Podstack.ai is a full-stack GPU cloud platform that provides on-demand access to fractional NVIDIA GPUs with per-minute billing, enabling AI developers to fine-tune, train, and serve models without infrastructure management overhead. The platform offers pre-configured templates, serverless inference endpoints, and managed MLOps tools, all at costs significantly lower than major hyperscalers.
Funding
Funding not disclosed
Founders
Product
Problem
AI development teams face significant barriers to GPU access, including high costs, long procurement times, and complex infrastructure management. Traditional cloud providers require committing to full GPU instances with hourly billing, making experimentation and fine-tuning expensive, while setup overhead diverts focus from actual model development.
Solution
Podstack.ai provides a full-stack GPU cloud platform that delivers fractional NVIDIA GPUs billed by the minute, 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 in as little as two minutes. For inference, Podstack.ai provides serverless model endpoints with OpenAI-compatible APIs, sub-200ms cold starts, and scale-to-zero capabilities that eliminate idle costs. The platform also includes managed MLOps services such as MLflow, Kubeflow Pipelines, and DVC, which are provisioned and patched automatically, while zero egress fees ensure predictable pricing for data-intensive workloads.
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 organizations requiring managed MLOps infrastructure without dedicated DevOps resources.
Features
- Fractional GPU instances from 12.5% to 100% of a card, with per-minute billing on QuickPods fleet
- 21 pre-configured templates across categories including LLM fine-tuning, inference, image generation, and scientific computing
- Serverless inference endpoints billed per 1M tokens with free-tier models and sub-200ms cold start times
- Managed MLOps stack including MLflow, Kubeflow Pipelines, DVC, and Metaflow with private endpoints
- OpenAI-compatible API for seamless integration with existing applications
- Time-travel notebook state rewinding and one-click dataset import from Hugging Face
- ISO 27001 certified infrastructure with 99.9% uptime SLA on managed services
- Zero egress fees and transparent live pricing across 20+ GPU models from RTX 2000 Ada to B300