Enterprise
Run BiOS, deployed on your infrastructure — on-prem or in your cloud.
The same OpenAI-compatible API, model catalog, and adaptive routing — running on infrastructure you own. Inference and fine-tuning stay inside your boundary; we operate the stack.
Illustrative — the stack runs where you decide it runs.
Build with leading models
What you get
Enterprise control without rebuilding the platform
Four things, and none of them is a compromise: your data stays yours, your developers keep one API, the operations are our job — and the weights you train are yours.
What does deployment look like?
01
Bring your capacity
GPUs you already own, or capacity you procure in your preferred cloud or data center. The cluster stays yours — your account, your nodes, your network policy.
02
We install the stack
Run BiOS deploys the serving stack into your cluster with a dedicated, revocable setup credential, then scopes every runtime component down to its own identity.
03
Validate together
GPU readiness, endpoint reachability, routing, and autoscaling are checked with your team before production traffic touches the cluster.
04
Serve production traffic
Your applications call the same OpenAI-compatible API they already use. Inference and fine-tuning run inside your boundary; we operate the stack day to day.
Questions teams ask before the first call
Where does inference and training actually run?+
Inside your environment — your cloud account or data center. The stack is deployed into Kubernetes infrastructure you own, and inference request and response handling, plus fine-tuning runs, stay within your network boundary.
Is it the same API as the cloud platform?+
Yes. One OpenAI-compatible API, the same model catalog, and BiOS Adaptive routing across your on-prem deployment and our managed capacity. Code written against one runs against the other.
Who operates the deployment?+
Run BiOS operates the serving stack: model deployment lifecycle, upgrades, GPU health monitoring, autoscaling, and observability. Your team owns the environment, the hardware, and the network policy.
What about fine-tuning?+
Fine-tuning runs on dedicated GPUs billed per second, and the checkpoint is a file you own — export it and serve it inside your deployment or anywhere else. The same training product, the same ownership.
What if traffic outgrows our cluster?+
Hybrid capacity is an option: eligible workloads can overflow onto Run BiOS-managed capacity during spikes or while your own GPUs are being expanded or remediated. Which workloads may overflow, when, and how routing behaves are agreed with your team up front.
What does it take to get started?+
A Kubernetes cluster with NVIDIA GPU nodes, network access that lets us manage the stack, and a conversation with our team about your environment. We confirm fit, then install and validate with you.
Bring us your environment.
Tell us where inference and training need to run — cloud account, data center, or existing GPU capacity — and we will confirm fit, install, and validate with your team.
One OpenAI-compatible API · BiOS Adaptive routing · We operate the stack