Open-weight language models, inside your own perimeter.
Open-weight language models deployed inside your perimeter: chosen against your memory and latency budget, placed behind an authenticating gateway, and adapted to your domain where the general model is not sufficient.
The problem it solves
Constraints on data leaving the perimeter. Your data may not be allowed out, in which case a hosted API is not an option regardless of price. The per-token bill becomes an unplanned line item. Or the model changes underneath you: a provider ships a new version, the prompts that worked stop working, and the release that broke your product was not yours. Running the weights yourself trades a subscription for hardware and answers all three at once.
What it covers
- Model selection against your memory and latency budget
- Deployment into your existing cluster
- A gateway with authentication and role-based access
- Rate limits and per-team quotas
- Request and response logging for audit
- An evaluation set built from your own tasks
- Domain adaptation where the general model falls short
- Cost and latency monitoring
- A model update path that does not need downtime
You get
A model running on hardware you control, a gateway that decides who may call it and records that they did, and the evaluation set to check its quality yourself — before and after any change we make.
Typically financial services and public-sector teams under a bar on data leaving the perimeter, and SaaS teams whose per-token bill has become a line item somebody has to forecast.
Book an architecture review →Adapting a model has levels, from changing the prompt to retraining the weights, and they differ by orders of magnitude in cost. We try the cheap levels first and justify every step up with a measurement rather than a preference. None of it can be judged without an evaluation set built from your own tasks, which is why that comes before the work and not after it. Serving carries its own cost, stated plainly: interactive generation wants an accelerator, and the sizing follows from the model chosen and the latency required.