We deploy open-source AI models, Llama, Qwen, Mistral, DeepSeek, embeddings, speech, on your own servers or private cloud. Run it fully offline for data you can't let leave the building, or simply to get off the per-token cloud bill. You get an OpenAI-compatible API your apps already speak, right-sized GPUs, monitoring, and a handover runbook. You own the weights, the hardware and the data.
The cloud API is the right call when you're starting out. These are the moments it stops being, when the meter, the compliance team, or the vendor's roadmap starts making the decision for you.
Per-token pricing is cheap to start and brutal at scale, success just makes the invoice bigger. A fixed GPU box has a flat cost, so past a certain volume you stop paying per request and start paying for electricity.
Patient records, case files, defence data, anything under strict regulation, sending it to a third-party API is a non-starter. Self-hosted models run entirely on your hardware, and can be fully air-gapped with no internet at all.
Rate limits, surprise price changes, models deprecated out from under you, shifting terms of service. Owning open weights on your own box means the thing that works today still works next year, on your schedule.
The right way to start is one model, on one box, serving one real workload behind an OpenAI-compatible endpoint. Not a data-centre project. We size the hardware to your actual usage, deploy the model, wire it into the app that needs it, and show you the cost math against your current cloud bill.
Most first deployments are running in one to two weeks. We can start on a rented dedicated GPU server so you buy nothing up front, then move to owned or on-prem hardware once the numbers prove out. You keep the weights, the configuration and the runbook.
A cloud API is a meter on someone else's server. Great for getting started, expensive at scale, and off-limits for data that can't leave your walls. Self-hosting flips it: open weights you keep, on hardware you control, at a cost that doesn't move with usage.
We do the hard part, GPU sizing, the inference server, quantization, monitoring, an OpenAI-compatible endpoint, and hand you a stack you own outright. No lock-in, no metering, no vendor deciding your roadmap. That's the whole point.
Open weights · your hardware · OpenAI-compatible · offline-capableWe start from your real workload and budget, not a hardware wishlist. If the honest answer is “the cloud API is cheaper for you right now,” we'll say so before you spend a cent.
We look at your workload, volume and budget, then recommend the models and the GPU to match, with the cost math against your current cloud bill, and an honest verdict if self-hosting won't pay off yet.
We install the models on your servers with a production inference server (vLLM, Ollama or TGI), quantized to fit your hardware, with monitoring and sensible autoscaling within the box.
You get an OpenAI-compatible endpoint, so your apps and agents switch over with a base-URL change. We add auth, logging and an optional cloud fallback for the hardest queries.
A runbook, dashboards and a clear update path. 30 days of support on us, then it's yours to run, or keep us on call by the hour. No lock-in either way.
Running a model in production isn't ollama run on a laptop. It's the right GPU, a real inference server, quantization that fits your hardware, monitoring, and an API your apps can actually depend on. We use well-supported, open tools so your team can run it after we leave, and so nothing about it is a mystery.
If yours isn't here, a 20-minute call almost always answers it.
Talk to a human →Tell us in two paragraphs what you're running on AI today and roughly how much you're spending. We come back inside a day with a hardware plan, the break-even math, and an honest “stay on the cloud for now” if that's the better call. Either way, you get a straight answer.