Serve inference across a multi-backend fleet — hosted providers and your own GPUs — and run training and fine-tuning workloads on infrastructure you control.
Renting frontier APIs is expensive and sends your data off-site; self-hosting is hard to schedule and scale. Most teams end up locked into one and paying for the trade-off.
What this layer gives you.
Serve open models on your own GPUs with SGLang and vLLM — tensor-parallel, prefix caching, and tuned KV-cache.
Blend self-hosted models with hosted providers behind the gateway; route by cost, capability, or data residency.
Workloads scheduled across Kubernetes, with warm pools kept ready for instant assignment.
Bring fine-tuning and training jobs to your own GPUs — no data or weights leave your perimeter.
Raw compute, in your perimeter
Your GPUs, your data, your weights.
One integrated stack, GPU to Agent. This layer, in context.