Trainy offers a cloud‑agnostic GPU orchestration platform that lets AI teams launch multi‑node, multi‑framework machine‑learning jobs with a single YAML file, handling networking, high‑bandwidth interconnects, and automatic GPU health monitoring. The service provides fault‑tolerant scheduling, preemptive priority queues, and usage‑based pricing that charges only for active GPU time, while supporting any Python‑based ML framework without code changes.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI teams often struggle with complex GPU provisioning, multi-cloud networking, and fault‑tolerant scheduling, leading to idle resources, costly downtime, and lengthy setup times for large‑scale training workloads.
Solution
Trainy provides a cloud‑agnostic GPU orchestration platform that lets users launch multi‑node, multi‑framework machine‑learning jobs via a single YAML configuration. The service automatically handles networking, high‑bandwidth interconnects, and GPU health monitoring, offering built‑in failover and self‑healing to minimize interruptions. Users can prioritize workloads with a preemptive queue, pause lower‑priority jobs, and resume them seamlessly. Pricing is usage‑based, charging only for active GPU time, while optional reserved clusters give dedicated capacity and advanced monitoring. The platform integrates with existing Kubernetes clusters and supports any Python‑based ML framework without code changes, delivering rapid provisioning (under 20 minutes) and enterprise‑grade SLA guarantees.
Target Audience
Primary customers are AI research and engineering teams in enterprises, startups, and labs that need scalable, multi‑cloud GPU clusters for training large models, as well as DevOps groups managing on‑prem or hybrid GPU infrastructure.
Features
- Single YAML file defines nodes, GPU types, priorities, and launch commands for any cloud provider
- Automatic multi‑cloud networking setup with high‑bandwidth (e.g., 3.2 TB/s InfiniBand) support
- Preemptive priority queue that pauses and resumes jobs based on defined priorities
- Fault‑tolerant scheduler with automatic GPU health detection, diagnostics, and recovery
- Compatibility with all major Python ML frameworks (PyTorch, HuggingFace, JAX, Ray, etc.) without code modifications
- Real‑time dashboard for job monitoring, queue management, and cluster utilization insights
- On‑demand pricing model (e.g., $3.60 per GPU‑hour) with optional reserved capacity and enterprise SLA
- 24/7 support and 99.5 % uptime SLA for production workloads