Podstack
Podstack provides a full-stack GPU cloud platform that splits NVIDIA GPUs into fractional shares billed by the minute or token, eliminating the cost of renting entire cards for small jobs. The platform also offers an operating system for data center operators to white-label and sell their spare GPU capacity, with an optional Grid layer that routes demand to partner operators.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
GPU compute is sold in the wrong shape. Traditional cloud providers require renting entire GPUs with hourly billing, which is too large for small jobs and too expensive for experimentation, fine-tuning, and inference with variable traffic. Data center operators with GPU hardware also lack the software layer needed to sell spare capacity as a cloud service, leaving their infrastructure underutilized.
Solution
Podstack.ai provides a full-stack GPU cloud platform that delivers fractional NVIDIA GPUs billed by the minute or token, allowing teams to pay only for the compute they actually use. The platform offers one-click templates with pre-loaded frameworks like Axolotl, LLaMA Factory, and PyTorch, enabling users to go from sign-in to a running GPU quickly. For inference, Podstack provides serverless model endpoints with an OpenAI-compatible API, scale-to-zero capabilities, and per-token billing that eliminates idle costs. The platform also includes managed MLOps services such as MLflow and Kubeflow Pipelines, zero egress fees, and a single wallet and CLI across all products. On the operator side, Podstack OS turns GPU racks into a sellable cloud under the operator's own brand, with optional participation in Podstack Grid to receive demand at a floor price set by the operator.
Target Audience
Primary customers are AI/ML engineers, data scientists, and research teams at startups and enterprises who need flexible, cost-effective GPU compute for fine-tuning, training, and serving models, as well as data center operators seeking to monetize spare GPU capacity through a white-labeled cloud platform.
Features
- Fractional GPU instances from 12.5% to 100% of a card, with per GPU-minute billing and no minimum commitment on QuickPods
- 21 pre-configured one-click templates covering fine-tuning, vLLM serving, ComfyUI, PyTorch, and scientific computing
- Serverless inference endpoints billed per token with scale-to-zero, no idle charges, and sub-200ms cold starts via a warm pool
- OpenAI-compatible API for seamless integration with existing applications, including per-key quotas and spend limits
- Managed MLOps stack including MLflow, Kubeflow Pipelines, DVC, and Metaflow with private endpoints
- Podstack OS for operators: DC Suite, NextGen DC Suite, PodVirt and Token Factory for white-labeled GPU cloud provisioning, metering, and billing
- Podstack Grid optional layer that routes Podstack demand to partner operators, who keep 65-80% of earnings
- ISO 27001 certified infrastructure with 99.9% uptime SLA on managed services
- CLI, Python SDK, and REST API that expose everything the console does, including launching and stopping pods
- Time-travel notebook state rewinding and one-click dataset import from Hugging Face