//GPU capacity, optimized in real time

Stop burning your GPU budget on idle capacity

Guaranteed GPU VMs are expensive; preemptible ones vanish without warning. Langotime uses live telemetry to decide when cheap preemptible accelerators are enough and when to pay for guaranteed capacity — under your own cost and SLO constraints.

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The problem

GPU capacity is the most expensive thing you can leave idle

GPU VMs cost far more than ordinary CPU capacity, so idle or misallocated accelerators burn budget fast. Cheap preemptible instances can disappear without warning; guaranteed ones are pricey enough that overprovisioning hurts. Static provisioning leaves you choosing between wasted spend and outage risk. Langotime works at the cloud-capacity layer — choosing and swapping VMs under your constraints — not physical hardware or data-center procurement.

//How Langotime does it

From accelerator telemetry to a capacity decision

Connect

Pull live GPU utilization, queue depth and workload telemetry into one operational picture.

Explain

Learn your real demand and how preemptible availability behaves over time.

Simulate

Project preemptible-to-guaranteed swaps against cost, availability risk and your SLOs.

Act

Recommend the capacity move under your cost function, with a human in the approval loop.

//Honest comparison

“Why not just…”

“KEEP IT ALWAYS ON”Idle accelerators are the most expensive idle you can buy.

“USE SPOT EVERYWHERE”Random evictions turn into user-visible outages without a plan to fall back.

“WATCH THE COST DASHBOARD”It's post-hoc. It can't model the consequence of the next capacity decision.

Why it's different

Built for the cost-versus-availability tradeoff

Langotime runs on a Time Series World Model — AI for metrics, not text. It models the one tradeoff that makes accelerators painful: cheap-but-fragile versus expensive-but-guaranteed, in real time.

Real-time, on live telemetry — not post-hoc reports

Uses cheap preemptible capacity — safely

Cost and availability tradeoffs made explicit

Cloud-capacity decisions only — no hardware lock-in

Works with your stack and the agents you already use

A human stays in the approval loop

//Who it's for

If idle GPUs are a line item you can see…

Inference fleets, training infrastructure, any team where accelerator spend is large enough that a 10% mistake is real money — and where preemptible capacity is on the table but feels too risky to lean on.

Langotime is for you.