Run your own models. Know what they cost.
GPU provisioning, LLM deployment, inference optimisation and GPU autoscaling, in cloud or on-prem.
API prices add up fast at scale. Serving models yourself can cost far less, if the GPUs are busy.
Six things we do, end to end.
GPU provisioning
The right GPUs, drivers and operators, in cloud or on-prem.
LLM deployment
vLLM and similar servers behind a stable API.
Inference optimisation
Batching, quantisation and caching for more tokens per GPU.
GPU autoscaling
Scale to demand, down to zero when idle.
Cost per 1M tokens
Measured per model and compared with API pricing.
Hybrid GPU
Owned GPUs for the base load, cloud for peaks.
From a real engagement.
We're writing up our ai infrastructure work.
Ask us on a call and we'll walk you through a recent engagement.
Rented, then owned.
Deliverables you keep.
- GPU and serving architecture
- Model serving on Kubernetes
- Autoscaling setup
- Cost-per-token dashboard
- Benchmark report
- Runbooks
Tools we use here.
Start small, or hand it all over.
Assess
Read-only review, ranked plan in 5 days.
Build & hand over
Built in your account, then handed to your team.
Fully managed
We run it end to end, with an SLA.
Support
Senior help for your engineers.
Questions about ai infra.
Is self-hosting cheaper than APIs?
At steady volume, often yes. We benchmark your models and show the break-even point first.
Cloud or on-prem GPUs?
Whichever fits your load; many teams end up hybrid.
Can we start small?
Yes. Most engagements start with a fixed-price assessment that ends in a ranked plan.
Can our team run it after you build it?
Yes. Everything is in your account and repo, with runbooks and training. Or we manage it end to end.