Inference that fits your hardware, your network, and your budget.
We build each platform around what you actually have: the GPUs you own, the ones you can rent, and the network between them.
Pick where you are today.
We meet you there. Choose the one closest to your setup, or let the assessment choose for you.
Not sure which you are? Seven questions give you a starting point.
Take the assessmentWhen not to run inference yourself
Sometimes a hosted API is the better answer. We will tell you so before you buy a GPU, and still help you do it well.
Which fits you?- Volume is low or unpredictable
- The data is not sensitive
- You are still finding out what the product needs
- Only a closed model can do the job today
- Choosing the provider and reading its data terms
- A gateway you control, so switching later needs no code changes
- Keeping sensitive fields out of prompts
- Tracking spend and latency from day one
Privacy comes in degrees.
From a hosted API to hardware that never leaves your building, we match each workload to the level it needs.
When it is time to run your own
Your data stays yours
Prompts, documents, and outputs are processed on infrastructure you control, in a place you chose. Residency and regulatory questions get simpler.
Costs you can predict
Per-token pricing grows with every request. Owned capacity is a fixed cost that gets cheaper per request at sustained volume.
A model that does not change under you
You pin the version. No surprise deprecations, quiet behavior changes, or repricing.
Speed and availability on your terms
The model sits next to your data and users. No one else's rate limits or outages.
A model fitted to the job
The right open-weight model for each task, tuned and sized for your workload.
Why run inference yourself
Your data stays yours
Prompts, documents, and outputs are processed on infrastructure you control, in a place you chose. Residency and regulatory questions get simpler.
Costs you can predict
Per-token pricing grows with every request. Owned capacity is a fixed cost that gets cheaper per request at sustained volume.
A model that does not change under you
You pin the version. No surprise deprecations, quiet behavior changes, or repricing.
Speed and availability on your terms
The model sits next to your data and users. No one else's rate limits or outages.
A model fitted to the job
The right open-weight model for each task, tuned and sized for your workload.
Industries that benefit from private inference
These have the most to gain, because for them the data is the business.
Client data and strategy stay in house
Account records, positions, underwriting, and research are the firm's edge, and regulators expect you to know where they are processed.
Patient records never leave your environment
Notes, claims, and clinical questions all touch protected health information. Keeping it on your systems keeps compliance scope small.
Confidential means confidential
Contracts, case files, and client communications are easier to keep confidential when no outside provider sees them.
Sovereign, and offline if it has to be
Open-weight models on your own hardware run on networks with no internet access, with nothing phoning home.
Volume where every millisecond is money
At high request rates, latency and cost per request decide the economics. Running the model next to the workload controls both.
Source code and plans stay private
Coding assistants and internal agents read your repositories, designs, and roadmaps. Keep them off someone else's servers.
Kubernetes is the lingua franca of inference.
Model servers, gateways, autoscalers, and GPU operators all meet at the Kubernetes API. Build on it and the platform runs the same on owned metal, a cloud account, or rented GPUs.
- One way to deploy everything.
- GPUs join as cluster nodes, on x86 and ARM64.
- We work with the Kubernetes you already run, or build it fresh.
- One gateway routes across clusters and sites.
- Tuned for low latency on bare metal.
# abridged
apiVersion: apps/v1
kind: Deployment
metadata:
name: [YOUR MODEL]
spec:
replicas: 2
template:
spec:
containers:
- name: model-server
image: [YOUR MODEL SERVER]
resources:
limits:
nvidia.com/gpu: 1
Finding the right GPU cloud.
Not every GPU cloud fits every workload. Whether you run entirely in the cloud or add capacity to what you own, we find the providers that fit and test them before you commit.
Capacity you can actually get
Which GPUs are free today, which need a reservation, and how fast a new node starts.
Pricing that fits your load
Reserved, on-demand, or preemptible, matched to how steady your traffic really is.
Privacy you can verify
Data terms, who can reach the host, and whether attestation is supported.
Location and network
Regions near your users and data, private connectivity, and what moving data out costs.
Fits your platform
Bring your own operating system and Kubernetes, or use theirs. Either way it joins the same gateway.
Support when it matters
Uptime commitments, how fast failed hardware is replaced, and a person to call.
How we help
Often the right answer is more than one provider, so one provider's shortage or outage never becomes yours.
Not sure where to start? Take the seven-question assessmentPrivacy comes in degrees.
From a hosted API to hardware that never leaves your building, we match each workload to the level it needs.
Wherever your workloads live.
One platform design, four ways to run it. Start with one and move to another without rebuilding.
On-prem
Your hardware in your racks. Most control, most predictable cost, for steady workloads and sensitive data.
Cloud
Your own cloud accounts. Quick to start, elastic, no hardware to buy.
Hybrid
Both, run as one platform, with the same deployments, gateway, and dashboards.
Burst
Owned capacity for the baseline, rented capacity for the peak.
Own the baseline. Rent the peak.
Owned GPUs carry steady demand at a fixed cost. Spikes overflow through the same gateway to rented capacity, which drains when demand falls. Callers never change an endpoint.
Bare metal and virtualization
Hypervisors, local storage, immutable operating systems
Model serving
Open-weight models tuned for your GPUs
Gateways and routing
Inference aware load balancing and failover
Networking and identity
Private mesh networks, internal endpoints, single sign-on
Hardware we know well
NVIDIA is one of the platforms we have worked with most, especially its paired CPU and GPU designs: Grace with Hopper, Grace with Blackwell, and Vera with Rubin. A fast, coherent link between processor and accelerator changes how a platform should be built, and we design for it.