Utho.ai gives AI teams dedicated GPUs, serverless inference and Kubernetes clusters on infrastructure we own and operate — with flat pricing, no egress fees, and capacity you can actually get.
Every AI team hits the same walls with hyperscalers and GPU marketplaces. We designed around them.
We own our hardware and publish real availability. Reserve capacity in advance, or deploy available GPUs in under a minute — no quota tickets, no sales gate.
One hourly or monthly rate per GPU. Moving your datasets, checkpoints and model weights in or out costs nothing. Your invoice matches your calculator.
Inference endpoints autoscale down to zero and back in seconds. Training VMs can be stopped with state intact — pay for storage, not idle silicon.
Pre-tuned GPU images, drivers and container runtimes out of the box. Managed Kubernetes with GPU node pools and autoscaling — your team ships models, not YAML.
OpenAI-compatible endpoints on dedicated capacity with warm pools. Consistent time-to-first-token, with per-token or dedicated-throughput billing.
Standard KVM, Kubernetes and open APIs. Your containers, weights and pipelines run anywhere — staying with us is a choice, not a trap. Data residency options included.
On-demand GPU VMs and dedicated bare metal with full passthrough performance.
OpenAI-compatible endpoints for open-weight models, autoscaling from zero.
GPU node pools, autoscaling and private networking, managed for you.
A voice agent and a document pipeline need completely different stacks. Pick your workload and see the deployment we'd recommend — then send it to us with one click.
Streaming STT + LLM + TTS where every millisecond is audible.
Interactive prototypes of the Utho.ai console, with demo data.
| Node | GPU | State |
|---|---|---|
| gpu-a-04 | PRO 6000 | running |
| gpu-a-05 | PRO 6000 | running |
| gpu-b-11 | PRO 6000 | running |
| gpu-c-02 | PRO 6000 | provisioning |
| gpu-a-06 | PRO 6000 | queued |
| Model | Replicas | State |
|---|---|---|
| llama-3.3-70b | 6 | serving |
| qwen2.5-coder | 4 | serving |
| whisper-v3 | 2 | serving |
| mixtral-8x7b | 0→2 | scaling |
| embed-large | 3 | serving |
| Service | Usage | Amount |
|---|---|---|
| GPU VMs · PRO 6000 | 8,940 hrs | $3,770 |
| Inference endpoints | 38.2B tokens | $1,310 |
| Kubernetes GPU pool | 2,300 hrs | $560 |
| Block storage | 18 TB | $180 |
Blackwell workstation-class GPUs are live today. Datacenter-class Hopper and Blackwell are next — waitlist members get priority allocation and launch pricing.
RTX PRO 6000 Blackwell capacity deploys in under a minute from the console. For H100, B200 and B300, join the waitlist — we allocate in order, and waitlist members get first access when new capacity lands.
Everything around the GPU: vCPU, system memory, NVMe storage, networking, IPs, managed Kubernetes and monitoring. There are no egress fees — moving your datasets, checkpoints and weights in or out costs nothing.
No. Run on-demand by the hour with no minimums. If you want predictable unit economics, reserved terms are available — but starting, stopping and leaving are always free of penalties.
Open-weight models like Llama, Qwen and Whisper out of the box, or bring your own fine-tuned checkpoints. Endpoints are OpenAI-compatible, so migrating is usually a one-line base URL change.
Yes, and it's free. Our infrastructure engineers help move your data, containers and pipelines, and validate performance against your current setup before you switch traffic.
Yes. Dedicated bare metal, private clusters and data residency options are available for teams with compliance or isolation requirements. Mention it in the waitlist form and we'll scope it with you.
Tell us what you want to run and we'll get back with capacity, pricing and onboarding. Waitlist members get priority allocation when new GPUs land.
Our team will reach out shortly with capacity and pricing for your selected GPUs and services.