Private LLM · Buenos Aires, Argentina · US Eastern hours · 15-minute incident response

Run your LLMs
in your own cloud

Production-ready in 4–6 weeks.

For teams that can't ship data to OpenAI. We deploy open-weight models inside your cloud and run them like production software: self-hosted inference (vLLM), GPU cost optimization, monitoring, rollbacks. Your data never leaves your VPC.

Book the free GPU teardown →See the three steps// reply within the hour

§01 / What we fix

01

Your AI cloud bill doubled in six months

GPUs sit idle most of the time, nobody knows which workloads burn money, and finance keeps asking questions

02

Compliance blocks you from OpenAI

Customers, auditors or regulators won't allow production data in third-party AI APIs, so AI features stay stuck in backlog

03

Self-hosted
LLM inference

vLLM and TGI on your Kubernetes: Llama, Qwen, Mistral, DeepSeek served inside your VPC with autoscaling and full observability.

04

GPU orchestration
& cost

Karpenter, KEDA, spot strategies, right-sizing and scale-to-zero for GPU workloads. We find where your GPU budget leaks and close it.

§02 / Start here — free

Free 30-minute
GPU spend teardown

Before anyone pays for anything: bring your current GPU or AI-cloud bill to a 30-minute call, and we'll point at two or three places the money is leaking. You keep the list either way.

What we look at

  • Where your GPU hours actually go, and how much of that is idle capacity
  • Whether instance types, regions and scaling match the workload
  • Which parts of the bill self-hosting would move — and which it wouldn't

What it costs

Nothing, and there is no proposal deck at the end of it. If your setup is already in reasonable shape, we'll tell you that — it makes for a shorter call and a straight answer.

Book the free teardown →

§03 / Three steps

Private AI infrastructure for teams that can't ship data to OpenAI

For FinTech, healthcare, legal and any product where data residency matters. We deploy open-weight models inside your cloud and run them like production software: SLA, monitoring, rollbacks, cost control.

GPU Cost Audit
$5,500 one-time
// 1–2 weeks
Where your GPU money goes, what to fix first, and whether self-hosting makes sense for your workload at all. Honest math, no hype. Fee counts toward the deployment.
Book the audit →
CORE OFFERPrivate LLM Deployment
$18,000–45,000 one-time
// 4–6 weeks, fixed scope
vLLM or TGI on your Kubernetes: model serving, autoscaling, RBAC, audit logs, network isolation, dashboards, runbooks and full handover documentation.
Scope a deployment →
AI Infra Retainer
$4,000–8,000 /mo
// ongoing, cancel with 15 days' notice
Model updates, incident response with SLA, capacity planning and continuous GPU cost optimization.
Talk retainer →

// Guarantee: if the audit doesn't find at least the audit fee in annual savings — it's free.

What stays in your cloud

Model weights, prompts, logs, embeddings — everything.

What we don't do

Model training, fine-tuning, data science. We run AI infrastructure, we don't build models.

Book an audit call →

How it went for a legal AI assistant

50,000+ legal documents, answers 25× faster (28s → 1.1s), inference cost down 62%.

Read the case →

Running an AI app on hosted APIs?

If the models are fine where they are and it's production that needs looking after, that's our main offer: fixed-price production for AI apps.

Production for AI apps →