Launch.
Train. Serve.
Operate.
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.
No credit card required. Spin up your first GPU in minutes.
Works with your stack
Most teams don't have a GPU problem.
They have a GPU-stack problem.
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
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
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
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
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.
QuickPods, TrainPods, and Inference all run on DC Suite - the same platform datacenters license to run their own GPU cloud.
The ML Infra Stack
Run your own GPU cloud — DC Suite
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.
Developer Tools
Podstack buildsServerless notebooks, Python SDK, CLI, web dashboard, and one-click templates - the developer entry point.
Ships inside DC Suite - run it on our cloud, or license it for yours.
What ships in the box
Proprietary control plane and scheduler, scale-to-zero, container/OCI compatible.
Slice any GPU 12.5-100% - NVIDIA and AMD, no vendor SDK lock-in.
Per-minute metering, billing, and invoicing for every tenant.
Cost tracking and utilisation insight across the fleet.
Out-of-the-box hooks into your datacenter infrastructure management.
Per-tenant isolation and audit logging, ready for regulated customers.
Your buyers sign up, launch, and pay without a ticket queue.
Utilisation, health, and capacity monitoring across every card.
Why datacenters license it
GPU capacity sits idle. Whole cards get sold to workloads that only need a fraction of the VRAM.
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.
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.
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.
DC Suite: Per-tenant isolation, audit logging, and residency controls are built into the platform - the same controls behind our own cloud.
Most datacenters worldwide still have no platform to sell and maintain their GPU inventory - raw GPU rental leaves value on the table.
DC Suite: Out-of-the-box DCIM integration turns inventory into a managed, value-added catalogue, so DCs upsell platform services on top of raw GPU - not just rack space.
Exact free capacity is opaque. A buyer who needs GPUs today has no way to see which datacenter has precisely that capacity available.
DC Suite: The Podstack network connects buyers in need to supplier DCs with exact, live capacity - demand lands on the right GPU at the right datacenter.
partner DCs expand their fleets once idle capacity starts earning
85-87% across datacenters running Podstack
SOC 1, SOC 2, GDPR, ISO and the other certifications a DC needs
Podstack runs its own GPU fleet and onboards partner capacity only after a stringent vetting process. Every datacenter in the network carries SOC 1, SOC 2, GDPR, ISO and the other certifications enterprise workloads demand.
Not sure which GPU? Ask the advisor.
Describe your workload and get GPUs ranked from live pricing and availability.
Custom & Enterprise Solutions
Need something custom? Talk to us.
Reserved capacity, high-performance storage, managed labs, and production inference - planned with our team and sized to your workload.
Trusted by teams shipping AI
Developer Tools
SDK, CLI, API, or YAML. Your choice.
import podstack
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
# Initialize Podstack
podstack.init(api_key="your-api-key")
# TOur own control plane and scheduler - not open-source patchwork.
Audited security controls for regulated workloads.
Choose where your workloads run.
One operator, one SLA, one bill.
Ready to accelerate your ML workflow?
Get started in minutes. No credit card required.
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