is rendered via react-helmet-async --><br>PodStack - Full-Stack GPU Cloud: Launch, Train, Serve, Operate
Full-Stack GPU Cloud<br>Launch.<br>Train. Serve.<br>Operate.<br>One full-stack GPU cloud for the entire model lifecycle. Fractional GPUs by the second, one-click training and inference, and a single bill - on infrastructure you can run yourself.<br>Zero EgressPer-Minute BillingGlobal AvailabilityISO 27001<br>Start BuildingSee the platform<br>No credit card required. Spin up your first GPU in minutes.
Powered by proprietary PodVirt platform
25%50%75%100%
Using 50% of GPU via PodVirt - pay only for what you use
99.9%<br>Uptime SLA
Launch time
12.5-100%<br>GPU fractions
Products, one bill
Works with your stack<br>PyTorchTensorFlowHugging FaceNVIDIA CUDAKubernetesRayJupyterDocker
The problemMost teams don't have a GPU problem.They have a GPU-stack problem.<br>Getting a model to production means fighting scarce, overpriced GPUs and a stack stitched together from half a dozen vendors - burning budget and weeks before you ship. Here's what's broken today, and how Podstack fixes it.
✕Scarce, overpriced GPUs<br>You rent whole cards on long commitments and pay for capacity you never use.
→Podstack: Fractional GPUs, per-minute billing, on-demand - pay only for the slice you use.
✕A fragmented stack<br>Compute, MLOps, inference and billing come from different vendors - many bills, many SLAs, endless glue code.
→Podstack: One platform to launch, train, serve and operate. One operator, one SLA, one bill.
✕Slow to production<br>It takes weeks of infrastructure plumbing to get from an idea to a served model.
→Podstack: One-click templates and a first-class CLI - from zero to a running model in minutes.
✕Lock-in & egress fees<br>Hyperscalers trap your data and workloads behind proprietary APIs and egress charges.
→Podstack: Portable and self-hostable, with zero egress. Run it on our cloud - or your own.
Three products. One platform.<br>QuickPods, TrainPods, and Inference all run on DC Suite - the same platform datacenters license to run their own GPU cloud.
Launch<br>QuickPods<br>Deploy production-ready AI stacks in one click.<br>1-click templatesMLOps built-inFractional GPUs<br>Learn more →<br>Train<br>TrainPods<br>On-demand NVIDIA GPUs by the hour - billed in INR, connected through the podstack CLI.<br>Billed hourly in INRpodstack CLI + SSHInstant-boot GPUs<br>Learn more →<br>Serve<br>Inference<br>Low-latency endpoints for open-source models. Ship to production instantly.<br>OpenAI-compatibleAutoscalingOpen models<br>Learn more →
All three run on one platform - meet DC Suite↓
The ML Infra Stack<br>Run your own GPU cloud — DC Suite<br>Every AI product runs on the same stack of layers. Podstack builds the five in the middle - developer tools down to GPU virtualization - and licenses them together as DC Suite, so any datacenter can turn GPU hardware into a cloud like this one.
ApplicationsYou buildModelsYou bringFrameworks3rd-party= DC Suite · LicensableDeveloper ToolsPodstack buildsMLOps PlatformPodstack buildsOrchestrationPodstack buildsInfrastructure (IaaS)Podstack buildsGPU VirtualizationPodVirt · patent pending<br>HardwareAny datacenter<br>Developer Tools<br>Podstack builds<br>Serverless notebooks, Python SDK, CLI, web dashboard, and one-click templates - the developer entry point.<br>Ships inside DC Suite - run it on our cloud, or license it for yours.
Podstack builds it - licensable to datacenters as DC SuiteYou bring it, or third parties provide it
What ships in the box<br>✓Orchestration & scheduling<br>Proprietary control plane and scheduler, scale-to-zero, container/OCI compatible.
✓PodVirt fractional GPUs<br>Slice any GPU 12.5-100% - NVIDIA and AMD, no vendor SDK lock-in.
✓BillOps<br>Per-minute metering, billing, and invoicing for every tenant.
✓FinOps<br>Cost tracking and utilisation insight across the fleet.
✓DCIM integration<br>Out-of-the-box hooks into your datacenter infrastructure management.
✓Isolation & audit trails<br>Per-tenant isolation and audit logging, ready for regulated customers.
✓Self-serve customer portal<br>Your buyers sign up, launch, and pay without a ticket queue.
✓Fleet observability<br>Utilisation, health, and capacity monitoring across every card.
Why datacenters license it<br>GPU capacity sits idle. Whole cards get sold to workloads that only need a fraction of the VRAM.<br>→DC Suite: PodVirt slices any GPU from 12.5% to 100%, so every gigabyte of VRAM is sellable and utilisation becomes revenue.
Hardware partitioning (MIG) is rigid: fixed slice sizes, top-end cards only, firmware changes and node restarts to reconfigure.<br>→DC Suite: Software-defined slicing works across NVIDIA and AMD - any slice size, resized dynamically, no vendor SDK lock-in.
Owning GPUs does not make you a cloud. You still need scheduling, metering, billing, and a customer portal.<br>→DC Suite: Orchestration, BillOps metering, billing and invoicing, and a self-serve portal ship in the box - capacity earns from day one.
Enterprise customers demand isolation, audit trails, and data residency you can prove.<br>→DC Suite:...