVIDIA-centric. VibOps was built from the ground up to support the full accelerator landscape — each connector implements the same vendor-agnostic interface, so the same workflow that works on H100s works on MI300X, Gaudi 3, and Trainium.

NVIDIA
H100 · H200 · Blackwell
NIM · DGX · MIG · DCGM
AMD
MI300X · MI325X
ROCm · SPX/DPX/QPX
Intel
Gaudi 3
SynapseAI · GAUDI_* metrics
AWS
Trainium · Inferentia
Neuron SDK · Trn1/Trn2
Google
TPU v5e · v5p · v6e
Topology-aware · GKE
Groq
LPU · GroqCloud
Per-token cost model
No vendor lock-in. Sovereign-friendly by construction.
European datacenter operators, national compute programs, and sovereign cloud providers can run heterogeneous GPU fleets — NVIDIA, AMD, and Intel in the same rack — without a vendor-specific operations stack. VibOps manages all of them from the same console, the same agent, the same audit trail.

Who it's for

Built for three segments that own GPU infrastructure

VibOps deploys inside your infrastructure — one instance per site, fully under your control. Not a SaaS platform you connect to.

🏭 GPU Datacenter Operators

HPC centers, national compute programs, colocation providers, sovereign GPU datacenters

You own the hardware. VibOps gives you the operations layer to turn raw multi-OEM compute into a managed AI infrastructure service — without building an MLOps platform from scratch.

  • Unified operations across heterogeneous fleets — NVIDIA, AMD, and Intel in the same datacenter
  • Tenant isolation and per-customer FinOps out of the box
  • Offer clients a managed GPU service with full auditability, not just raw access
  • On-prem deployment — no data leaving your facility
  • Air-gap compatible for classified or sovereign environments

🏢 Cloud Service Providers

Outscale, OVH, Scaleway, CoreWeave, regional GPU clouds — any CSP reselling GPU capacity

Transform raw GPU rental into a managed AI platform — differentiated product, higher margin, deep switching cost. Deploy one VibOps instance per client in minutes.

  • Each GPU cluster under VibOps management becomes a billable managed service
  • Clients operationally embedded in your ecosystem don't churn
  • Your AI platform offering ships in days, not quarters
White-label ready

Your brand. Your console. Your pricing. Configure markup per accelerator type, per customer segment, or per workload. Built-in chargeback reports per customer. Data isolation between you, your customers, and your engineering team.

🏦 Large Enterprises

Banks, pharma, research labs, defence, public sector — any organisation managing their own GPU fleet internally

Your MLOps and SRE teams operate GPU clusters without requiring deep Kubernetes expertise at every level. Onboard faster. Operate consistently across on-prem, cloud, and hybrid.

  • New team members are productive from day one — no six-month onboarding
  • Audit trail for SOC 2, internal governance, and data residency requirements
  • Consistent operations across on-prem, cloud, and hybrid clusters
  • Multi-vendor fleet managed from a single interface — no NVIDIA lock-in required

How it works

Up and running in minutes

VibOps deploys as a lightweight control plane next to your existing infrastructure. No agents required on GPU nodes. No data exfiltration.

🚀 01

Deploy VibOps

Self-hosted via Helm or Docker Compose. Deploys inside your infrastructure — on-prem, colocation, cloud, or hybrid. Your perimeter, your control.

🔌 02

Connect your clusters

Install a lightweight Connect Gateway on each cluster or site. Auto-discovers namespaces, deployments, and GPU resources across all vendor stacks.

🤖 03

Operate in natural language

Use the VibOps console or connect Claude Desktop via pip install vibops-mcp. Your operators describe what they need — VibOps handles the vendor-specific execution.

📋 04

Full audit trail

Every operation logged with user, timestamp, vendor, cluster, and exact command executed. Immutable. Compliance-ready from day one.


Early access

Built for teams that take
GPU infrastructure seriously

We're onboarding a limited number of GPU datacenter operators, Cloud Service Providers, and large enterprise teams. Request access and we'll reach out personally.

No spam. We'll reach out personally.

You're on the list. We'll be in touch shortly.