FlexGPU offers a cloud-native platform that lets AI/ML engineers, visual effects studios, and simulation teams provision NVIDIA GPUs on demand through a RESTful API or CLI. The service integrates with Docker and Kubernetes, provides per‑second billing, real‑time utilization metrics, and automated scaling to align compute costs with actual workload needs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and developers often face high capital expenditures and limited elasticity when provisioning GPU resources for AI model training, 3D rendering, or scientific simulation. On-premise GPU clusters can become underutilized during idle periods and insufficient during peak demand, leading to cost inefficiencies and project delays.
Solution
FlexGPU delivers a cloud-native GPU infrastructure that allows users to provision and release GPU instances on demand via a RESTful API or CLI. The platform supports a range of NVIDIA GPUs, enabling workloads to scale horizontally without manual hardware provisioning. Usage is billed per second, aligning operational spend with actual compute consumption. Integrated with popular container orchestration tools, FlexGPU lets teams embed GPU resources directly into CI/CD pipelines and Kubernetes clusters. Real-time performance metrics and automated health checks ensure consistent throughput and reliability across burst and sustained workloads.
Target Audience
Primary customers are AI/ML engineers, visual effects studios, and simulation teams that require elastic GPU capacity for training, rendering, or compute‑intensive analysis. The service also targets enterprise IT departments seeking to augment on‑premise clusters with cloud burst capability.
Features
- API‑driven on‑demand provisioning of NVIDIA A100, V100, and T4 GPUs with instant spin‑up times
- Native integration with Docker and Kubernetes for seamless inclusion in existing DevOps workflows
- Pay‑per‑second billing model with granular usage reporting to optimize cost allocation
- Multi‑tenant isolation using hardware‑level virtualization and encrypted data paths
- Pre‑installed deep‑learning frameworks (TensorFlow, PyTorch, MXNet) and rendering engines for rapid environment setup
- Real‑time dashboard displaying GPU utilization, temperature, and error logs with alerting hooks
- Automated scaling policies that trigger instance addition or removal based on workload metrics