What it is meant to cover
The work that starts when a shared endpoint stops being an option.
Dedicated deployment
Single-tenant inference on dedicated hardware, sized to a real traffic shape rather than a benchmark — including the boring parts: capacity planning, warm pools, upgrade paths, and what happens when a node dies.
Fine-tuning
Adapting an open model on your own data, with the dataset work treated as a first-class step rather than an afterthought — which is where Runix Pipeline plugs in.
Evaluation before rollout
A tuned model is only better if you can show it. Task-specific evaluation sets, before-and-after comparison, and a decision you can defend to whoever signs off.
One interface either way
Whatever runs where, it stays reachable through Runix Gateway — so your application does not learn the difference between a hosted provider and your own hardware.
Three reasons teams end up here
Not everyone needs dedicated hardware, and we will say so if you do not. The cases where it earns its cost are consistent:
- Data cannot leave — a regulator, a customer contract, or an internal policy rules out shared inference
- The workload is steady and large — at constant volume, dedicated capacity stops being the expensive option
- The model is the moat — you have tuned something on proprietary data and it should not run on someone else's terms
Tell us the constraint you cannot design around
Residency, latency, cost at volume, or a model of your own — describe it and we will tell you honestly whether this is the answer.
Scope an engagement