Deployment
Where your AI runs. All private.
Every deployment mode we offer keeps your data out of consumer chatbots. No training on your prompts. No leaking to public models. The difference is where the hardware lives and who manages it.
Deployment modes
The four modes.
On-premises
Open-weight models deployed on GPU servers inside your building. Data never leaves. You own the hardware, we configure and deploy.
- Models — DeepSeek, Llama, Mistral, Qwen, GLM
- Hardware — NVIDIA H100/H200/B200, AMD MI300X
- Privacy — Complete. Fully offline (air-gapped) option available
Private GPU cloud
Open-weight models running on dedicated GPU instances in a private cloud. The GPUs are yours alone — no sharing with other customers.
- Models — Same open-weight catalog
- Privacy — Dedicated instances, US-based infrastructure
- Cost — Monthly operational. No hardware purchase
Via API
Access the most capable models in the world through API endpoints we configure and secure. Or serve your own open-weight model through hosted endpoints.
- Models — Claude, GPT, Gemini, Grok
- Privacy — Contractual: under the enterprise agreements we configure, providers do not train on your data
- Cost — Pay per token. Lowest barrier to entry
The best of both
Most mature AI stacks end up hybrid: open-weight models on hardware you control for the regulated, high-volume work — frontier models via API for the hardest problems. One architecture, one audit trail, one team running both. Each request routes to the right model: sensitive data stays in-house, and you pay per token only for the exceptions.
Sovereign AI
Your AI, inside your borders.
Sovereign AI means your models, your data, and your operations stay inside a legal boundary you control — your building, your jurisdiction, your rules. No foreign cloud. No consumer chatbot. For utilities, banks, healthcare, and government, that isn't a preference — it's a requirement.
We build for it: on-premises deployment with a fully offline (air-gapped) option, US federal jurisdiction for regulated data, and a bilingual team serving Puerto Rico, the Caribbean, and Latin America. How we build it →
Privacy
Privacy across all modes.
| Concern | On-prem | Hosted Dedicated | Via API | Hybrid |
|---|---|---|---|---|
| Data used to train public models? | No | No | No (enterprise terms) | No |
| Data leaves your jurisdiction? | No | No (US-based infrastructure) | No (enterprise terms) | You choose, per workload |
| You control the model weights? | Yes | Yes | No (frontier) / Yes (open-weight served) | Yes, for the open-weight side |
| Audit trail available? | Yes | Yes | Yes | Yes — one trail across both |
Decision guide
How to choose.
| On-prem | Hosted Dedicated | Via API | Hybrid | |
|---|---|---|---|---|
| Upfront cost | High (hardware purchase) | None | None | Varies — start small |
| Ongoing cost | Low (power, maintenance) | Medium (monthly) | Variable (per token) | Mixed — owned volume + per-token exceptions |
| Control | Maximum | High | Low–Medium | High |
| Intelligence ceiling | Open-weight | Open-weight | Frontier (Claude, GPT, Gemini) | Frontier, where you route to it |
| Best if | Data must stay on-site, long-term high volume | You want control without buying hardware, with predictable monthly spend | You need max intelligence, variable or low volume | Mixed workloads: regulated volume plus frontier-grade problems |
Many of our clients start with one mode and evolve. A bank might prototype via API, move to hosted dedicated for production, and eventually bring it on-prem. That evolution is the hybrid path — and we handle every transition.
Contact
Not sure which deployment fits?
We'll walk through your requirements — data sensitivity, budget, use case, timeline — and recommend the right architecture.
Find your deployment model →