Training infrastructure
Reproducible runs on spot-heavy clusters. A preempted node costs minutes, not the epoch.
Who we serve
AI startups
Your moat is the model. Your burn rate is the infrastructure. We work on the second so you can work on the first.
What's at stake
The demo got funded. Production is a different machine.
Investors saw the model; customers feel the latency and you feel the GPU bill. Between funding rounds, infrastructure efficiency is runway.
Where we come in
Inference that holds
p99 targets met under real traffic, with batching and autoscaling tuned to your models.
Runway protection
Cost-per-token dashboards and idle-GPU reclamation. The bill tracks usage, not habit.
Scale without rebuild
The platform that serves your tenth customer also serves your thousandth.
What we bring
Production Kubernetes
GPU node pools, drivers, and scheduling handled inside one coherent platform.
Ship-daily delivery
Pipelines that move model and product changes to production without a release day.
AI-aware observability
Latency, quality, and cost signals on the same pane as your infra metrics.
- 0
- GPU spend on idle training capacity, via scale-to-zero
- 3
- Clouds the same platform deploys to unchanged
- Weeks
- From single-cloud prototype to a portable, multi-cloud platform
Work in this space
Ready when you are
Thirty minutes with the architect who would build it. No deck, no account manager, no follow-up sequence.