VibOps orchestrates the full AI value chain — GPU infrastructure, LLM inference routing, and agent governance — through a single conversation. One platform that abstracts any hardware vendor, any LLM backend, any agent framework. Self-hosted. Multi-tenant. Open-core.
No spam. We'll reach out personally.
"Which agent costs the most?" — VibOps answers with real GPU cost data, per agent, per model, per team.
Demo: GPU cost per agent, budget enforcement, model policies — all in natural language.
You deploy 50 agents calling LLMs on your GPU fleet. The bill arrives: $47,000. GCP Billing sees VMs. LangSmith counts tokens. Neither can tell you which agent consumed how much GPU. VibOps can — because it sits between your agents and your LLMs.
Transparent LLM proxy captures every inference — tokens, GPU time, real cost. Attributed to the agent that made the call. Not tokens — GPU-hours.
Set monthly spend caps per agent. Soft alert at 80%, hard block at 100% (HTTP 429). No runaway costs, no surprises on the bill.
Control which agent can use which LLM. Glob patterns, deny-first. The pricing agent gets Llama-70B; the FAQ bot gets Mistral-7B only (HTTP 403).
Cost spikes, request surges, error rate jumps — detected per agent every 10 minutes. Auto-resolve when the condition clears.
"If I migrate Llama-70B, which agents break?" — impact analysis with call counts and cost exposure before any change.
Machine identities for every agent — create, rotate, revoke. Full lifecycle tracked in the immutable audit log.
Every inference logged with HMAC-SHA256 chain. Tamper-detectable. Exportable to any SIEM. AI Act and SOC 2 ready.
Point your agents to the VibOps proxy — one URL change, one header. No SDK, no code change. Works with n8n, LangChain, CrewAI, Dify, or any OpenAI-compatible client.
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.
Custom bash scripts for partitioning, DCGM exporter for metrics, manual MIG profiles per node. Works — but only for NVIDIA engineers.
Different driver model, different metric names, different partitioning (SPX/DPX/QPX). Separate documentation, separate on-call runbook.
Gaudi SynapseAI, Neuron SDK for Trainium, TPU topology flags — each accelerator speaks a different language to the control plane.
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.
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.
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.
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.
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.
Transparent LLM proxy tracks every inference per agent — cost, tokens, latency. Set budgets per agent, enforce model access policies, detect anomalies. Works with any framework: n8n, LangChain, CrewAI, Dify — just change the base URL.
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.
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.
Lightweight agent deployed at each site. Supports air-gapped clusters, remote datacenters, and client-managed infrastructure — all visible from one control plane.
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.
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.
Every operation is logged: who ran it, when, what parameters, what the outcome was. Tamper-proof. SOC 2 ready. Exportable for compliance review.
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.
Deployed inside your own infrastructure. Cluster state, credentials, and operator conversations stay in your network. On-prem LLM supported for fully air-gapped operation.
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.
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.
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.
VibOps deploys inside your infrastructure — one instance per site, fully under your control. Not a SaaS platform you connect to.
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.
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.
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.
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.
VibOps deploys as a lightweight control plane next to your existing infrastructure. No agents required on GPU nodes. No data exfiltration.
Self-hosted via Helm or Docker Compose. Deploys inside your infrastructure — on-prem, colocation, cloud, or hybrid. Your perimeter, your control.
Install a lightweight Connect Gateway on each cluster or site. Auto-discovers namespaces, deployments, and GPU resources across all vendor stacks.
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.
Every operation logged with user, timestamp, vendor, cluster, and exact command executed. Immutable. Compliance-ready from day one.
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.