CloudEngine is a complete cloud, not a GPU rack with an API. Compute, networking, storage, Kubernetes, inference and observability — one engine, owned end to end, with flat pricing and zero egress fees.
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.
The new wave of GPU providers hands you a VM and an SSH key — networking, storage, scaling and monitoring become your problem. CloudEngine ships the entire runtime around every workload, GPU or not — 25 production services, all live today.
Bare GPU instances with everything else missing. You end up building VPCs, storage layers, load balancing, observability and billing glue yourself — the undifferentiated work that slows serious teams down.
Compute, network, storage, orchestration and observability as one integrated platform — built and operated on our own stack for 15+ years. GPUs are one cylinder of the engine, not the whole product.
VMs and dedicated bare metal, CPU or GPU, provisioned in under a minute.
Blackwell-class GPUs with full passthrough performance, on demand or reserved.
Managed clusters with CPU and GPU node pools and event-driven autoscaling.
Serverless, OpenAI-compatible endpoints that scale from zero to production.
VPCs, load balancers, firewalls and private interconnect — isolated by default.
NVMe block, object storage and snapshots, replicated within the region.
Metrics, logs and alerts built into every resource — no third-party stack needed.
Full REST API and Terraform-ready provisioning with per-team access control.
On-demand GPU VMs and dedicated bare metal with full passthrough performance.
OpenAI-compatible endpoints with 19 open-weight models live — chat, code, vision, image gen, embeddings, speech and Indic languages.
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 CloudEngine console, with demo data.
| Node | GPU | State |
|---|---|---|
| gpu-a-04 | PRO 6000 | running |
| gpu-b-11 | PRO 6000 | running |
| k8s-pool-2 | GPU nodes ×6 | running |
| web-vm-12 | 16 vCPU | running |
| db-vm-03 | 32 vCPU | running |
| gpu-c-02 | PRO 6000 | provisioning |
| Model | Replicas | State |
|---|---|---|
| gemma-3-4b-it | 6 | serving |
| deepseek-coder-v2-lite | 4 | serving |
| flux-schnell | 3 | serving |
| xtts-v2 | 0→2 | scaling |
| bge-m3 | 2 | serving |
| kokoro-tts | 2 | 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 |
Generate your first image.
Describe an image and press Generate — endpoints scale from zero, so the first call may take ~30s.
Blackwell workstation-class GPUs are live today. Datacenter-class Hopper and Blackwell are next — request access for priority allocation and launch pricing.
The controls, isolation and guarantees your security and platform teams will ask about — standard on every deployment.
Financially backed availability commitments with 24×7 engineer support and a 15-minute response SLA on critical issues.
Private VPC networking, dedicated tenancy options and full workload isolation — your traffic never shares a path it shouldn't.
SSO/SAML, role-based access control and complete audit logs across console and API. Every action is attributable.
Always-on scrubbing and network-layer protection in front of every deployment, at no extra charge.
Encryption at rest and in transit as the default, with customer-managed key options for regulated workloads.
Pin data and workloads to a specific region for compliance. Residency is enforced at the infrastructure layer, not by policy alone.
No. Neoclouds rent you GPUs; CloudEngine runs workloads. Every deployment gets the full platform — compute, VPC networking, block and object storage, Kubernetes, inference endpoints and observability — operated as one engine on our own stack. GPUs are a component, not the product.
RTX PRO 6000 Blackwell capacity deploys in under a minute from the console. For H100, B200 and B300, request access — we allocate in order, and early requests 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.
19 models are live today across chat, code, vision, image generation, embeddings, speech-to-text and text-to-speech — including Gemma, DeepSeek Coder, Flux, XTTS, Kokoro and BGE, with Indic language support. You can also bring your own fine-tuned weights. 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 access request form and we'll scope it with you.
Tell us what you're running — an inference product, a training pipeline, or your entire stack — and our engineers come back with an architecture, capacity and onboarding plan. Access covers the whole engine, not just GPUs.
Our team is reviewing your request and will reach out shortly with capacity and onboarding details for your selected GPUs and services.